Le fameux rapport d'Amy Webb, sur les Les nouvelles tendances technologiques qui influenceront le business, l'éducation, la politique, les gouvernements, l'éducation et la société dans l'année à venir.

1. 1
2017
Tech Trend Report
Emerging technology trends that will influence business, education,
politics, government, education and society in the coming year.

2. A great wave of disruption—anchored in artificial intelligence, robotics, self-driv-
ing cars, genomic editing, cognitive computing, the Internet of Things, and big
data—is underway. As these technologies move from the fringe to the main-
stream, they promise to forever change how we live, work and play.
The 2017 Tech Trends Report, now in its 10th year, focuses on mid- to late-stage
emerging technologies that are on a growth trajectory. We have identified more
than 150 trends for the coming year across numerous industries, and our broad
scope was intentional. In this era of rapidly accelerating technological advance-
ment, changes within one industry necessarily impact another. We listen to sig-
nals across different sectors, and we encourage you to do the same.
Now more than ever, organizations must examine the potential impact of tech
trends. Whether you are a Fortune 500 company, a government agency, a start-
up, a university, a foundation or a small business, you must factor the trends
in this report into your strategic thinking for the coming year, and adjust your
planning, operations and business models accordingly. Failing to track trends in
a meaningful way will put your competitive advantage and growth at risk.
This is a robust, detailed document—don’t try to read it in one sitting. Start with
the executive summary, and read through the top trends listed for your industry.
In the coming weeks, spend some time with all of the trends. I hope you will take
your time with our 2017 report, and allow your mind to wander productively.
If you use our trends during an annual meeting to set your strategy for the com-
ing year, that’s a good start—but it isn’t enough to safeguard your organization
from what’s on the horizon. Organizations that use a formal process to research
and track trends are more likely to reduce risk, harness new opportunities and
drive change within their fields. What comes next won’t arrive fully formed. The
future is yours to build. The Future Today Institute is eager to help you in 2017
and beyond.
This is a robust,
detailed document–
don’t try to read it
in one sitting. Start
with the executive
summary, and read
through the top
trends listed for
your industry. In the
coming weeks, spend
some time with all
of the trends. I hope
you will take your
time with our 2017
report, and allow
your mind to wander
productively.
Amy Webb | Founder | Future Today Institute
2
© 2017 Future
Today Institute

3. Key Takeaways
• Convergence: In 2017, a critical mass of emerging technologies will start to converge,
finding advanced uses beyond initial testing and applied research. That’s a signal
worth paying attention to. We have devoted extra attention to artificial intelligence,
mixed reality, object recognition systems, robots, autonomous vehicles, genomics,
bioelectronics and automation.
• Artificial Intelligence: For the first time, artificial intelligence research has advanced
enough that it is now a core component of most of our trends. It is vitally important
that all decision-makers within an organization familiarize themselves with what AI
is, what it is not, and why it matters. We have included an AI Primer in our Trend Re-
port this year to aid in that effort.
• Crossover Trends: Leaders must pay attention to signals outside their immediate
industries. In 2017 and beyond, technology developments in one industry sector
will impact many others. For example, why should a logistics company like UPS or a
grocery store chain like Kroger pay close attention to gene editing? Well...advance-
ments in the CRISPR gene editing technique will lead to seeds for hyper-productive
plants that require just a few feet of space and don’t need much water. Those plants
could be farmed within dense urban areas, decreasing a reliance on local grocery
stores. This would impact merchants, importers, truck drivers, UPC code sticker pro-
viders, and marketing agencies, which would all experience a loss in profit. It would
devastate the farmers in countries such as Brazil, Thailand and Mexico, who could
rise up or revolt, causing political instability in those countries. This scenario isn’t
guaranteed, but it is plausible, and it’s why UPS and Kroger should at least have
gene editing on their 2017 radar. We can very easily build scenarios connecting the
dots between myriad technologies, companies and industry sectors. For that rea-
son, we encourage you to pay close attention to technology trends adjacent to
your industry.
EXECUTIVE SUMMARY
Our mission at the Future Today Institute is to help
organizations see around corners. Leaders tend to make
bad decisions under duress. When surprised by a new,
disruptive technology, their “flight-or-fight” response
system kicks in, and they either ignore important tech
trends and imperil their companies, or they act rashly and
make ill-informed investments. Right now, all organizations,
regardless of their size and scope, should be paying close
attention to the fringe so that when a new technology
arises, teams can evaluate their options rationally.
© 2017 Future Today Institute3

4. • New Ecosystems: Many of the areas we’ve included in this report during the past
decade have moved from the fringe towards the mainstream. In the process entire
new ecosystems have blossomed: autonomous vehicles, genetics, robotics and the
like. We have grouped these together in this year’s report to help you gain a deeper
understanding of their ecosystems.
• R&D Moves From Universities To Corporations: One observation we made while
preparing our 2017 report: some of the unusual suspects we tracked on the fringes
turned out to be R&D departments within very large, established corporations. This
is because companies are courting academia now more than ever, and in some cases
poaching entire research teams. Companies are successfully mimicking a university
culture within their walls, with one big exception: money. They’re able to provide
significantly more resources and exponentially higher salaries than what’s offered in
academe.
• Government Policy: Both in the U.S. and elsewhere in the world, policy, privacy
and security will be more complicated than ever in the coming year. Technology is
changing faster than the government’s ability to legislate and regulate it—this will
lead to complicated discussions and debates in the year ahead.
• More Trends Than Ever: Our biggest takeaway (perhaps yours too, if you took a
peak at the very end of the report) is that there are many more tech trends to pay
attention to in 2017 than in previous years. Technology begets technology. We
are witnessing an explosion in slow motion.
Real trends worth your time and attention don’t have clever names. They don’t sound
“trendy.”
EXECUTIVE SUMMARY
© 2017 Future Today Institute4

5. Our Trend Report this year is intentionally dense—there’s a lot here to digest.
That’s because it’s meant as a practical resource for you and your organiza-
tion. We don’t want you to skim through this during your lunch break. Instead,
use it to listen for the signals talking and to advise your strategic thinking
throughout 2017.
Don’t discount a trend simply because at first glance it doesn’t seem to relate
directly to you or your field. Instead, think about these 159 trends and ask
yourself the following questions:
01 How does this trend impact our industry and all of its parts?
02 Who are the drivers of change in this trend? Which companies, leaders, founders, startups, researchers?
03 How are companies/ agencies/ organizations in adjacent spaces addressing this trend?
04 Where does this trend create potential new partners or collaborators for us?
05 How are our competitors/ related agencies harnessing this trend (or failing to do so?)
06 Which of our customer segments—existing, former, potential, theoretical—does this trend address?
07 How will the wants, needs and expectations of our customers change as a result of this trend?
08 Will this trend create new competitors for us?
09 How does this trend inspire us?
10 How does this trend help us think about innovation?
This is a big report.
We don’t want you
to skim through this
during your lunch
break.
10 QUESTIONS
How to relate these trends back to your organization in 2017
5
© 2017 Future
Today Institute

6. Advertising, Public Relations
Key Trend Themes For 2017
Artificial Intelligence
Recognition
Digital Frailty
Video
Mixed Reality
Security
Privacy
Big Data
Drones
Automation
Internet Mobs and Trolls
Wearables
Pay Special Attention To These Trends In Our Report
01 Artificial Intelligence
(whole section)
12 Hidden Bias in AI
13 Accountability and Trust
14 Bots
15 Deep Learning
16 Cognitive Computing
17 Smart Virtual Personal Assistants
18 Ambient Interfaces
19 Deep Linking
20 Consolidation in AI
24 Robot Companions
26 Ethical Manufacturing
29 Productivity Bots
33 Faceless Recognition
34 Bias in Recognition Algorithms
35 Adversarial Images
36 Ambient Proximity
37 Character Recognition
and Analytics
38 Attention
39 Digital Frailty
41 Limited-Edition News Products
43 One-To-Few Publishing
47 Conversational Interfaces
48 Dynamic Listicles
50 Connected TVs
51 Cord Cutting
53 Streaming Social Video
54 Media Consolidation
56 Crowdlearning
57 Blocking the Ad Blockers
59 Leaking
60 The First Amendment
in a Digital Age
63 Virtual Reality
64 Augmented Reality
65 360-degree Video
66 Holograms
67 Data Retention Policies
71 Glitches
75 Prize Hacks
79 Weaponizing Wikileaks
80 Anonymity
81 Differential Privacy
83 Trolls
84 Authenticity
91 Ownership
93 Organizational Doxing
98 VR Marketing
99 FOBO (Fear Of Being Offline)
100 Retail APIs
102 Sense and Avoid Technology
121 Internet Mob Justice
133 Head Mounted Displays
134 Smartwatches
137 Women/ Wearables
THE MOST
IMPORTANT TRENDS
FOR YOUR INDUSTRY
AND ORGANIZATION
We’ve created lists to help you find the most important tech
trends that will matter most to you, your organization and
your industry in 2017. For your convenience, industries are
listed alphabetically along with the corresponding trends.
© 2017 Future Today Institute6

7. Pay Special Attention To These Trends In Our Report
01 Artificial Intelligence
02 Real-Time Machine Learning
13 Accountability and Trust
15 Deep Learning
16 Cognitive Computing
20 Consolidation in AI
22 Smart Dust
25 Collaborative Robots
26 Ethical Manufacturing
32 Sharing Economy
and Lendership
35 Adversarial Images
61 Synthetic Data Sets
64 Augmented Reality
67 Data Retention Policies
69 Zero Day Exploits
71 Glitches
73 Open Source App
Vulnerabilities
75 Prize Hacks
81 Differential Privacy
84 Authenticity
87 Eye in the Sky
88 Right to Eavesdrop/
Be Eavesdropped On
89 Drone Surveillance
103 Drone Lanes
106 Microdrones
107 Drone Delivery
108 Internet of Things
109 Intelligent Cameras
110 Open Source Systems
111 Adaptive Driving Systems
112 Vehicle-to-Vehicle
Communications
115 Autonomous Vehicle Legislation
126 Molecular Programming
129 Synthetic Biology
131 Norms and Regulations
(Biological)
148 Deep Learning For
Food Recognition
149 Smart Farms
150 Terraforming
151 Cultivated Food and Beverage
152 Anthropocene and Climate
Agriculture
Key Trend Themes For 2017
Artificial Intelligence
Robotics
Recognition
Augmented Reality
Security
Privacy
Big Data
Drones
Internet of Things
Automation
Self-Driving Vehicles
Genomic Editing
Deep Learning
Anthropocene and Climate
© 2017 Future Today Institute7

8. Pay Special Attention To These Trends In Our Report
01 Artificial Intelligence
02 Real-Time Machine Learning
13 Accountability and Trust
15 Deep Learning
16 Cognitive Computing
18 Ambient Interfaces
20 Consolidation in AI
21 Human-Machine Interfaces
24 Robot Companions
25 Collaborative Robots
31 Nanodegrees
32 Sharing Economy and Lendership
33 Faceless Recognition
34 Bias in Recognition Algorithms
35 Adversarial Images
36 Ambient Proximity
56 Crowdlearning
61 Synthetic Data Sets
64 Augmented Reality
66 Holograms
67 Data Retention Policies
68 Remote Kill Switches
69 Zero Day Exploits
70 Backdoors
71 Glitches
73 Open Source App Vulnerabilities
74 Selfie Security
75 Prize Hacks
76 Automated Hacking
81 Differential Privacy
86 Encryption Management
88 Right to Eavesdrop/
Be Eavesdropped On
89 Drone Surveillance
93 Organizational Doxing
99 FOBO (Fear Of Being Offline)
100 Retail APIs
103 Drone Lanes
104 Clandestine, Disappearing Drones
105 Autonomous Underwater Vehicles
106 Microdrones
107 Drone Delivery
108 Internet of Things
109 Intelligent Cameras
110 Open Source Systems
111 Adaptive Driving Systems
112 Vehicle-to-Vehicle Communications
113 Autonomous Testing Facilities
114 Solar Highways
115 Autonomous Vehicle Legislation
123 City-Level Cyber Security
124 Smart City Systems
132 Biointerfaces
133 Head Mounted Displays
134 Smartwatches
136 Connected Fabrics
143 Thinkables
152 Anthropocene and Climate
153 Space Exploration
154 Invisibility Cloaks
157 Internet of X
158 5G
Infrastructure
Key Trend Themes For 2017
Artificial Intelligence
Robotics
Human-Machine Interfaces
Recognition
Mixed Reality
Security
Privacy
Big Data
Drones
Internet of Things
Automation
Self-Driving Vehicles
Smart Cities
Wearables
© 2017 Future Today Institute8

9. Pay Special Attention To These Trends In Our Report
01 Artificial Intelligence
02 Real-Time Machine Learning
12 Hidden Bias in AI
13 Accountability and Trust
14 Bots
16 Cognitive Computing
17 Smart Virtual Personal Assistants
20 Consolidation in AI
26 Ethical Manufacturing
27 Universal Basic Income
28 Artificial Intelligence in Hiring
29 Productivity Bots
31 Nanodegrees
33 Faceless Recognition
34 Bias in Recognition Algorithms
36 Ambient Proximity
37 Character Recognition and Analytics
39 Digital Frailty
54 Media Consolidation
57 Blocking the Ad Blockers
63 Virtual Reality
64 Augmented Reality
67 Data Retention Policies
68 Remote Kill Switches
70 Backdoors
71 Glitches
72 Darknets
75 Prize Hacks
86 Encryption Management
88 Right to Eavesdrop/
Be Eavesdropped On
90 Private Networks
91 Ownership
93 Organizational Doxing
95 E-Residents
96 Social Payments
97 Bitcoin and Blockchain
99 FOBO (Fear Of Being Offline)
100 Retail APIs
101 Digital Associates
118 Old Laws Clash With
New Technology
121 Internet Mob Justice
156 3D Printing
157 Internet of X
158 5G
Business Associations
Key Trend Themes For 2017
Artificial Intelligence
Recognition
Digital Frailty
Security
Privacy
Big Data
E-Residents
Social Payments
Bitcoin and Blockchain
Automation
Internet Mobs and Trolls
© 2017 Future Today Institute9

10. Pay Special Attention To These Trends In Our Report
01 Artificial Intelligence (whole section)
12 Hidden Bias in AI
13 Accountability and Trust
14 Bots
16 Cognitive Computing
18 Ambient Interfaces
20 Consolidation in AI
21 Human-Machine Interfaces
27 Universal Basic Income
30 Adaptive Learning
31 Nanodegrees
33 Faceless Recognition
34 Bias in Recognition Algorithms
36 Ambient Proximity
37 Character Recognition and Analytics
38 Attention
39 Digital Frailty
47 Conversational Interfaces
48 Dynamic Listicles
59 Leaking
60 The First Amendment
in a Digital Age
61 Synthetic Data Sets
62 Torrents
63 Virtual Reality
64 Augmented Reality
65 360-degree Video
66 Holograms
67 Data Retention Policies
68 Zero Day Exploits
69 Backdoors
71 Glitches
72 Darknets
73 Open Source App Vulnerabilities
74 Selfie Security
75 Prize Hacks
76 Automated Hacking
79 Weaponizing Wikileaks
81 Differential Privacy
82 Digital Self-Incrimination
83 Trolls
84 Authenticity
85 Revenge Porn
86 Encryption Management
88 Right to Eavesdrop/
Be Eavesdropped On
91 Ownership
92 Uploading Photos To Law
Enforcement Databases
93 Organizational Doxing
99 FOBO (Fear Of Being Offline)
108 Internet of Things
157 Internet of X
158 5G
Colleges, Universities & Schools
Key Trend Themes For 2017
Artificial Intelligence
Robotics
Human-Machine Interfaces
Recognition
Digital Frailty
Mixed Reality
Security
Privacy
Big Data
Internet of Things
Automation
© 2017 Future Today Institute10

11. Pay Special Attention To These Trends In Our Report
01 Artificial Intelligence
02 Real-Time Machine Learning
13 Accountability and Trust
14 Bots
32 Sharing Economy and Lendership
34 Bias in Recognition Algorithms
36 Ambient Proximity
37 Character Recognition and Analytics
61 Synthetic Data Sets
63 Virtual Reality
64 Augmented Reality
65 360-degree Video
66 Holograms
67 Data Retention Policies
75 Prize Hacks
83 Trolls
93 Organizational Doxing
96 Social Payments
99 FOBO (Fear Of Being Offline)
100 Retail APIs
101 Digital Associates
107 Drone Delivery
108 Internet of Things
133 Head Mounted Displays
134 Smartwatches
136 Connected Fabrics
137 Women/ Wearables
138 Kids/ Wearables
139 Pets/ Wearables
142 Earables
143 Thinkables
148 Deep Learning For
Food Recognition
151 Cultivated Food and Beverage
156 3D Printing
157 Internet of X
158 5G
Consumer Package Goods & Retail
Key Trend Themes For 2017
Artificial Intelligence
Mixed Reality
Security
Privacy
Big Data
Social Payments
Drones
Internet of Things
Wearables
© 2017 Future Today Institute11

12. Pay Special Attention To These Trends In Our Report
01 Artificial Intelligence
12 Hidden Bias in AI
13 Accountability and Trust
20 Consolidation in AI
26 Ethical Manufacturing
28 Artificial Intelligence in Hiring
31 Nanodegrees
54 Media Consolidation
59 Leaking
60 The First Amendment
in a Digital Age
67 Data Retention Policies
69 Zero Day Exploits
70 Backdoors
71 Glitches
75 Prize Hacks
76 Automated Hacking
79 Weaponizing Wikileaks
80 Anonymity
81 Differential Privacy
82 Digital Self-Incrimination
83 Trolls
84 Authenticity
86 Encryption Management
88 Right to Eavesdrop/
Be Eavesdropped On
91 Ownership
93 Organizational Doxing
97 Bitcoin and Blockchain
118 Old Laws Clash With
New Technology
121 Internet Mob Justice
Corporate Boards
Key Trend Themes For 2017
Artificial Intelligence
Robotics
Human-Machine Interfaces
Security
Privacy
Big Data
E-Residents
Bitcoin and Blockchain
Automation
Internet Mobs and Trolls
© 2017 Future Today Institute12

13. Pay Special Attention To These Trends In Our Report
01 Artificial Intelligence (whole section)
12 Hidden Bias in AI
13 Accountability and Trust
14 Bots
15 Deep Learning
16 Cognitive Computing
20 Consolidation in AI
21 Human-Machine Interfaces
22 Smart Dust
23 Soft Robotics
25 Collaborative Robots
33 Faceless Recognition
34 Bias in Recognition Algorithms
35 Adversarial Images
36 Ambient Proximity
37 Character Recognition and Analytics
52 WebRTC
56 Crowdlearning
61 Synthetic Data Sets
62 Torrents
63 Virtual Reality
64 Augmented Reality
66 Holograms
67 Data Retention Policies
68 Remote Kill Switches
69 Zero Day Exploits
70 Backdoors
71 Glitches
72 Darknets
73 Open Source App Vulnerabilities
74 Selfie Security
75 Prize Hacks
76 Automated Hacking
77 Offensive Government Hacking
78 Cyber Mission Forces in the Field
79 Weaponizing Wikileaks
81 Differential Privacy
82 Digital Self-Incrimination
83 Trolls
86 Encryption Management
87 Eye in the Sky
88 Right to Eavesdrop/
Be Eavesdropped On
89 Drone Surveillance
90 Private Networks
92 Uploading Photos To Law
Enforcement Databases
93 Organizational Doxing
99 Bitcoin and Blockchain
103 Drone Lanes
104 Clandestine, Disappearing Drones
105 Autonomous Underwater Vehicles
106 Microdrones
107 Drone Delivery
108 Internet of Things
109 Intelligent Cameras
110 Open Source Systems
111 Adaptive Driving Systems
112 Vehicle-to-Vehicle Communications
113 Autonomous Testing Facilities
114 Solar Highways
115 Autonomous Vehicle Legislation
119 Digital Caliphate
120 Asking Tech Companies
To Help Fight Terrorism
121 Internet Mob Justice
125 Precision Medicine
126 Molecular Programming
128 Neuroenhancers
129 Synthetic Biology
131 Norms and Regulations (Biological)
132 Biointerfaces
133 Head Mounted Displays
134 Smartwatches
135 Wireless Body Area Networks
136 Connected Fabrics
140 Tattooables
141 Ingestibles/ Implantables
142 Earables
143 Thinkables
144 Smart Thread
145 Bioelectronics
147 Touch-Sensitive Prosthetics
154 Invisibility Cloaks
155 Quantum Computers
156 3D Printing
157 Internet of X
158 5G
Defense
Key Trend Themes For 2017
Artificial Intelligence
Robotics
Human-Machine Interfaces
Recognition
Mixed Reality
Security
Privacy
Big Data
Bitcoin and Blockchain
Drones
Internet of Things
Automation
Self-Driving Vehicles
Internet Mobs and Trolls
Genomic Editing
Wearables
© 2017 Future Today Institute13

14. Pay Special Attention To These Trends In Our Report
01 Artificial Intelligence (whole section)
13 Accountability and Trust
14 Bots
15 Deep Learning
16 Cognitive Computing
18 Ambient Interfaces
20 Consolidation in AI
21 Human-Machine Interfaces
24 Robot Companions
56 Crowdlearning
61 Synthetic Data Sets
63 Virtual Reality
67 Data Retention Policies
93 Organizational Doxing
99 FOBO (Fear Of Being Offline)
108 Internet of Things
125 Precision Medicine
126 Molecular Programming
127 Nanobot Treatments
128 Neuroenhancers
129 Synthetic Biology
130 Running Out of Space
For Genome Storage
131 Norms and Regulations (Biological)
132 Biointerfaces
133 Head Mounted Displays
134 Smartwatches
135 Wireless Body Area Networks
140 Tattooables
141 Ingestibles/ Implantables
142 Earables
143 Thinkables
144 Smart Thread
145 Bioelectronics
146 Patient-Generated Health Data
147 Touch-Sensitive Prosthetics
156 3D Printing
157 Internet of X
158 5G
Doctors & Other
Health Professionals
Key Trend Themes For 2017
Artificial Intelligence
Human-Machine Interfaces
Recognition
Mixed Reality
Security
Privacy
Big Data
Internet of Things
Genomic Editing
Wearables
© 2017 Future Today Institute14

15. Pay Special Attention To These Trends In Our Report
01 Artificial Intelligence
02 Real-Time Machine Learning
13 Accountability and Trust
15 Deep Learning
16 Cognitive Computing
20 Consolidation in AI
21 Human-Machine Interfaces
22 Smart Dust
23 Soft Robotics
56 Crowdlearning
61 Synthetic Data Sets
67 Data Retention Policies
93 Organizational Doxing
108 Internet of Things
125 Precision Medicine
126 Molecular Programming
127 Nanobot Treatments
128 Neuroenhancers
129 Synthetic Biology
130 Running Out of Space
For Genome Storage
131 Norms and Regulations (Biological)
132 Biointerfaces
134 Smartwatches
135 Wireless Body Area Networks
140 Tattooables
141 Ingestibles/ Implantables
142 Earables
143 Thinkables
144 Smart Thread
145 Bioelectronics
146 Patient-Generated Health Data
147 Touch-Sensitive Prosthetics
156 3D Printing
157 Internet of X
158 5G
Drug Manufacturers/
Pharmaceuticals
Key Trend Themes For 2017
Artificial Intelligence
Robotics
Human-Machine Interfaces
Recognition
Security
Privacy
Big Data
Internet of Things
Automation
Genomic Editing
Wearables
Bioelectronics
© 2017 Future Today Institute15

16. Pay Special Attention To These Trends In Our Report
01 Artificial Intelligence
02 Real-Time Machine Learning
03 Image Completion
05 Natural Language Generation
08 Zero Knowledge Proofs
10 Algorithm Marketplaces
11 Pre-Trained AI Chips
12 Hidden Bias in AI
13 Accountability and Trust
14 Bots
15 Deep Learning
16 Cognitive Computing
17 Smart Virtual Personal Assistants
18 Ambient Interfaces
19 Deep Linking
20 Consolidation in AI
31 Nanodegrees
33 Faceless Recognition
34 Bias in Recognition Algorithms
35 Adversarial Images
37 Character Recognition and Analytics
56 Crowdlearning
61 Synthetic Data Sets
67 Data Retention Policies
69 Zero Day Exploits
70 Backdoors
71 Glitches
73 Open Source App Vulnerabilities
74 Selfie Security
75 Prize Hacks
76 Automated Hacking
81 Differential Privacy
86 Encryption Management
93 Organizational Doxing
95 E-Residents
96 Social Payments
97 Bitcoin and Blockchain
118 Old Laws Clash With
New Technology
121 Internet Mob Justice
Finance
Key Trend Themes For 2017
Artificial Intelligence
Recognition
Bots
Security
Privacy
Big Data
E-Residents
Social Payments
Bitcoin and Blockchain
Automation
Internet Mobs and Trolls
© 2017 Future Today Institute16

17. Pay Special Attention To These Trends In Our Report
01 Artificial Intelligence
12 Hidden Bias in AI
13 Accountability and Trust
14 Bots
16 Cognitive Computing
20 Consolidation in AI
24 Robot Companions
26 Ethical Manufacturing
27 Universal Basic Income
30 Adaptive Learning
31 Nanodegrees
33 Faceless Recognition
34 Bias in Recognition Algorithms
35 Adversarial Images
37 Character Recognition and Analytics
39 Digital Frailty
40 Nutritional Labels For News
41 Limited-Edition News Products
43 One-To-Few Publishing
44 Journalism as a Service (Jaas)
47 Conversational Interfaces
54 Media Consolidation
56 Crowdlearning
59 Leaking
60 The First Amendment
in a Digital Age
63 Virtual Reality
64 Augmented Reality
65 360-degree Video
66 Holograms
67 Data Retention Policies
73 Open Source App Vulnerabilities
74 Selfie Security
75 Prize Hacks
76 Automated Hacking
79 Weaponizing Wikileaks
93 Organizational Doxing
95 E-Residents
96 Social Payments
97 Bitcoin and Blockchain
121 Internet Mob Justice
123 City-Level Cyber Security
124 Smart City Systems
152 Anthropocene and Climate
Foundations, Philanthropists
& Non-profits
Key Trend Themes For 2017
Artificial Intelligence
Robotics
Recognition
Digital Frailty
Mixed Reality
Security
Privacy
Big Data
Bitcoin and Blockchain
Automation
Internet Mobs and Trolls
Smart Cities
Anthropocene and Climate
© 2017 Future Today Institute17

18. Pay Special Attention To These Trends In Our Report
01 Artificial Intelligence (whole section)
12 Hidden Bias in AI
13 Accountability and Trust
14 Bots
15 Deep Learning
16 Cognitive Computing
17 Smart Virtual Personal Assistants
18 Ambient Interfaces
19 Deep Linking
20 Consolidation in AI
21 Human-Machine Interfaces
22 Smart Dust
24 Robot Companions
26 Ethical Manufacturing
27 Universal Basic Income
33 Faceless Recognition
34 Bias in Recognition Algorithms
35 Adversarial Images
36 Ambient Proximity
37 Character Recognition and Analytics
39 Digital Frailty
40 Nutritional Labels For News
43 One-To-Few Publishing
47 Conversational Interfaces
49 Real-Time Fact Checking
52 WebRTC
54 Media Consolidation
56 Crowdlearning
57 Blocking the Ad Blockers
58 Natural Language Generation
for Reading Levels
59 Leaking
60 The First Amendment
in a Digital Age
61 Synthetic Data Sets
62 Torrents
63 Virtual Reality
64 Augmented Reality
65 360-degree Video
66 Holograms
67 Data Retention Policies
68 Remote Kill Switches
69 Zero Day Exploits
70 Backdoors
71 Glitches
72 Darknets
73 Open Source App Vulnerabilities
74 Selfie Security
75 Prize Hacks
76 Automated Hacking
77 Offensive Government Hacking
78 Cyber Mission Forces in the Field
79 Weaponizing Wikileaks
80 Anonymity
81 Differential Privacy
82 Digital Self-Incrimination
83 Trolls
84 Authenticity
85 Revenge Porn
86 Encryption Management
87 Eye in the Sky
88 Right to Eavesdrop/
Be Eavesdropped On
89 Drone Surveillance
90 Private Networks
91 Ownership
92 Uploading Photos To Law
Enforcement Databases
93 Organizational Doxing
95 E-Residents
97 Bitcoin and Blockchain
103 Drone Lanes
104 Clandestine, Disappearing Drones
105 Autonomous Underwater Vehicles
106 Microdrones
107 Drone Delivery
108 Internet of Things
110 Open Source Systems
111 Adaptive Driving Systems
112 Vehicle-to-Vehicle Communications
113 Autonomous Testing Facilities
114 Solar Highways
115 Autonomous Vehicle Legislation
118 Old Laws Clash With
New Technology
119 Digital Caliphate
120 Asking Tech Companies
To Help Fight Terrorism
121 Internet Mob Justice
122 Overhauling Government
Tech Infrastructure
123 City-Level Cyber Security
124 Smart City Systems
130 Running Out of Space
For Genome Storage
131 Norms and Regulations (Biological)
146 Patient-Generated Health Data
152 Anthropocene and Climate
155 Quantum Computers
157 Internet of X
158 5G
Human Resources
Government, Politics,
Legal, Policy
Key Trend Themes For 2017
Artificial Intelligence
Robotics
Human-Machine Interfaces
Recognition
Digital Frailty
Mixed Reality
Security
Privacy
Big Data
Bitcoin and Blockchain
Drones
Internet of Things
Automation
Self-Driving Vehicles
Internet Mobs and Trolls
Genomic Editing
© 2017 Future Today Institute18

19. Pay Special Attention To These Trends In Our Report
01 Artificial Intelligence
02 Real-Time Machine Learning
03 Image Completion
04 Predictive Machine Vision
05 Natural Language Generation
08 Zero Knowledge Proofs
09 Algorithmic Personality Detection
12 Hidden Bias in AI
13 Accountability and Trust
14 Bots
15 Deep Learning
16 Cognitive Computing
19 Deep Linking
20 Consolidation in AI
24 Robot Companions
25 Collaborative Robots
28 Artificial Intelligence in Hiring
29 Productivity Bots
30 Adaptive Learning
31 Nanodegrees
33 Faceless Recognition
34 Bias in Recognition Algorithms
35 Adversarial Images
37 Character Recognition and Analytics
63 Virtual Reality
64 Augmented Reality
65 360-degree Video
66 Holograms
67 Data Retention Policies
70 Backdoors
75 Prize Hacks
80 Anonymity
81 Differential Privacy
82 Digital Self-Incrimination
83 Trolls
86 Encryption Management
93 Organizational Doxing
99 FOBO (Fear Of Being Offline)
118 Old Laws Clash With
New Technology
128 Neuroenhancers
© 2017 Future Today Institute
Key Trend Themes For 2017
Artificial Intelligence
Robotics
Recognition
Mixed Reality
Security
Privacy
Big Data
Automation
Mixed Reality
Security
Privacy
Big Data
Automation
19

20. Pay Special Attention To These Trends In Our Report
01 Artificial Intelligence (whole section)
12 Hidden Bias in AI
13 Accountability and Trust
14 Bots
15 Deep Learning
16 Cognitive Computing
18 Ambient Interfaces
20 Consolidation in AI
21 Human-Machine Interfaces
22 Smart Dust
24 Robot Companions
25 Collaborative Robots
33 Faceless Recognition
34 Bias in Recognition Algorithms
35 Adversarial Images
36 Ambient Proximity
37 Character Recognition and Analytics
56 Crowdlearning
62 Torrents
63 Virtual Reality
64 Augmented Reality
67 Data Retention Policies
68 Remote Kill Switches
69 Zero Day Exploits
70 Backdoors
71 Glitches
72 Darknets
73 Open Source App Vulnerabilities
74 Selfie Security
75 Prize Hacks
76 Automated Hacking
77 Offensive Government Hacking
78 Cyber Mission Forces in the Field
79 Weaponizing Wikileaks
80 Anonymity
81 Differential Privacy
82 Digital Self-Incrimination
83 Trolls
84 Authenticity
85 Revenge Porn
86 Encryption Management
87 Eye in the Sky
88 Right to Eavesdrop/
Be Eavesdropped On
89 Drone Surveillance
90 Private Networks
91 Ownership
92 Uploading Photos To Law
Enforcement Databases
93 Organizational Doxing
96 Social Payments
97 Bitcoin and Blockchain
103 Drone Lanes
104 Clandestine, Disappearing Drones
105 Autonomous Underwater Vehicles
106 Microdrones
107 Drone Delivery
108 Internet of Things
109 Intelligent Cameras
110 Open Source Systems
111 Adaptive Driving Systems
112 Vehicle-to-Vehicle Communications
113 Autonomous Testing Facilities
114 Solar Highways
115 Autonomous Vehicle Legislation
118 Old Laws Clash With
New Technology
120 Asking Tech Companies
To Help Fight Terrorism
121 Internet Mob Justice
123 City-Level Cyber Security
124 Smart City Systems
132 Biointerfaces
154 Invisibility Cloaks
155 Quantum Computers
157 Internet of X
158 5G
Law Enforcement
Key Trend Themes For 2017
Artificial Intelligence
Robotics
Human-Machine Interfaces
Recognition
Mixed Reality
Security
Privacy
Big Data
Social Payments
Bitcoin and Blockchain
Drones
Internet of Things
Automation
Self-Driving Vehicles
Internet Mobs and Trolls
Smart Cities
© 2017 Future Today Institute20

21. Pay Special Attention To These Trends In Our Report
01 Artificial Intelligence
02 Real-Time Machine Learning
03 Image Completion
04 Predictive Machine Vision
05 Natural Language Generation
08 Zero Knowledge Proofs
09 Algorithmic Personality Detection
12 Hidden Bias in AI
13 Accountability and Trust
14 Bots
16 Cognitive Computing
18 Ambient Interfaces
20 Consolidation in AI
33 Faceless Recognition
34 Bias in Recognition Algorithms
35 Adversarial Images
37 Character Recognition and Analytics
49 Real-Time Fact Checking
62 Torrents
63 Virtual Reality
64 Augmented Reality
67 Data Retention Policies
68 Remote Kill Switches
69 Zero Day Exploits
70 Backdoors
71 Glitches
72 Darknets
73 Open Source App Vulnerabilities
74 Selfie Security
75 Prize Hacks
76 Automated Hacking
77 Offensive Government Hacking
78 Cyber Mission Forces in the Field
79 Weaponizing Wikileaks
80 Anonymity
81 Differential Privacy
82 Digital Self-Incrimination
83 Trolls
84 Authenticity
85 Revenge Porn
86 Encryption Management
87 Eye in the Sky
88 Right to Eavesdrop/
Be Eavesdropped On
89 Drone Surveillance
90 Private Networks
91 Ownership
92 Uploading Photos To Law
Enforcement Databases
93 Organizational Doxing
95 E-Residents
96 Social Payments
97 Bitcoin and Blockchain
103 Drone Lanes
104 Clandestine, Disappearing Drones
105 Autonomous Underwater Vehicles
106 Microdrones
107 Drone Delivery
108 Internet of Things
110 Open Source Systems
112 Vehicle-to-Vehicle Communications
115 Autonomous Vehicle Legislation
118 Old Laws Clash With
New Technology
120 Asking Tech Companies
To Help Fight Terrorism
121 Internet Mob Justice
Lawyers, Law Firms & Lobbyists
Key Trend Themes For 2017
Artificial Intelligence
Recognition
Mixed Reality
Security
Privacy
Big Data
E-Residents
Drones
Internet of Things
Automation
Self-Driving Vehicles
Internet Mobs and Trolls
© 2017 Future Today Institute21

22. Pay Special Attention To These Trends In Our Report
01 Artificial Intelligence
02 Real-Time Machine Learning
04 Predictive Machine Vision
03 Image Completion
12 Hidden Bias in AI
13 Accountability and Trust
15 Deep Learning
16 Cognitive Computing
20 Consolidation in AI
21 Human-Machine Interfaces
22 Smart Dust
23 Soft Robotics
24 Robot Companions
25 Collaborative Robots
56 Crowdlearning
61 Synthetic Data Sets
63 Virtual Reality
64 Augmented Reality
93 Organizational Doxing
108 Internet of Things
125 Precision Medicine
126 Molecular Programming
127 Nanobot Treatments
128 Neuroenhancers
129 Synthetic Biology
130 Running Out of Space
For Genome Storage
131 Norms and Regulations (Biological)
132 Biointerfaces
134 Smartwatches
135 Wireless Body Area Networks
136 Connected Fabrics
140 Tattooables
141 Ingestibles/ Implantables
142 Earables
143 Thinkables
144 Smart Thread
145 Bioelectronics
146 Patient-Generated Health Data
147 Touch-Sensitive Prosthetics
156 3D Printing
157 Internet of X
158 5G
Medical, Health, Life Sciences
Key Trend Themes For 2017
Artificial Intelligence
Robotics
Human-Machine Interfaces
Recognition
Mixed Reality
Security
Privacy
Big Data
Internet of Things
Genomic Editing
Wearables
Bioelectronics
© 2017 Future Today Institute22

23. Pay Special Attention To These Trends In Our Report
01 Artificial Intelligence
02 Real-Time Machine Learning
03 Image Completion
04 Predictive Machine Vision
05 Natural Language Generation
06 Generative Algorithms For Voice
07 Generative Algorithms For Sound
09 Algorithmic Personality Detection
12 Hidden Bias in AI
13 Accountability and Trust
14 Bots
15 Deep Learning
16 Cognitive Computing
17 Smart Virtual Personal Assistants
18 Ambient Interfaces
19 Deep Linking
20 Consolidation in AI
22 Smart Dust
29 Productivity Bots
33 Faceless Recognition
34 Bias in Recognition Algorithms
35 Adversarial Images
37 Character Recognition and Analytics
38 Attention
39 Digital Frailty
40 Nutritional Labels For News
41 Limited-Edition News Products
42 Artificial Intelligence For News
43 One-To-Few Publishing
44 Journalism as a Service (Jaas)
45 Transparency in Metrics
46 Intentional Rabbit Holes
47 Conversational Interfaces
48 Dynamic Listicles
49 Real-Time Fact Checking
50 Connected TVs
51 Cord Cutting
52 WebRTC
53 Streaming Social Video
54 Media Consolidation
55 CARv2.0
56 Crowdlearning
57 Blocking the Ad Blockers
58 Natural Language Generation
for Reading Levels
59 Leaking
60 The First Amendment
in a Digital Age
61 Synthetic Data Sets
62 Torrents
63 Virtual Reality
64 Augmented Reality
65 360-degree Video
66 Holograms
67 Data Retention Policies
69 Zero Day Exploits
71 Glitches
73 Open Source App Vulnerabilities
75 Prize Hacks
76 Automated Hacking
79 Weaponizing Wikileaks
80 Anonymity
81 Differential Privacy
82 Digital Self-Incrimination
83 Trolls
84 Authenticity
86 Encryption Management
91 Ownership
93 Organizational Doxing
96 Social Payments
103 Drone Lanes
118 Old Laws Clash With
New Technology
121 Internet Mob Justice
157 Internet of X
158 5G
News, Journalism,
Media, Publishing
Key Trend Themes For 2017
Artificial Intelligence
Recognition
Digital Frailty
Accountability
Video
Mixed Reality
Security
Privacy
Big Data
Social Payments
Drones
Automation
Internet Mobs and Trolls
© 2017 Future Today Institute23

24. Pay Special Attention To These Trends In Our Report
01 Artificial Intelligence
09 Algorithmic Personality Detection
12 Hidden Bias in AI
13 Accountability and Trust
14 Bots
17 Smart Virtual Personal Assistants
28 Artificial Intelligence in Hiring
29 Productivity Bots
30 Adaptive Learning
31 Nanodegrees
32 Sharing Economy and Lendership
34 Bias in Recognition Algorithms
37 Character Recognition and Analytics
58 Natural Language Generation
for Reading Levels
67 Data Retention Policies
75 Prize Hacks
83 Trolls
84 Authenticity
86 Encryption Management
91 Ownership
93 Organizational Doxing
157 Internet of X
158 5G
Professional Services
Key Trend Themes For 2017
Artificial Intelligence
Robotics
Recognition
Mixed Reality
Security
Privacy
Big Data
Automation
© 2017 Future Today Institute24

25. Pay Special Attention To These Trends In Our Report
01 Artificial Intelligence (whole section)
12 Hidden Bias in AI
13 Accountability and Trust
14 Bots
15 Deep Learning
16 Cognitive Computing
18 Ambient Interfaces
19 Deep Linking
20 Consolidation in AI
21 Human-Machine Interfaces
25 Collaborative Robots
26 Ethical Manufacturing
29 Productivity Bots
31 Nanodegrees
33 Faceless Recognition
34 Bias in Recognition Algorithms
35 Adversarial Images
36 Ambient Proximity
51 Cord Cutting
52 WebRTC
63 Virtual Reality
64 Augmented Reality
65 360-degree Video
66 Holograms
67 Data Retention Policies
68 Remote Kill Switches
69 Zero Day Exploits
70 Backdoors
71 Glitches
72 Darknets
73 Open Source App Vulnerabilities
74 Selfie Security
75 Prize Hacks
76 Automated Hacking
77 Offensive Government Hacking
78 Cyber Mission Forces in the Field
79 Weaponizing Wikileaks
80 Anonymity
81 Differential Privacy
82 Digital Self-Incrimination
83 Trolls
84 Authenticity
85 Revenge Porn
86 Encryption Management
87 Eye in the Sky
88 Right to Eavesdrop/
Be Eavesdropped On
89 Drone Surveillance
90 Private Networks
91 Ownership
92 Uploading Photos To Law
Enforcement Databases
93 Organizational Doxing
95 E-Residents
96 Social Payments
97 Bitcoin and Blockchain
103 Drone Lanes
104 Clandestine, Disappearing Drones
105 Autonomous Underwater Vehicles
106 Microdrones
107 Drone Delivery
108 Internet of Things
120 Asking Tech Companies
To Help Fight Terrorism
121 Internet Mob Justice
124 Smart City Systems
153 Space Exploration
155 Quantum Computers
156 3D Printing
157 Internet of X
158 5G
Technology Companies/
Manufacturers
Key Trend Themes For 2017
Artificial Intelligence
Robotics
Human-Machine Interfaces
Recognition
Mixed Reality
Security
Privacy
Big Data
E-Residents
Drones
Internet of Things
Automation
Internet Mobs and Trolls
Smart Cities
Space Exploration
© 2017 Future Today Institute25

26. Pay Special Attention To These Trends In Our Report
01 Artificial Intelligence
09 Algorithmic Personality Detection
12 Hidden Bias in AI
13 Accountability and Trust
14 Bots
20 Consolidation in AI
25 Collaborative Robots
26 Ethical Manufacturing
27 Universal Basic Income
28 Artificial Intelligence in Hiring
29 Productivity Bots
31 Nanodegrees
32 Sharing Economy and Lendership
34 Bias in Recognition Algorithms
36 Ambient Proximity
37 Character Recognition and Analytics
39 Digital Frailty
43 One-To-Few Publishing
54 Media Consolidation
56 Crowdlearning
63 Virtual Reality
64 Augmented Reality
67 Data Retention Policies
73 Open Source App Vulnerabilities
75 Prize Hacks
79 Weaponizing Wikileaks
80 Anonymity
81 Differential Privacy
82 Digital Self-Incrimination
83 Trolls
84 Authenticity
86 Encryption Management
92 Uploading Photos To Law
Enforcement Databases
93 Organizational Doxing
95 E-Residents
118 Old Laws Clash With
New Technology
152 Anthropocene and Climate
157 Internet of X
158 5G
Trade, Professional Associations,
Interest Groups
Key Trend Themes For 2017
Artificial Intelligence
Recognition
Digital Frailty
Mixed Reality
Security
Privacy
Big Data
E-Residents
Automation
Anthropocene and Climate
© 2017 Future Today Institute26

27. Pay Special Attention To These Trends In Our Report
01 Artificial Intelligence
09 Algorithmic Personality Detection
12 Hidden Bias in AI
13 Accountability and Trust
15 Deep Learning
16 Cognitive Computing
17 Smart Virtual Personal Assistants
20 Consolidation in AI
24 Robot Companions
32 Sharing Economy and Lendership
43 One-To-Few Publishing
53 Streaming Social Video
63 Virtual Reality
64 Augmented Reality
65 360-degree Video
66 Holograms
67 Data Retention Policies
73 Open Source App Vulnerabilities
74 Selfie Security
75 Prize Hacks
83 Trolls
84 Authenticity
86 Encryption Management
93 Organizational Doxing
103 Drone Lanes
107 Drone Delivery
152 Anthropocene and Climate
153 Space Exploration
158 5G
Travel, Entertainment
Key Trend Themes For 2017
Artificial Intelligence
Robotics
Recognition
Mixed Reality
Security
Privacy
Big Data
Drones
Automation
Anthropocene and Climate
Space Exploration
© 2017 Future Today Institute27

28. Pay Special Attention To These Trends In Our Report
01 Artificial Intelligence
02 Real-Time Machine Learning
03 Image Completion
04 Predictive Machine Vision
05 Natural Language Generation
08 Zero Knowledge Proofs
09 Algorithmic Personality Detection
12 Hidden Bias in AI
13 Accountability and Trust
14 Bots
15 Deep Learning
16 Cognitive Computing
19 Deep Linking
20 Consolidation in AI
24 Robot Companions
25 Collaborative Robots
26 Ethical Manufacturing
27 Universal Basic Income
28 Artificial Intelligence in Hiring
29 Productivity Bots
30 Adaptive Learning
31 Nanodegrees
33 Faceless Recognition
34 Bias in Recognition Algorithms
37 Character Recognition and Analytics
63 Virtual Reality
64 Augmented Reality
66 Holograms
67 Data Retention Policies
72 Darknets
73 Open Source App Vulnerabilities
81 Differential Privacy
86 Encryption Management
93 Organizational Doxing
95 E-Residents
110 Open Source Systems
112 Vehicle-to-Vehicle Communications
115 Autonomous Vehicle Legislation
158 5G
Work (Future of)
Key Trend Themes For 2017
Artificial Intelligence
Robotics
Recognition
Mixed Reality
Security
Privacy
Big Data
E-Residents
Automation
Self-Driving Vehicles
© 2017 Future Today Institute28

29. The Future Today Institute’s forecasting
model relies on quantitative and qualitative
data. Our six steps alternate between
broad and narrow scopes, which include:
identifying very early stage fringe research,
focusing on patterns, interrogating trend
candidates, calculating a trend’s trajectory,
writing scenarios and finally pressure-testing
strategies and recommendations.
METHODOLOGY
29
© 2017 Future
Today Institute

30. HOW TO USE THE
2017 REPORT
Our 2017 Trend Report reveals strategic opportunities and
challenges for your organization in the coming year.
The Future Today Institute’s annual Trend Report prepares leaders and organ-
izations for the year ahead, so that you are better positioned to see emerging
technology and adjust your strategy accordingly. Use our report to identify
near-future business disruption and competitive threats while simultaneously
finding new collaborators and partners. Most importantly, use our report as a
jumping off point for deeper strategic planning.
Explaining why these trends matter.
Rather than simply offering an overview of the trends that will matter in 2017,
our annual report takes the additional step of explaining why and how these
trends will impact your organization. In some cases, you will see very specific
use cases and descriptive illustrations, so that you can more clearly envision
the potential outcomes of these trends during the next 12 months.
© 2017 Future Today Institute30

31. You’ll find six important pieces of information with each trend in
this report.
01 Key Insight
Short, easy explanation of this trend so that you can internalize it and discuss
with your colleagues.
02 Examples
Real-world use cases, some of which will sound familiar.
03 What’s Next
What this trend means for you and your organization in the coming year.
04 Watchlist
Notable companies, founders and researchers working in this trend space.
05 Years On The List
We’ve noted how many years we’ve been tracking the trend, even as it has
evolved. This is an indication of how the trend is progressing.
06 Action Meter
An easy-to-read graphic indicating where the trend is along it’s trajectory.
It tells you whether the trend needs monitoring, should inform your strategy,
or requires action.
We recommend using our 2017 Tech Trends Report as part of a formalized
process to evaluate disruptive technologies throughout the year.
© 2017 Future Today Institute
Needs Monitoring Informs Strategy Requires Action
31
01
05
02
03
06
04
HOW TO USE
OUR REPORT

32. WHAT IS A TREND,
EXACTLY? Mapping the future for your organization begins with identifying early signposts as
you look out on the horizon. In order to chart the best way forward, you must un-
derstand emerging trends: what they are, what they aren’t, and how they operate.
At any moment, there are hundreds of small shifts in technology—developments on
the fringes of science and society—that will impact our lives in the future. A trend is
a new manifestation of sustained change within an industry sector, society, or hu-
man behavior. A trend is more than the latest shiny object.
Fundamentally, a trend leverages our basic human needs and desires in a meaning-
ful way, and it aligns human nature with breakthrough technologies and inventions.
All trends share a set of conspicuous, universal features:
• A trend is driven by a basic human need, one that is catalyzed by new
technology.
• A trend is timely, but it persists.
• A trend evolves as it emerges.
• A trend can materialize as a series of unconnectable dots which begin out
on the fringe and move to the mainstream.
Identifying something as a trend means connecting the dots, or relating changes in
the present to what’s coming in the future. To map what the future holds, seek out
the early adopters, the hackers, the developers with seemingly impossible ideas. It’s
within these circles that meaningful changes begin. As the trend evolves, the work
of these disparate groups begins to overlap, until it converges in a single point—be-
fore perhaps evolving once again.
© 2017 Future Today Institute32

33. WHAT IS A TREND, EXACTLY?
Because trends are a different way of seeing and interpreting our current reality, they provide a useful framework to
organize our thinking, especially when we’re hunting for the unknown and trying to learn something about which we
do not yet know how to ask.
There are ten modern sources of change in society with technology as the primary connector.
Therefore, if we want to forecast the future of anything, we would need to plot out the intersecting vectors of
change—their direction and magnitude—as they relate to new developments in emerging technology.
These features are what make up a trend. Together they distinguish a trend from a fad, which will invariably pass.
Trends are what help us to forecast the future.
Wealth
distribution
Education Government Politics Public health
Demography Economy Environment Journalism
Media
(our individual and
collective use of social
networks, chat services,
digital video channels,
photo sharing services
and so on)
01
06
02
07
03
08
04
09
05
10
33
© 2017 Future
Today Institute

34. WHAT IS A TREND,
EXACTLY?
The future is simultaneously 250 years, 3 decades, 1 months,
88 hours, 37 minutes and 14 seconds from right now. Forecast-
ing the future requires thinking in contradictory ways. We must
accept that the future is not predetermined—that we can both
know what’s past the horizon and intervene to shape it—while
simultaneously acknowledging that any number of variables, at
any time, can influence an outcome. We must solve the paradox
of the present by practicing ambidextrous thinking.
When it comes to technology, it is helpful to organize the evolu-
tion of trends along six general time zones. They are not arbitrary;
they follow the pattern of acceleration across various sectors of
the tech ecosystem. For example, consider the evolution of cars:
Futures of Cars, 2017 - 2057
Now: within the next 12 months.
By the end of 2018, cars will be equipped with software updates and new sensors that
perform more functions for the driver, such as parking and adaptive cruise control.
Near-term: 1 - 5 years.
By 2022, most cars will be equipped with cross-path cameras to sense nearby objects
and they will have adaptive cruise control for driving in stop-and-go traffic. They will
offer many driverless functions—but you will still need to operate the car in neighbor-
hoods and many city streets.
Mid-range: 5 - 10 years.
By 2027, advanced Lidar and WiFi technology will transmit your vehicle’s location and
will recognize other vehicles sharing the road. This will begin to enable cars to drive
themselves on highways and many city streets.
Long-range: 10 - 20 years.
By 2037, the highway system will have been upgraded to work in symbiosis with
semi-autonomous vehicles. Human drivers will take over on smaller streets. On man-
dated autonomous highway lanes, people will be free to read, watch videos or con-
duct work.
Far-range: 20 - 30 years.
By 2047, we may no longer own cars and cities might no longer operate buses. In-
stead, automated buses could be subsidized thought taxes and offered at no cost.
Those with the means to do so may subscribe to a transportation service, which op-
erates vehicles that are fully automated and will take them to destinations as desired.
Distant: more than 30 years.
By 2057, autonomous vehicles could be connected to a series of high-speed maglev
trains, which may have started to supplant commercial air routes on the East and
West coasts.
© 2017 Future Today Institute
The future is simultaneously 250
years, 3 decades, 1 months, 88
hours, 37 minutes and 14 seconds
from right now.
34

35. Artificial Intelligence
Second year on the list
Key Insight
Many facets of artificial intelligence (AI) have made our list since we first started
publishing this report a decade ago. For 2017, we have grouped them all under
one trend.
What You Need To Know About AI
Think about AI as the next layer of technology that will be integrated into
everything you do professionally. Simply put, AI is a branch of computer sci-
ence in which computers are programmed to do things that normally require
human intelligence. This includes learning, reasoning, problem-solving, under-
standing language and perceiving a situation or environment. AI is an extremely
large, broad field, which uses its own computer languages and even special kids
of computer networks WHICH are modeled on our human brains. The idea that
we might someday create artificially intelligent, sentient robots was first sug-
gested by prominent philosophers in the mid-1600s.
There are two kinds of AI: weak (or “narrow”) and strong (or “general”). When
Netflix makes recommendations to you, or Amazon displays books you might
like to read, that’s narrow AI. The H.A.L. supercomputer from 2001: A Space
Odyssey, which was not only sentient, but decided it no longer had use for us
humans, is a representation of artificial general intelligence.
For the past six decades, researchers have been modeling AI using our own hu-
man brain as inspiration. Neural networks are the basic computer architecture
that attempts to mimic some of what we know about how the human brain and
central nervous system transfers signals.
A neural network is the place where information is sent and received, and a
program is the set of meticulous, step-by-step instructions that tell a system
precisely what to do so that it will accomplish a specific task. How you want
the computer to get from start to finish—essentially, a set of rules—is the “algo-
rithm.”
Machine learning programs run on neural networks and analyze data in order to
help computers find new things without being explicitly programmed where to
look. Within the field of AI, machine learning is useful because it can help com-
puters to predict and make real-time decisions without human intervention.
001 Deep Neural Networks (see also “Deep Learning”)
Deep learning is a relatively new branch of machine learning, and it will soon be
an invisible part of every organization. Programmers use special deep learning
algorithms alongside a corpus of data—typically many terabytes of text, images,
videos, speech and the like. The system is trained to learn on its own. While con-
ceptually, deep learning isn’t new, what’s changed recently is the amount of com-
pute power and the volume of data that’s become available. In practical terms,
this means that more and more human processes will be automated. Including
the writing of software, which computers will soon start to do themselves.
002 Real-Time Machine Learning
It is recently possible to use a continual flow of transactional data and adjust
models in real-time. Potential use cases include: matching customers to the right
product as they are looking at a website, re-writing content on a site to match the
needs of each individual user, real-time fraud detection, and security measures
such as authenticating someone based on her typing habits.
TRENDS
001 - 013
© 2017 Future Today Institute
Needs Monitoring Informs Strategy Requires Action
35
HBO’s new series Westworld contemplates
a world in which artificially intelligent
humanoids are built for our amusement.

36. 36 © 2017 Future Today Institute
TRENDS
001 - 013
006 Generative Algorithms For Voice
Researchers at MIT are studying how children learn new words in order to train
computers on automatic speech recognition. As humans, we are able to master
a new concept from just one or two examples; for machines, this is a more diffi-
cult task when it comes to language. In the coming year, we expect to see more
research emerging in this area.
007 Generative Algorithms For Sound
MIT’s CSAIL researchers have trained computers to watch videos and predict cor-
responding sounds in our physical world. For example, what sound is generated
when a wooden drumstick taps a couch? A pile of leaves? A glass windowpane?
The focus of this research is to help robots understand how objects interact with
each other in the physical realm. But future versions of the algorithms could be
used to automatically produce sound and sound effects for news videos, mov-
ies and TV shows. It also raises the specter of audio fraud—what happens when
computers are able to spoof our voices and natural sound?
008 Zero-Knowledge Proofs
With all of the hacking scandals that have plagued us in the past few years, smart
businesses and organizations are turning to zero-knowledge proofs, which allows
one party to verify data without conveying any additional information (like how
or why the mathematical statement is true). In essence, this eliminates the need
for a company to store private identity data during the verification process. Ze-
ro-knowledge proofs aren’t new, but deploying them to protect our credit cards
003 Image Completion
If a computer system has access to enough images—millions and millions—it can
patch and fill in holes in pictures. There are practical applications for amateur
photographers: if the foreground of a mountain is out of focus, another version of
the scene can be swapped in to generate the perfect picture. Image completion
is also a useful tool for law enforcement and military intelligence officers—com-
puters can now assist them in identifying who or what is in the frame.
004 Predictive Machine Vision
Researchers at MIT’s CSAIL have trained computers to not only recognize what’s
in a video, but to predict what humans will do next. Trained on YouTube videos
and TV shows such as “The Office” and “Desperate Housewives,” a computer sys-
tem can now predict whether two people are likely to hug, kiss, shake hands or
slap a high five. This research will someday enable robots to more easily navigate
human environments—and to interact with us humans by taking cues from our
own body language.
005 Natural Language Generation
Algorithms can transfer data into a narrative using natural language generation.
Dozens of news and other organizations, including Bloomberg and the Asso-
ciated Press, are using Automated Insights, which mines data and is capable
of writing more than 2,000 stories per second. They will use natural language
generation to produce stories about fantasy football, earnings reports and the
like. Narrative Science employs its NLG system to build narratives out of big data
sets and to help non-data science people make better sense of what’s happening
within their organizations.
Artificial Intelligence cont.
© 2017 Future Today Institute
In the near-future, we won’t write code for
computers. Instead, we’ll train them like dogs.
36

37. 37 © 2017 Future Today Institute
TRENDS
001 - 013
010 Algorithm Marketplaces
Most organizations can’t staff a team of developers who have unlimited time to
create, test and refine algorithms. As a result, communities of developers are of-
fering up their algorithms in emerging algorithm marketplaces. Algorithmia and
DataMapper are a sort of Amazon for algorithms, where developers can upload
their work to the cloud and receive payment when others pay to access it. DataXu
offers a marketplace for its proprietary algorithms. PrecisionHawk has launched
a marketplace for predictive agriculture algos. A number of other networks, such
as Nara Logics, MetaMind, Clarifai offer tools for developers to build deep learn-
ing into any application. Look for even more niche marketplaces in 2017.
011 Pre-Trained AI Chips
A number of companies, including IBM, Google, Intel and Movidius, are working
on extremely powerful chips, and some come pre-trained. In short, this means
that the chips are ready to work on AI projects. Google’s chip is called a Tensor
Processing Unit (or TPU), and was specifically built for the deep learning branch
of AI. It is designed to work with the company’s TensorFlow system. For refer-
ence, TPUs are what was used in the famous AlphaGo match between Goog-
le’s DeepMind system and a world Go champion. Marketing pre-trained chips to
businesses will speed up commercialization and as a result will further R&D.
and online identities is an emerging application. Irish startup Sedicii recently
launched its zero-proof software, which promises to reduce fraud. Researchers
at Microsoft and Princeton University are working on a zero-knowledge proof
so that inspectors can identify something as a nuclear weapon without requiring
them to take it apart, which would spread information about how to build one.
009 Algorithmic Personality Detection
Marketers will soon have access to algorithms that can assess your personality
and predict your specific needs and desires. Nashville-based startup Crystal and
IBM’s Personality Insights both use social data and will rewrite everything from
emails to resumes based on each of your intended recipients. Meanwhile, some
life insurance underwriters are attempting to assess your personality—via your
magazine and website subscriptions, the photos you post to social media, and
more—in order to determine how risky an investment you are. Some lenders have
used personality algorithms to predict your future financial transactions. (The
data show that if you look at two people with the same professional and personal
circumstances, the one with a higher college G.P.A. will be more likely to pay off
a debt.) Algorithms will harness your data in order to assess your predicted suc-
cess at work, how likely you are to bounce around jobs and more.
Artificial Intelligence cont.
© 2017 Future Today Institute
Algorithmia is a marketplace for algorithms.
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TRENDS
001 - 013
Researchers at a number of universities—including Carnegie Mellon, MIT, Prince-
ton, University of California-Berkeley, International Computer Science Insti-
tute, among others—have started to discover unintended, but explicit bias in
algorithms. In 2016, Google was shown to display high-income job ads to men
six times more often than women, while searching for “C.E.O.” in Google Images
returned 89% men. (As of October 6, 2016, the first instance of a woman was
actually a marketing photo for the CEO Barbie doll.)
Part of the problem has to do with the building blocks of AI: computers are
trained using a limited initial set of data, and the training programs are built by
humans. Often, the training sets reveal unacknowledged bias hidden within us.
So if the training data predominantly shows white male CEOs, algorithms won’t
recognize people who look different. How can we push the limits of data and
algorithms without accidentally creating future socio-economic, religious and
ethnic divides?
012 Uncovering Hidden Bias in AI
Every single day, you are creating unimaginable amounts of data, both explicitly
(uploading and tagging photos on Facebook) or implicitly (searching online for
cooking utensils, cars, or doctors). That data is mined and used, often without
your direct knowledge or understanding, by algorithms. It is used to create ad-
vertising, to help potential employers predict our behaviors, to determine our
mortgage rates and even to help law enforcement predict whether or not we’re
likely to commit a crime. In May 2016, ProPublica.org published an exceptional
investigation on machine bias and the problem of using AI to predict future crim-
inals. Their findings: so-called “risk assessment” software is increasingly com-
mon in courtrooms across the nation, and it is used to inform decisions about
everything from bond amounts to the length of a criminal sentencing. Perhaps
unsurprisingly, the software is biased against black people. (We encourage you
to read ProPublica’s full report: https://www.propublica.org/article/machine-bi-
as-risk-assessments-in-criminal-sentencing.)
As of October 6, 2016, the first instance of
a woman returned for “CEO” in a Google
Images search was a marketing photo for the
CEO Barbie doll.
Artificial Intelligence cont.
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TRENDS
001 - 013
013 Accountability and Trust
We will soon reach a point when we will no longer be able to tell if a data set has
been tampered with, either intentionally or accidentally. AI systems rely on our
trust. If we no longer trust the outcome, decades of research and technological
advancement will be for naught. Leaders in every sector—government, business,
the nonprofit world and so on—must have confidence in the data and algorithms
used.
Building trust and accountability is a matter of showing the work performed. This
is a complicated process, as understandably corporations, government offices,
law enforcement agencies and other organizations want to keep data private.
Committing to transparency in method would create trust without necessarily
divulging any personal data used.
In addition, hiring an ethicist to work directly with managers and developers, as
well as greatly diversifying the pool of developers to include people of different
races, ethnicities and genders will solve for inherent bias in AI systems.
© 2017 Future Today Institute
Trust and accountability are important trends
within AI in 2017.
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Bots
Second year on the list
Key Insight
You’ve no doubt heard of a bot: a software application that’s been designed to
automate certain tasks, such as scheduling or managing basic customer service
requests. In the past year, bots have emerged from the fringe and have started
to enter our mainstream vocabulary. There are now 12,000 Facebook Messenger
bots available, as well as a number of platforms that make it easy for anyone to
build a bot.
Examples
In the 1960s, Joseph Weizenbaum wrote a computer program called Eliza that
was capable of simulating a conversation between a psychiatrist and patient. It
offered up plausible responses to common questions. In March 2016, the world
watched as @Tai.ai, a Microsoft experimental Twitterbot, went on an anti-Semitic,
homophobic, racist rampage within 24 hours after its first tweet.
What’s Next
Many brands you recognize (1-800-Flowers.com, Pizza Hut and Dutch airline
KLM) are using chatbots for marketing and customer service. Disney partnered
with bot developer Imperson to deploy character bots, including Miss Piggy.
Tai.ai was built on the same platform as Microsoft’s experimental Mandarin-lan-
guage bot, Xiaoice. Both were capable of intimate conversations with users, be-
cause the program is able to remember details from previous conversations and
because it mined the Internet for human conversations in order to synthesize
chat sessions. The key difference: Xiaoice was released in a country where cit-
izens carefully guard their activity online, while Tai.ai was born into America’s
free-wheeling internet. During the recent campaign cycle, we witnessed the rise
of botnets—networks of computers designed to send out spam. Fake social me-
dia accounts, many of which originated in Russia, artfully tricked people into hav-
ing arguments about everything from Donald Trump to immigration to taxes.
The intelligence community should deploy bots for surveillance and for digital
diplomacy. HR managers can use bots to train employees. Meantime, as Slack
continues to grow in scale and popularity, bots within that environment will help
automate meetings and status updates and more, saving time and increasing
productivity.
Watchlist
Russia; Weibo; WeChat; Alphabet (Google); Snapchat; China; Microsoft; Chatfuel;
Pandorabots; Twilio; Amazon; Facebook; Slack
The Tay.ai Twitter bot went on a
homophobic, sexist, racist, anti-Semitic
bender within 24 hours of its launch.
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We are training bots in our own image. When developing your bots in 2017, we
recommend that you rate your work before deployment. Use this scale to rate
your bot on its effectiveness—and to determine, in advance, whether or not
you’ve accidentally encoded bias into your system. Rate your bot on a scale of
0 - 10, with 10 being the highest (and preferred) score
The Botness Scale
01 Is your bot’s purpose explicit? Will people interacting with your
bot clearly understand what its purpose is after the first few
interactions?
02 Does your bot perform its designated function well?
03 Is your bot easy to access, either on a designated platform
or across platforms?
04 Is it intuitive and easy to use?
05 Does your bot help people learn something new, or does it
effectively reinforce something that people already know?
06 Does your bot provide people with the necessary context
to have a good conversation?
07 Does the corpus (the initial, base set of questions and answers)
you’ve created reflect only one gender, race or ethnicity?
If so, was that intentional?
08 Did you assign your bot a traditional gender, ethnic or racial
identity? If so, does it reference any stereotypes?
09 Does your bot respond to gendered or sexist remarks?
Does it respond to racial epithets or religious slurs?
If it does respond, are the responses appropriate to people
of the group targeted?
10 Does your bot help people learn about their own biases
or broaden their worldviews?
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Deep Learning
Second year on the list
Key Insight
Programmers use special deep learning algorithms alongside a corpus of data—
typically many terabytes of text, images, videos, speech and the like. The system
is trained to learn on its own.
Examples
Deep learning is a relatively new branch of machine learning. The Oregon Trail
was easy for anyone to play because a person created the game, and he only in-
cluded straightforward choices and outcomes. But in real life, during 1810 - 1840,
the Oregon Trail presented more threats than what was in the game: there were
an infinite amount of variables to consider, which means an unknowable number
of possible outcomes. This is a problem for deep learning. Theoretically, an artifi-
cially intelligent machine could be trained to learn how to survive on the Oregon
Trail, even with such a high number of details to consider. Meanwhile, in early 2016,
Google’s AlphaGo—an AI program—beat a professional Go player 5-0. The great
hope of those working in AI is that someday, deep learning will have advanced
enough not just to make better decisions in a challenge like the Oregon Trail, or
to beat a human at Go—but to interact with us such that they’re indistinguishable
from other people. Except that their artificial neural networks are orders of mag-
nitude more powerful than the biological neural networks inside our heads.
What’s Next
Deep learning isn’t just used by Google, though researchers on the DeepMind
team are working on a host of applications for neural networks. Internet searching
will be aided by deep learning—we’ll have conversations and get to our desired
answers far more quickly than we do today searching through pages of content.
Chip designers at Fujitsu and Intel are using deep learning to design smaller,
faster systems. Scientists at the Karlsruhe Institute of Technology developed a
deep neural network that can automatically identify people’s faces using infrared
cameras—law enforcement will soon be able to see as clearly in total darkness
as they can in the bright daylight. DeepEar was developed by Bell Labs and is a
neural network that listens via our smartphones without draining our batteries.
Facebook is using this technology to automatically generate faceprints—which is
like a fingerprint, but using our photos instead. (The U.S. doesn’t yet have biom-
etric data protection laws.)
We recommend that you download your Facebook file if you haven’t already,
as this will give you an indication of what data Facebook is storing and how it
relates that information across user profiles. Qualcomm is developing neuromor-
phic chips, which have been used in robots to help them recognize objects they
haven’t seen before, or navigate themselves to a new location. In 2017, experi-
mentation in deep learning will begin to lay the groundwork for all that’s to come.
Watchlist
Baidu; IBM; Stanford’s AI Lab and Vision Lab; MIT’s CSAIL; Google; Amazon; Mi-
crosoft; Fujitsu, Facebook; Karlsruhe Institute of Technology; Bell Labs; University
of Toronto; University of California - Los Angeles, University of California - Berke-
ley; NSA; Qualcomm
Google’s DeepMind challenge a champion
Go player to a game—and won.
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Cognitive Computing
Fifth year on the list
Key Insight
This trend has evolved from a key idea in our 2010 trend report: anticipatory
computing. Cognitive computing systems use natural language processing and
artificial intelligence in order to understand our intentions.
Examples
By now, you will have heard of IBM’s Watson platform, a system that uses natural
language processing and machine learning to enable people and machines to
interact more naturally. Cognitive systems like Watson super-charge our human
ability to think through complex problems. Watson is assisting doctors, scientists,
engineers, lawyers and other professionals now in their everyday work. For ex-
ample, in August 2016, doctors in Tokyo were treating a patient who met all of
the criteria for acute myeloid leukemia, a blood cancer. After treatment, her re-
covery was unusually slow, and doctors began to wonder if there was something
else wrong. Yet their reading of the patient’s medical data kept pointing to acute
myeloid leukemia. They fed the patient’s data into Watson, which crunched her
genetic data against a larger corpus of data. Ten minutes later, Watson offered a
set of new diagnoses, which included acute myeloid leukemia but also showed
a strong possibility of a rare secondary leukemia. Doctors changed the woman’s
treatment plan, and within just a few weeks she was in remission.
What’s Next
Late in 2016, IBM launched the Cognitive Horizons Network, which is a collabo-
rative network dedicated to accelerating the field of cognitive computing. Those
schools include: Rensselaer Polytechnic Institute, Massachusetts Institute of
Technology, the University of Illinois at Urbana-Champaign, the University of
Maryland, Baltimore County, the University of Michigan, and the University of
Montreal. AI researchers at member schools will share resources and work jointly
on cognitive problems. Meanwhile, Watson is learning how to read and interpret
medical images—a job that was previously reserved for highly-trained radiolo-
gists. One interesting offshoot: a cognitive system could read all of the other data
captured in a medical image, making the image far more valuable to a patient’s
record. Watson and CVS have partnered to learn about patients and their medi-
cal histories in order to red-flag problematic behaviors, potentially transforming
the role of your local pharmacist. Working together with Watson, your local phar-
macist may begin to offer you holistic advice and interventions. IBM is now devel-
oping advanced data-centric supercomputing systems that will embed compute
power everywhere data resides in a system, which means a convergence of an-
alytics, modeling, visualization, and simulation, and driving new insights at very
fast speeds. In 2017, IBM will make its big push into a number of fields, including
finance, journalism, retail and healthcare, part of which includes better patient ac-
cess to their own health and prescription history. Watson—and for the time being,
your doctor—will see you now.
Watchlist
IBM Research, Cognitive Horizons Network
IBM’s Watson is a cognitive computing
platform.
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Smart Virtual Personal Assistants (SVPAs)
Third year on the list
Key Insight
Smart Virtual Personal Assistants use semantic and natural language processing,
along with our data, in order to anticipate what we want or need to do next...even
before we know to ask. You already have an SVPA in your phone, and you’ll have
one in your kitchen or living room soon.
Examples
If you’ve ever used Siri, Google Now or Amazon’s Alexa, you’re familiar with
SVPAs. They have nearly invisible interfaces and are being further designed so
that you talk, conversationally, with them. Most of the earlier SVPA apps have
now been acquired. Emu was acquired by Google, Donna was acquired by Ya-
hoo, Cue was acquired by Apple...and the list goes on. When it was still ac-
tive, Emu was a clever stand-in for a personal secretary. It would monitor the
conversation and automatically make suggestions as two people texted. If you
asked your friend to see a movie, Emu would immediately geolocate both of
you, suggest a nearby theater and show films and times, then check your calen-
dars for your availability. It would even display a preview for you to watch. Once
it determined the best time for you to meet, it would help you purchase tickets
and enter all the data into your calendar. And it did all of this inside a single mo-
bile application. This illustrates what to expect soon. According to what we’ve
learned from product teams throughout the tech industry, a new wave of virtual
assistants will continue to surface in the coming year.
What’s Next
SVPAs will become more pervasive in 2017. There are a number of devices launch-
ing in the coming year. Watch for Alexa-like products from Alphabet and Apple.
In addition to hardware, you’ll find a number of SVPA apps coming to market that
will help you do everything from schedule appointments to feed you real-time
information as you watch news programs. You’ll also see SVPAs hidden through-
out other connected devices, such as your home thermostat, your refrigerator
and your phone. Researchers at MIT, Stanford, and the University of Texas at
Austin are building infrastructure so that our devices will be able to listen and
watch: They’ll know the places we go, the people we interact with, our habits,
our tastes and preferences, and more. Then they’ll use this data to anticipate our
needs. Marketers, credit card companies, banks, local government agencies (po-
lice, highway administration), political campaigns and many others can harness
SVPAs to both surface and deliver critical information.
Watchlist
Stanford University; MIT; University of Texas at Austin; Amazon; Apple; Alphabet;
Microsoft; Yahoo; Robin Labs; IBM.
Amazon’s Echo is an SVPA for your living
room.
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Ambient Interfaces
Second year on the list
Key Insight
Ambient music pioneer and visionary Brian Eno wrote in his liner notes for Music
for Airports: “Ambient music must be able to accommodate many levels of lis-
tening attention without enforcing one in particular; it must be as ignorable as it
is interesting.” Our modern interfaces are becoming more and more like ambient
music—able to do more for us with fewer direct actions, yet still able to captivate
our attention.
Examples
If you’ve interacted with Google Now or Amazon’s Alexa, or if you own a smart-
watch, you’ve used an ambient interface. It listens—figuratively and literally—and
automatically delivers you the information you need to know, just as you need to
know it.
What’s Next
In our modern age of information, the average adult now makes more than 10,000
decisions a day: some big, like whether or not to invest in the stock market, and
some small, like whether to glance at your mobile phone when you see the screen
light up. What makes ambient design so tantalizing is that it should require us to
make fewer and fewer decisions in the future. Think of it as a sort of autocom-
plete for intention. Our mobile devices and many of the wearables coming to
market will be listening and observing in the background and will offer up either
text, audio or haptic notifications as needed, and those will be decided by algo-
rithm. In 2017, we expect to see even more ambient interfaces being embedded
into productivity software and apps, tools used by law enforcement, customer
loyalty apps, news apps and gaming systems.
Watchlist
Alphabet (Google); Amazon; Apple; Microsoft; Spotify; Mumble; IFTTT; Samsung
Google Home’s ambient interface delivers
you just the information you need, whenever
you need it.
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Deep Linking
Third year on the list (non-consecutive)
Key Insight
Deep mobile linking has been around since the beginning of smartphones. Deep
mobile links make it easier to find and share data across all of the apps in your
phone.
Examples
There are three kinds of deep links: traditional, deferred and contextual. Tradition-
al deep links reroute you from one app or site (such as a link posted in Twitter)
directly to the app, as long as you have that app installed. Deferred deep links
either link straight to content if the app is installed, or to an app store for you to
download the app first. In 2016, we saw the proliferation of deep linking through-
out the Apple and Android operating systems. Contextual deep links offer much
more robust information—they take you from site to app, app to site, or app to
app, and they can also offer personalized information. For example, when you
land at the airport, you might find that your airline app sends you a link to Uber.
(You’ll find similar offerings within Google Maps.) Apple’s iOS10 enables quick
switching and sharing between apps via deep links.
What’s Next
This interoperability signals a new shift in thinking, as many mobile app devel-
opers have been hesitant to use deep links. With Google and Apple’s changes,
app-to-app experiences should start to become more common. For example,
Branch is a startup that helps developers deliver previews of their applications
before users install them. We expect to see this trend develop as the deep linking
ecosystem starts to take off in the coming year.
Watchlist
Button; Google; Apple; Branch; MobileDeepLinking.org; Deeplink.me; Quixey;
Appsfire; AdRoll; tapCommerce; Vungle; InMobi; MoPub; Vserv
Like Uber, many platforms are now offering
deep mobile links.
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Consolidation in AI
First year on the list
Key Insight
All of the recent enthusiasm for AI has led to a number of startups—along with
lightening-fast acquisitions. Some now worry that the still nascent field of AI is
already under the direction of too few companies.
Examples
In the past year, Apple has bought Seattle-based AI startup Turi for $200 million,
but it isn’t the only company shopping around. To date, the top acquirer of AI
startups includes Google, Twitter, Apple, Intel, Salesforce, AOL and IBM, in that
order.
What’s Next
As with any technology, when just a few companies dominate the field, they tend
to monopolize both talent and intellectual property. This isn’t necessarily bad, but
when it comes to the future of AI, we should ask whether consolidation makes
sense for the greater good, and whether competition—and therefore access—will
eventually be hindered as we’ve seen in other fields such as telecommunications
and cable.
Watchlist
Alphabet (Google); Apple; Microsoft; Intel; Salesforce; AOL; IBM; Facebook; Am-
azon.
Consolidation has begun within AI.
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Human-Machine Interfaces
Second year on the list
Key Insight
Researchers are finding new ways to connect humans and mammals directly to
computers. With these human-machine interfaces, people can communicate via
thought alone. Which promises new options for those suffering from stroke and
paralysis.
Examples
At the University of Washington’s Center for Sensorimotor Neural Engineering,
researchers built a system allowing one person to transmit his thoughts directly
to another person. Using electrical brain recordings and a form of magnetic stim-
ulation, one researcher sent a brain signal to another person elsewhere on cam-
pus, causing his finger to tap a keyboard. Meanwhile, researchers at the Center
for Neuroengineering at Duke University have built a real-life Iron Man suit, al-
lowing a young man suffering from complete paralysis of his lower body to walk
out onto a soccer field and kick the first ball of the World Cup.
What’s Next
The team at Duke is now working on a so-called “Brainet,” which connects the
brains of a group of mammals to harness and direct their neural activity. One
successful experiment: to see if networking rats together would allow researchers
to solve a basic forecasting problem that individual rats struggled to complete
on their own. In every trial, the Brainet successfully solved the problem, and per-
formance improved the more the rats worked together. This work has a practical
and altruistic purpose: to help victims of stroke or traumatic brain injury regain
their cognitive abilities and motor function. Rather than having to relearn, they
need only reload those memories.
Watchlist
Duke University’s Center for Neuroengineering; University of Southern California;
University of Washington’s Center for Sensorimotor Neural Engineering; Johns
Hopkins University; Carnegie Mellon University; Starlab; Case Western Reserve
University; Penn State University; Johns Hopkins Applied Physics Laboratory;
DARPA.
Dr. Miguel Nicolelis and his team of researchers
at the Center for Neuroengineering at Duke
University are researching how to connect our
brains to machines. This brain-controlled car
is being operated by a rhesus monkey, whose
brains are similar to ours.
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Smart Dust
First year on the list
Key Insight
These are computers, no larger than a grain of dust, that are light enough they
can suspend in the air. (Or in your brain.)
Examples
For years, researchers have been hard at work on miniaturization, as they try to
shrink computers as much as possible, down to the size of sand or dust. Each
particle-computer consists of circuits and sensors capable of monitoring the en-
vironment, and even taking photographs. Scientists at the University of Califor-
nia Berkeley developed what they call “neural dust,” which are microscopic com-
puters that work alongside remote ultrasound to send and receive data about
the brain. Meanwhile, researchers at the University of Stuttgart figured out how
to print tiny 3D lenses—120 millionths of a meter in diameter, or about the size of
a grain of sand.
What’s Next
Researchers believe that this technology will dramatically change our approach
to medical imaging. Rather than relying on our current endoscopic technology,
which is bulky and invasive, a patient could simply inhale smart dust. Beyond
medicine, trillions of smart dust particles could be released in the wind to meas-
ure air quality or take photos. But we must also consider other use cases: would
you know if you’d inhaled rouge smart dust on a windy day? In the farther-future,
could this technology be used to track us surreptitiously?
Watchlist
University of Stuttgart; University of California Berkeley; Stanford University; Uni-
versity of Washington; Purdue University; USC Robotics Research Lab; DARPA.
The University of Stuttgart’s multi-lens
system next to a single doublet lens.
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Soft Robotics
First year on the list
Key Insight
This is a relatively new field of robotics research. Imagine robots that are squishy
and can operate in unpredictable environments.
Examples
Scientists at the BioRobotics Institute at the Scuola Superiore Sant’Anna in Pisa,
Italy, have been investigating soft robotics. Recently, they created a robot octo-
pus, capable of replicating the animals’ agile motions. In order to replicate the bi-
ology of an octopus, they built computer models using exact measurements and
then experimented with a number of soft actuators to develop artificial muscles.
Researchers at Worcester Polytechnic Institute have been working on a robotic
snake.
What’s Next
Soft robots mean that someday soon, we will be able to enter and explore envi-
ronments previously unreachable by conventional methods: deep ocean waters,
the terrain of Mars, and perhaps even the gushing rivers of blood inside our own
bodies. This is just the beginning. Don’t expect to play with a squishy robot drone
at your next pool party. However those who follow drones, robotics, infrastruc-
ture and defense should have new prototypes to look at in 2017.
Watchlist
Scuola Superiore Sant’Anna; Worcester Polytechnic Institute; MIT Media Lab;
Johns Hopkins Applied Physics Laboratory; DARPA.
This octobot is an example of a soft robot.
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Robot Companions
First year on the list
Key Insight
After many years being developed inside of R&D labs, we are now starting to see
the first generation of robot companions emerge.
Examples
Many countries, including Japan, Italy, and Germany, will soon face rapid demo-
graphic shifts. In Japan, one in four people are now age sixty-five or older—there
aren’t enough people working to support both retirees and children. Science and
technology will eventually stand in for the lack of people: robots will assist with
everything from elder care, to medical assistance, to everyday companionship.
Unsurprisingly, this first generation of companion robots is being built in Japan:
Toyota is launching its Kirobo Mini companion, while Sharp has already debuted
its RoboHon. When SoftBank’s Pepper went on sale at the very end of 2015, the
company sold all 1,000 units in under a minute.
What’s Next
Within a generation, there will not be enough people to make Japanese society
work as it does today—but Japan isn’t alone in its demographic shift. Many in-
dustrialized countries, including the U.S., could suffer the same fate. For those
concerned that AI and robots will take away jobs, that doesn’t necessarily take
into consideration that we already didn’t have enough citizens ready to do the
work on their own.
Anyone interested in the future of robotics would be wise to look not to Silicon
Valley, but instead to universities and R&D labs in Japan, where extensive re-
search on the next generation of robot companions is already underway. Out
of necessity, robots—mechanical systems, artificial intelligence, and automated
services—will act as productive, emotionally-intelligent stand-ins for a younger
generation that was simply too small in numbers.
Watchlist
SoftBank; Sharp; Toyota; MIT Media Lab; Buddy; Nanyang Technological Universi-
ty; Sony; Tokyo University; Johns Hopkins Applied Physics Laboratory;
Pepper is a robot companion from Japan’s
SoftBank.
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Collaborative Robots
First year on the list
Key Insight
Robots that communicate in real-time and cooperate on projects make a strong
team.
Examples
Teams of robots are now capable of working together, and they’re efficient, since
not every single robot has to excel at every task. Researchers at Carnegie Mellon
University have built collaborative robots that are designed to work together.
In their recent work, a robot named Baxter is stationed at a table working on a
project. Once completed, another robot on the team—CoBot—picks up the item
and hands it to a human. Teams of collaborative robots can communicate to each
other, on their own, about when to wait, when to move, to carry out an activity,
or even to ask what to do.
What’s Next
Use your imagination and you can probably see what’s on the horizon. Collabora-
tive robots will play a key role in automating the tasks performed in warehouses,
manufacturing plants, logistics and delivery services. They will also run opera-
tions in conflict zones.
Watchlist
Carnegie Mellon University; MIT’s Interactive Robotics Group; DARPA; Auton-
omous Solutions; Energid Technologies; Boston Dynamics; Alphabet (Google);
Amazon; ABB Robotics; Aethon Inc.; FANUC Robotics; EPSON Robotics; Seegrid;
SoftBank Robotics Corporation; Toyota; ULC Robotics; VEX Robotics; Yamaha;
University of Tokyo; Johns Hopkins Applied Physics Laboratory
Self-assembling swarming microbots
developed at MIT.
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Ethical Manufacturing
First year on the list
Key Insight
Could robots bring the end of forced labor and lead a new era of ethical manu-
facturing?
Examples
Recent advancements in robotics are proving that soon, humans may no longer
be required to perform labor-intensive manufacturing jobs. While this certainly
means that people will be out of certain kinds of work, it does imply the end of
bonded, forced and child labor—not to mention slavery—which unfortunately has
become commonplace in places like China, the Philippines and Bangladesh. In
October 2016, a Canadian court allowed a lawsuit brought by Eritrean workers
against Nevsun Resources, a mining company: it was the first time in history that
a tort claim for modern slavery went ahead in Canada. Meanwhile, in September,
the Associated Press published a searing account of foreign fishing workers, con-
fined and forced to work on U.S. fishing boats. The AP’s investigation revealed a
disturbing present-day reality: men are forced to use buckets instead of toilets,
suffer sores from bed bugs and don’t have enough food to sustain them.
What’s Next
Look for a number of new advancements in robotics that further reduce the need
for human labor, such as the Sewbo, which is similar to a traditional sewing ma-
chine but also has a smart robotic arm, allowing it to both sew and assemble
an item of clothing. Elon Musk’s giant Gigafactory will soon employ hundreds
of robotic arms and “automated guided vehicles,” essentially mobile robots that
transport items from one area to another. There could be models available for the
future sooner than you might think. Manufacturing regions, like Ohio and Michi-
gan, could find their workforces disrupted.
Watchlist
National Association of Manufacturers; Alliance For American Manufacturing; Te-
sla; Sewbo; Carnegie Mellon University; MIT’s Interactive Robotics Group; Alpha-
bet (Google); Amazon; ABB Robotics; Aethon Inc.; ULC Robotics.
Factories staffed with robots could mean
the end of harsh working conditions.
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Universal Basic Income
First year on the list
Key Insight
An unconditional, guaranteed income for everyone within a country—a concept
made popular in the 1960s—is being debated once again in the wake of automa-
tion, advanced robotics and artificial intelligence.
Examples
The origins of universal basic income (UBI) date back to Thomas Paine who, in a
1797 essay, theorized that governments should pay citizens £15 a year. There have
been a number of universal basic income (UBI) experiments. In the 1970s, the Ca-
nadian province of Manitoba began sending UBI checks to all of its residents. An
economist analyzed the program, which only lasted a few years, and discovered
that life improved significantly: kids stayed in school, fewer people wound up in
the hospital, and many reported that their quality of life overall was better. UBI
has had its share of supporters throughout history, from Dr. Martin Luther King,
Jr., to the Nixon Administration, to far-right economist Milton Friedman.
What’s Next
Finland has just launched a two-year UBI pilot study. A randomly-selected group
of 2,000 - 3,000 citizens will begin receiving a monthly stipend of 560 Euros
(or about $600 dollars.) Y-Combinator, the storied Silicon Valley incubator of
hot tech startups such as Reddit, Wufoo and Rapportive, is sponsoring a UBI
program in Oakland, California. In June 2016, Swiss voters rejected a UBI initi-
ative guaranteeing citizens around $2,500 a month. Critics of UBI argue that if
citizens are simply handed a wad of cash each month, they won’t spend it on
preventative health or nutritious food, and that they will be less inclined to work.
Proponents say that our fast-approaching robotics era will render many out of
work anyway—and that a UBI will help insure our country against an imminent
implosion in the job market.
Watchlist
Stanford Center for Philanthropy and Civil Society; White House Council of Eco-
nomic Advisors; American Enterprise Institute; Oxford University’s Carl Benedikt
Frey and Michael Osborne; Finland; Netherlands; Norway; Sweden; Organization
for Economic Co-operation and Development.
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Artificial Intelligence in Hiring
First year on the list
Key Insight
Researchers are finding that the usual data sources—test scores, GPAs and the
like—aren’t reliable indicators of talent.
Examples
Artificial Intelligence is being used to assess personal attributes like empathy,
thoughtfulness, engagement, motivation and drive. Coupled with social media
screening—looking at an applicants previous posts on Facebook, Twitter, Insta-
gram, blogs and elsewhere online—emerging systems consider both personality
and behavior when evaluating possible new hires. Companies like RoundPegg
and Interviewed, which collect data and perform assessments on candidates, are
being used by high-profile clients including ExxonMobil, Xerox and Razorfish.
What’s Next
One likely future benefit of AI-assisted hiring is that machines—theoretically, at
least—can be more objective than people. However, as we start to rely more on
these systems, we must also redouble our efforts to ensure that the algorithms
and data used aren’t also subject to bias. After all, these systems were originally
envisioned, architected and programmed by humans.
Watchlist
Mya A.I.; Recruit Strategic Partners; HackerRank; LinkedIn; IBM Watson; Alphabet
(Google); Facebook; University of Oxford’s Martin Programme on the Impacts of
Future Technology; Xerox; Razorfish; ExxonMobil; RoundPegg; Interviewed.
AI-assisted hiring could reduce bias and
diversity workforces.
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Productivity Bots
Second year on the list
Key Insight
Productivity Bots will help assist workers and students perform tasks more effi-
ciently.
Examples
Slack is a popular hybrid instant message/ email system that lets you sort and
tag, search, and choose to broadcast messages to your whole team or just to a
few members. The average Slack user spends around 140 minutes (nearly two
and a half hours) per weekday on the service, and much of the reason is because
Slack bots can automate myriad productivity services on their behalf. For exam-
ple: you can automatically retrieve and read news from multiple sources, set work
and break timers, plan meetings and events for a few staff without spamming an
entire team and collaborate on Google docs. Productivity bots are now offered in
a number of different office apps, including Yammer, HipChat and Skype.
What’s Next
Slack’s success has inspired numerous startups and competitors, including Mi-
crosoft, which introduced its own product late 2016. We should expect deeper
integrations across all platforms and services in 2017, which should promise even
more increased productivity. Watch for more automated features within Trello,
Dropbox, Google Drive, Heroku, Mailchimp and Zendesk, to name a few.
Watchlist
Slack; Alphabet (Google); Yammer; HipChat; Skype; Trello; Dropbox; IFTTT; Hero-
ku; Y Comibinator; Mailchimp; Zendesk; Microsoft.
Slack is a popular platform that now
includes bot assistants.
TREND 029
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