The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

1. Artificial Intelligence
Facts and Myths
Joaquim Jorge

2. Professor at IST
UNESCO Chair
VR & AI
Healthcare

3. Professor at IST
UNESCO Chair
VR & AI
Healthcare
AI-generated
image

5. Seven Everyday applications of AI
Facebook
Twitter
Instagram
Google
Maps
Waze
Google
Search
Bing
Siri
Alexa
Google
Assistant
Netflix
Amazon
Spotify
Credit Risk
Portfolio
Management
Medical
Diagnosis

6. Motivation – AI in Search of Intelligence

7. Early days of AI
[Dartmouth 1956]
AI describes efforts to program computers to solve
problems that require intelligent human
behavior/attention.

8. Objective: "Simulate human intelligence through
machines"

9. The 80s: Machine Learning

10. 1990s: Advances in Algorithms and Computational Power

11. Data Mining

12. 2000s:
Big Data and
Deep Learning

13. 2010: AI becomes Mainstream

14. AlphaGo: the triumph of Reinforcement Learning

15. Advancements in Autonomous Vehicles: Tesla,
Waygo

16. OpenAI - Generative
AI (GPT)
Natural language, text
generation, translation
and question
answering.

17. 2020s: AI in
Everyday Life and
Ethical
Considerations
Widespread
Application:
Healthcare, Finance,
Customer Service, and
Entertainment…

18. AI Ethics and Regulation:
ethical use of AI,
data privacy,
bias in AI algorithms and the
Need for regulation

19. AI in Healthcare
During COVID-19:
from drug
discovery 
vaccine
development 
analyzing the
spread of the
virus.

20. AI: toolbox
and
techniques
Supervised
Learning
(classifies things)
Unsupervised
learning
Generative AI
Reinforcement
learning

21. Supervised Learning
Systems that Learn with Examples
Input A   Classification B

22. Supervised Learning
Input (A) Classification (B) Application
Mail Spam (0/1) Filter Spam
Ad, User Profile. Clicks? (0/1) Online Advert
Image, Radar Pos. Cars on Road Self Driv Cars
RX Image Diagnostics Healthcare
Phone Image Defects? Quality Control
Audio Record Text Transcription Speech Recognition
Restaurant Review Sentiment (Pos/Neg) TripAdvisor / Yelp

23. Generative AI2010  2020
Small-Scale AI Systems
Large-Scale AI Systems
(Transformers)

24. Generate Text using Large Language Models
(LLMs)
I love to eat _______
prompt

25. Generate Text using LLMs
I love to eat _______
prompt Steak and french fries

26. Generate Text using LLMs
I love to eat _______
prompt Steak and french fries
Homemade food

27. Generate Text using LLMs
I love to eat _______
prompt Steak and french fries
Homemade food
Out with friends

28. Generate Text with Large Language Models
(LLMs)
I love to eat _______
prompt Steak and french fries
Homemade food
out with friends
IA output

29. How LLMs Work
Use pretrained model (A B): predict the next word
Input (A) Output (B)
My favorite food is steak

30. How LLMs Work
Use pretrained model (A B): predict the next word
Input (A) Output (B)
My favorite food is steak
My favorite food is steak and

31. How LLMs Work
Use pretrained model (A B): predict the next word
Input (A) Output (B)
My favorite food is steak
My favorite food is steak
My favorite food is steak and
and
french

32. How LLMs Work
Use pretrained model (A B): predict the next word
Input (A) Output (B)
My favorite food is steak
My favorite food is steak
My favorite food is steak and
My favorite food is steak and french
and
french

33. How LLMs Work
Use pretrained model (A B): predict the next word
Input (A) Output (B)
My favorite food is steak
My favorite food is steak
My favorite food is steak and
My favorite food is steak and french
and
french
fries

34. How LLMs Work
Use pretrained model (A B): predict the next word
Input (A) Output (B)
My favorite food is steak
My favorite food is steak
My favorite food is steak and
My favorite food is steak and french
My favorite food is steak and french fries
and
french
fries

35. How LLMs Work
Use pretrained model (A B): predict the next word
Input (A) Output (B)
My favorite food is steak
My favorite food is steak
My favorite food is steak and
My favorite food is steak and french
My favorite food is steak and french fries
and
french
fries
When we train a Large AI System with Billions or Trillions of
words, we get a Large Language Model (e.g. GPT)

36. Large Language Models
There are MANY
ChatGPT (Open AI)
Bard (Google)
Bing (Microsoft)
LLAMA2
---

37. ChatGPT
GPT: Generative
Pretrained Transformer

38. User: what is on the image?
ChatGPT:
The image shows a simple,
modern wall clock with a
white face and black
markings.
The numbers 1 through 12 are displayed in a
traditional layout. The clock hands are positioned
such that the hour hand is
pointing to the number
10 and the minute hand
is pointing to the number
2, which indicates a time
of 10 o'clock.
The background is plain and light-colored, providing
a stark contrast to the black elements of the clock,
which makes the image look clean and minimalistic.
ChatGPT can’t tell time! Why?

39. BBC 24/Feb/24
Chatbot
offered 100%
discount for
bereavement
travel…
Which the
airline did not
feature!

40. Diffusion: The
Art of AI in
Generating
Images
Artificial intelligence model that
generates detailed images from
textual descriptions.
A deep learning process called
"diffusion," which learns to turn
noise into structured images.

41. How does it work?
1.Training: The model is fed with a large number of
images and corresponding descriptions.
2.Diffusion: Learns to add noise to images, until
only noise is visible, losing the original structure.
3.Reversal: The model then learns to reverse this
process by creating an image from noise, based
on a textual description.

42. How does it work?
1.Training: The model is fed with a large number of
images and corresponding descriptions.
2.Diffusion: Learns to add noise to images, until
only noise is visible, losing the original structure.
3.Reversal: The model then learns to reverse this
process by creating an image from noise, based
on a textual description.

43. Add Noise to the Image until it becomes uncharacteristic

45. From NOISE reverse diffusion recovers a cat OR a dog
image.

46. Recover
Reverse diffusion works by subtracting
the predicted noise from the image
successively.

47. Stable
Diffusion:
OpenAI

48. Chat.open
ai.com
Prompt: show a utopic futuristic city
powered by advanced AI Tools

49. a futuristic mansion by a lake in the style of Oscar Nyemeyer

50. Show me a lady playing the violin. Openai:

51. Some Goals are
Elusive
Self-Driving Cars

52. Training Self-
Driving Cars is
Difficult
Too Many Special Cases

53. FastCompany
12/Feb/2024
https://www.fastcompany.com/91027651/waymo-self-driving-car-fire-
vandalism-sf-crowd

54. Using VR to Train Self Driving Cars

55. Biases

56. The Risks of Artificial Intelligence: COMPAS
Correctional Offender Management Profiling for Alternative Sanctions
(

57. Can we Trust this?
(analysis by ProPublica.org)
Prior offenses:
2 armed robberies
1 attempted armed
robbery
Prior offenses:
4 juvenile
misdemeanors

58. Can we Trust this?
(analysis by ProPublica.org)
Prior offenses:
2 armed robberies
1 attempted armed
robbery
Prior offenses:
4 juvenile
misdemeanors

59. Can we Trust this?
(analysis by ProPublica.org)
Prior offenses:
2 armed robberies
1 attempted armed
robbery
Prior offenses:
4 juvenile
misdemeanors
Subsequent offenses:
1 grand theft
Subsequent offenses:
None

60. Can we Trust this?
(analysis by ProPublica.org)
Subsequent offenses:
1 grand theft
Subsequent offenses:
None
Subsequent offenses:
None
Subsequent offenses:
3 drug possessions

61. What can a machine tell you about sexual orientation?
Wang, Yilun, and Michal Kosinski. "Deep neural networks are more accurate than humans at detecting sexual
orientation from facial images." Journal of personality and social psychology 114.2 (2018): 246.

62. Wang, Yilun, and Michal Kosinski. "Deep neural networks are more accurate than humans at detecting sexual
orientation from facial images." Journal of personality and social psychology 114.2 (2018): 246.

65. The need (rush?)
for AI regulation

67. GDPR (2018) EU GDPR
People have a right to explanation!
“Companies should commit to ensuring
systems that could fall under GDPR,
including AI, will be compliant. The threat
of sizeable fines of €20 million or 4% of
global turnover provides a sharp incentive.
Article 22 of GDPR empowers individuals
with the right to demand an explanation
of how an AI system made a decision that
affects them. ”
- European Commission

68. Initiatives/Issues
G7: Hiroshima Process Declaration on
Artificial Intelligence
UK: Bletchley Declaration on the
Safety of Artificial Intelligence
USA: Executive Order on the Safety
and Reliability of Artificial Intelligence.
EU: Artificial Intelligence Act
Canada and Japan: WIP

70. Dystopia vs Utopia

77. Why the Fracas?
Three Parties:
1. Extinction-Level Event? (Current Board)
2. Develop + Make AI the next Industrial
Revolution (Altman+Brockman)
3. Corporate Greed / Petty Arguments?

78. Adam D’Angelo, the chief executive of Quora; Lawrence
Summers, the former Treasury secretary; and Bret Taylo
former executive at Facebook and Salesforce.Credit...

79. Luddites vs Progress
AI Pioneer Admits There is a
Small Chance AI Leads to
Humanity's Extinction
Open-source AI models will
soon become unbeatable.

80. Should AI’s future lay
with Corporations?

82. AI is a set of tools
Skynet is a good Hollywood argument... But that's it
Yet, armies and autonomous killing machines...
Regulations should not be an obstacle to progress
The danger is not in AI, but in bad AI applications
--- Corporations
--- Ethics QUESTIONS?
jorgej@tecnico.ulisboa.pt jorgej@acm.org