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Artificial intelligence in practice- part-1

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Summary is made in 5 parts-
This is Part -1
Cyber-solutions to real-world business problems Artificial Intelligence in Practice is a fascinating look into how companies use AI and machine learning to solve problems. Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe.

• The rapidly evolving field of artificial intelligence has expanded beyond research labs and computer science departments and made its way into the mainstream business environment.
• Artificial intelligence and machine learning are cited as the most important modern business trends to drive success.
• It is used in areas ranging from banking and finance to social media and marketing.
• This technology continues to provide innovative solutions to businesses of all sizes, sectors and industries.
• This engaging and topical book explores a wide range of cases illustrating how businesses use AI to boost performance, drive efficiency, analyse market preferences and many others.
• This detailed examination provides an overview of each company, describes the specific problem and explains how AI facilitates resolution.
• Each case study provides a comprehensive overview, including some technical details as well as key learning summaries:
o Understand how specific business problems are addressed by innovative machine learning methods Explore how current artificial intelligence applications improve performance and increase efficiency in various situations
o Expand your knowledge of recent AI advancements in technology
o Gain insight on the future of AI and its increasing role in business and industry
o Artificial Intelligence in Practice: How 50 Successful Companies Used Artificial Intelligence to Solve Problems is an insightful and informative exploration of the trans-formative power of technology in 21st century commerce

Veröffentlicht in: Technologie
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Artificial intelligence in practice- part-1

  1. 1. Some Impressionistic Take away from the Book of Bernard Marr & Matt Ward Artificial Intelligence in Practice ( Part – 1) ( How 50 Successful companies used AI & Machine Learning to Solve problems) Ramki ramaddster@gmail.com
  2. 2. The Summary of this book is made in 4 parts due to large coverage of the book . This is Part – 1
  3. 3. About the Authors BERNARD MARR is the founder and CEO of Bernard Marr & Co and an internationally best-selling business author, futurist, keynote speaker and strategic advisor to companies and governments. He is one of the world's most highly respected voices and a renowned expert when it comes to topics such as artificial intelligence and big data. Marr advises many of the world's best-known organizations on strategy, digital transformation and business performance. He is the author of Big Data in Practice: How 45 Successful Companies used Big Data Analytics to Deliver Extraordinary Results and Big Data: Using SMART Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance, both published with Wiley. MATT WARD is the research lead for Bernard Marr & Co. Matt has a background in investigative journalism and spent the last few years working closely with Bernard Marr on the latest technology topics. Matt is an expert and experienced writer in the field of business technology and artificial intelligence, where he has worked with companies such as IBM, Intel, Citibank and NASA.
  4. 4.  Artificial Intelligence in Practice is a fascinating look into how companies use AI and machine learning to solve problems. Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe.  The rapidly evolving field of artificial intelligence has expanded beyond research labs and computer science departments and made its way into the mainstream business environment.  Artificial intelligence and machine learning are cited as the most important modern business trends to drive success. It is used in areas ranging from banking and finance to social media and marketing.  This technology continues to provide innovative solutions to businesses of all sizes, sectors and industries. This engaging and topical book explores a wide range of cases illustrating how businesses use AI to boost performance, drive efficiency, analyze market preferences and many others.  Best-selling author and renowned AI expert Bernard Marr reveals how machine learning technology is transforming the way companies conduct business. Prelude
  5. 5.  This detailed examination provides an overview of each company, describes the specific problem and explains how AI facilitates resolution. Each case study provides a comprehensive overview, including some technical details as well as key learning summaries:  Understand how specific business problems are addressed by innovative machine learning methods  Explore how current artificial intelligence applications improve performance and increase efficiency in various situations  Expand your knowledge of recent AI advancements in technology  Gain insight on the future of AI and its increasing role in business and industry  Artificial Intelligence in Practice: How 50 Successful Companies Used Artificial Intelligence to Solve Problems is an insightful and informative exploration of the transformative power of technology in 21st century commerce. Prelude
  6. 6. Some understanding of Technology A brief guide to some of the most common and some of the latest terminology being used when discussing cutting-edge ( It is important to read & understand the next few slides which gives the flavor of technology)
  7. 7. Technology AlphaGo  It is an AI which became the first computer program to beat a professional player at the board game Go.  Game playing has often been a field in which computer scientists have sought to prove that machines can outperform humans.  However earlier applications such as chess computers are not considered “true” AI today because they don’t really learn – they simply rely on brute force to consider every permutation of a structured dataset (all of the moves possible in a game of chess.)  AlphaGo uses deep learning to refine its algorithms based on the results of historical games, and from running simulated games against itself.  This means it can be considered to be learning and comes closer to what we consider “true” (human-like) intelligence..
  8. 8. Technology Artificial Intelligence  This is the original, catch-all term for “machines which can think”, first conceived by philosophers and storytellers in ancient times.  Technological advancement has brought them closer to reality and also redefined what we consider “intelligence” when it comes to machines.  Rather than walking, talking automatons, today’s AI’s are more likely to take the form of discreet computer code dedicated to handing a particular task in an intelligent way. Big Data  The “fuel” of AI. Knowledge unlocks understanding and wisdom. AI platforms leverage the huge volume, variety and velocity of information available in today’s digitized world to learn faster and make increasingly well-informed recommendations and decisions.
  9. 9. Technology Cognitive computing  Cognitive computing is the process by which computers think and learn, as well as the development of these processes.  These give rise to artificial intelligence, machine learning, deep learning and all technologies which involve simulating human thought and decision- making.  In practice, the term is often used synonymously with modern, application- focused AI. Deep learning  This is a subfield of machine learning (see below) which uses many layers of artificial neural networks to handle processing of data in increasingly complex ways.  This means that classification (sorting into sets) can be done more precisely and pattern recognition is more sophisticated.  These are two of the most useful fundamental tasks that AI carries out today, meaning Deep Learning is a cutting-edge and very active field of research. Layers of neural networks stacked on top of each other to be used in deep learning are known as deep neural networks.
  10. 10. Technology Generalized AI  Generalized AI is a concept – widely thought to still be some way off – of a machine which can carry out any job it is told to do.  An android such as those seen in Star Trek or Blade Runner – who could be given a mop and told to clean a floor, or given a weapon and told to defend against attacking Klingons, would be an archetypal example.  While advances such as machine learning and deep neural networks point towards it being something that we will achieve in the future, currently the majority of AI research focuses on creating applied or specialized AIs (see below). Image recognition  Teaching machines to recognize and classify objects visually – by inputting visual data – is an important foundation of AI because visual information is so valuable to humans, and AI seeks to emulate human thought processes.  Either using cameras or raw image data such as picture or video files, computers are being taught to classify images according to what they depict, using pattern recognition to identify key features.  Advances in machine learning have greatly improved the ability of computers to do this, as they have become able to teach themselves from vast image databases, increasing their probability of outputting accurate results.
  11. 11. Technology Machine learning  Often used synonymously with AI these days, but there is an important distinction.  While AI applies to the entire concept of “thinking” machines from sci-fi robots to self-learning computer code being developed by business and academia today, Machine Learning (ML) is the practical implementation that is generating the biggest breakthroughs in the real world.  At its most basic it is technology designed around the principle that rather than have to teach machines to carry out every task, we should just be able to feed them data and allow them to work out the rules by themselves.  This is done through a process of simulated trial-and-error where machines crunch datasets through algorithms which are capable of adapting, based on what they learn from the data, in order to more efficiently process subsequent data.
  12. 12. Technology Natural Language Processing Natural Language Processing (NLP) technology is concerned with building machines which can understand human speech patterns. Because spoken communication comes far more naturally to us than writing computer code, it makes sense that machines, with their superior processing powers, learn to adapt to us by understanding and speaking our language, rather than us adapt to them! Due to the huge variance in human languages and the way they are used, machine learning is employed to pick out patterns, tonal variances and colloquial or non-literal use of language and interpret what we are trying to express. ML-derived NLP can be seen or heard in action in virtual assistants such as Apple’s Siri, Microsoft’s Cortana and Amazon’s Alexa.
  13. 13. Technology Neural networks  Algorithmic models structured as hierarchical networks of nodes which all pass information (data) between themselves, extrapolating more and more precise meaning and value from it as it passes along the chain.  Their complex, interconnected nature allows data to be processed far more comprehensively than traditional, linear algorithms allow, enabling them more insightful output from big, messy and unstructured datasets.  The more precise and correct term, artificial neural networks (ANNs), is often simply shortened to “neural network”, the term for the system of biological neurons in the animal brain which machine learning attempts to emulate. Specialized AI  The form of AI becoming commonplace in business, scientific research and our everyday lives – usually in the form of applications designed to carry out one specific task in an increasingly efficient way.  This could be anything from giving you tips on improving your fitness by monitoring exercise patterns, to predicting when machinery will break down on a production line, to spotting genetic indicators of illness in a human gene sequence.
  14. 14. Technology Supervised Learning  Supervised learning is a term used for machine learning processes where the output of the algorithm is checked, and the results fed back to the computer to enable it to know how accurate they are.  It can then use this knowledge to increase the probability that it will return with an acceptably accurate result next time around.  As a simple example imagine an AI fraud detection algorithm designed to flag up suspicious transactions by a bank.  In unsupervised learning, data is matched against previous outcomes to look for patterns in financial transactions, such as their point of origin, size or time of day they take place, which may indicate they are suspicious.  As new suspicious transactions are identified, the algorithm adapts to “learn” that other features of the newly-identified suspicious transactions may also be an indicator of fraud.  In this way, a supervised learning system can learn to identify fraud from characteristics that were not highlighted in its initial training data as indicators of fraud.
  15. 15. Technology Unsupervised Learning  Unsupervised learning is the flip-side of the coin from supervised learning and involves giving computers the ability to increasingly accurately recognize and classify data without needing a human, or initial training data, to check it if it is right or wrong.  In unsupervised learning the algorithm only ever sees the input data, and it classifies it according to patterns that it recognizes from other input data that it has previously processed.  This is generally done through a statistical process known as clustering, where objects (financial transactions to carry on ) are grouped together according to qualities and attributes that they share.  This approach to the problem of data classification has tremendous potential for developing machines which more closely emulate our own thought and decision-making processes, but also requires huge amounts of processing power compared to supervised learning.
  16. 16. Part – 1 Artificial Intelligence Trailblazers
  17. 17. Using AI to Power the Retail & Business to Business services of the Future
  18. 18. Alibaba – Using AI to Power Retail & Business  Alibaba- Chinese company the world’s largest e-commerce marketplace with US $ 248 billion in transactions ( more than eBay & Amazon combined) invests in AI and ML , you realize quickly that the list what is doesn’t impact is much shorter.  Since 1999 when the company was launched in founder Jack Ma’s apartment , Alibaba’s core business remains to sell good, but its influence & operations have expanded to make it one of the Most valuable Tech companies in the world.  One of the reasons Alibaba and other Chinese tech companies such as Tencent and Baidu, collectively known as BAT, are making extraordinary gains in artificial intelligence is the support, investment and commitment of the Chinese government to become the dominant AI player in the world.  With plans to build a US $ 1 Trillion AI Industry by 2030, China is on a path to overtake the United States as the world’s leader in technology.  Along with an enormous population that generates critical data to inform AI algorithms and help make them better as well as a society who is keen on technological change and not as pushy on regulation, China is fertile ground to develop AI applications, and Alibaba is moving full throttle ahead.
  19. 19. Alibaba – Using AI to Power Retail & Business Along with an enormous population that generates critical data to inform AI algorithms & help make them better as well as a society who is keen on technological change & not as pushy on regulation, China is fertile ground to develop AI applications, and Alibaba is moving full throttle ahead. Alibaba invested in 7 research labs that will focus on AI, ML, network security , natural language processing & more . Singles’ day leverages AI  Singles’ Day, once an anti-Valentine’s Day to celebrate being single has now transformed - thanks to Alibaba and its founder Jack Ma - into the largest e-commerce event in the world.  To handle the incredible volume, more than Black Friday and Cyber Monday combined in one day, Alibaba used automation, robots, AI and machine learning throughout the process from guiding shoppers to the products to delivery.  Here are few AI applications that were used:
  20. 20. Alibaba – Using AI to Power Retail & Business  Tmall Smart Selection: This AI-powered algorithm backed by deep learning and natural language processing helps recommend products to shoppers and then communicates to the retailers to increase inventory to keep up with the demand.  Dian Xiaomi: This AI-powered chatbot can understand more than 90 percent of customer’s queries according to Alibaba and serves more than 3.5 million users a day. The latest version of the chatbot can understand a customers’ emotion and can prioritize and alert human customer service agents to intervene.  Robots to pack and drones to deliver. More than 200 robots in automated warehouses can process 1 million shipments each day.  Once the robots received the orders on Singles’ Day, they packaged and shipped the goods, and, in some cases, their efficient allowed same-day shipment. Alibaba also used drones for some deliveries. These technological advances that helped power Singles’ Day last year resulted in $25 billion in sales, up from $17.8 billion in sales for Single’s Day 2016 and more than the GDP of Iceland and nearly $20 billion more than Cyber Monday in the U.S.
  21. 21. Turning physical shops into “ Smart Stores”  In addition to turning some physical shops into “smart stores” for  Singles’ Day, Alibaba is introducing “digitalization in a box” under its Tmall brand to smaller retailers.  Alibaba has redone about 1 million mom-and-pop shops and 100 superstores across China where retailers get all of their goods through Alibaba's platform and use its Alipay app.  These efforts allow Alibaba to create self-contained, AI-infused retail models that combine e-commerce and brick-and-mortar into a mixed experience.  Alibaba gives retailers a relatively low cost to join and provides the operating systems for stores to go digital.  According to the McKinsey Global Institute, 42% of global ecommerce transactions took place in China, more than Japan, France, Germany, the UK and the U.S. combined.  Many believe that it will only be those retailers who embrace digitization who will survive, and Alibaba is providing the structure to make it possible.
  22. 22. Investment in SenseTime, DAMO Academt, & Research  As China’s largest R&D spender, Alibaba is the largest single investor in SenseTime; an AI start-up known for its facial- recognition technology, that launched an AI lab in Hong Kong.  The lab hopes to "advance the frontier of AI" by supporting other startups as they commercialize their AI tech and develop ideas and products. Researchers and other industry participants can also collaborate with start-ups in the lab.  Alibaba plans to spend $15 billion over three years on DAMO (discovery, adventure, momentum, and outlook) Academy. While AI is the most significant research project for DAMO, it will also have research groups for emerging technologies such as blockchain, computer security, quantum computing and fintech.
  23. 23. City Brain: AI Control For Cities  With its City Brain project, Alibaba hopes to help cities run their operations by artificial intelligence.  Already improving traffic issues, City Brain uses a cloud-based system where data about a city and everyone in it is stored and processed through AI algorithms.  The project’s success in reducing traffic jams by 15% was achieved by monitoring every vehicle in the city. Already successful in Hangzhou, City Brain is going to Malaysia next.  In addition to these examples, Alibaba uses AI to optimize its supply chain, build products and drive personalized recommendations.  Ultimately, Alibaba aspires to be the tech giant to provide cloud-based AI which would make AI available to anyone with a computer and internet connection as well as an AI chip available through the cloud.
  24. 24.  Alibaba is China’s biggest investor in R & D , which has given it a strong start in the race to become the world leader in AI.  Its model for rolling out AT to million of customers & businesses is to deploy its services through the cloud. This cuts customer risk & infrastrucutre cost, while giving Alibaba access to valuable data about how its customer behaves.  By applying technology designed to drive sales at its retail portals to other problems in business and society, it identifies new use case for AI, within & outside its established business operations Key Challenges, Learning points & Takeaways
  25. 25. Maximising the Potential of AI
  26. 26.  Alphabet – US based Multinational  Core -Internet services , Technology & Life science  Main business  Google -Internet search giant google,  Verily ( life sciences)  Waymo ( Self –driving technology) ,  Nest ( smart home company) ,  Deep mind ( AI). Alphabet & Google
  27. 27.  Google- widely used search engine is peppered with AI  Text, Voice or image search – queries processed by smart, self-teaching systems.  Text & Voice search uses natural language processing- Every word entered relates to other word it is used with.  Google image search uses computer vision to recognize the content of image data.  Deep learning algorithms allow it to become increasingly good at recognizing and labelling different elements of the pictures.  AI processed the query and decides what the user wants & matches against its directory online content- web pages, images, videos or documents.  Simple google search involves – great deal of complex, fast AI calculations.  Google uses AI for Security, Gmail accounts, adwords ( businesses to pay for ads) How Alphabet uses AI ?
  28. 28. AI Personal Assistant  PA using Voice assistants – Google home, Amazon Alexa, Apple Siri  Limitations- they can respond well to basic, relatively short sentences & commands.  They are still infants compare to real human being, since they do not have enough data.  This is changing very fast – Google’s Duplex tech is leading this change. Language Translation  Machine learning has enables to teach a computer to speak one language – it can teach itself to speak any language.  Google’s language translation service uses deep learning to break languages down to their fundamental building block.  Google uses deep neutral networks to refine its algorithms.  Has built features – Pixel Bud headphones leading to real time translations directly through head phones Personal Assistants & Language Translation
  29. 29.  No Steering wheels or driver controls  Designed for new age urban motoring  Waymo’s service is aimed at the ride-sharing networks for smart cities Self-Driving Cars
  30. 30. Other segments Captioning Millions for Videos – Uses machine learning natural language algorithms for creating subtitles for the hard hearing . Diagnosing Disease – AI – deep learning technology is extensively used in the medical field. 3d Infrared scans for diagnosing eye conditions. Uses two deep learning algorithms – Compares what is normal and indicative problem and other one based on medical data . Google brain – Vast data generated by internet will unlock the usefulness through machine learning & deep learning. Deep mind- Neural net simulations of the brain which is trained to play games.
  31. 31. Key Challenges, Learning points & Takeaways Alphabet & Google clearly believe that AI is the Launchpad that will drive the next wave of transformative computer technology. As well as this, they believe the social impact of this next wave will be even greater than that of previous waves- including the development of the internet. Having more data than anyone else is a key advantage, which has enabled Alphabet to continue to develop first-in-class services from search to ad serving, language translation, speech processing , smart homes and autonomous driving.
  32. 32. Using Deep Learning to Drive Business Performance
  33. 33. How does the Amazon Alexa Works ?  “Alexa, what’s the weather going to be like today.”  It’s taken decades for scientists to understand natural human speech to the point where voice-activated interfaces such as Alexa, the natural language processing system by Amazon, are sufficiently enabled to be successfully accepted by consumers.  Alexa is who talks to users of Amazon’s Echo products including the Echo, Dot and Tap, as well as Amazon Fire TV and other third-party products.  Even since 2012, when the patent was filed for what would ultimately become Amazon’s artificial intelligence system Alexa, there has been tremendous growth in capabilities and the credit for that growth goes to machine learning.  For something that we do every day without giving it any thought, conversation between machines and humans is complex. So, how did Amazon and others in the space such as Google, Apple and Microsoft crack the code? Amazon
  34. 34.  Over 30 million smart speakers were sold globally last year, and this number is expected to grow to nearly 60 million this year.  While Amazon remains the industry leader in smart speakers selling about 20 million devices last year, others (especially Google) are also growing and starting to catch up.  There are nuances to each, but let’s look “under the hood” of an Echo to see how Alexa works.  While there is some capability contained in the Echo cylinder such as speakers, a microphone and a small computer that can awake the system and blink its lights to let you know it’s activated, its real capabilities occur once it sends whatever you have told Alexa to the cloud to be interpreted by Alexa Voice Services (AVS). ABC of Alexa
  35. 35.  So, when you ask Alexa, “What’s the weather going to be like today,” the device records your voice. Then that recording is sent over the Internet to Amazon’s Alexa Voice Services which parses the recording into commands it understands. Then, the system sends the relevant output back to your device.  When you ask about the weather, an audio file is sent back and Alexa tells you the weather forecast all without you having any idea there was any back and forth between systems.  What that of course means is that if you lose internet connection Alexa is no longer working.  The skills Echo has out of the box are impressive to most of us, but Amazon allows and encourages approved developers free access to Alexa Voice Services so they can create new Alexa skills to augment the system’s skill-set just as Apple did with the app store.  As a result of this openness, the list of skills that Alexa (currently over 30,000) can help with continues to grow rapidly. Users can, of course, purchase products from Amazon, but they can also order pizza from Domino’s, hail a ride from Uber or Lyft, control their light fixtures, make a payment through the Capital One skill, get wine pairings for dinner and so much more. ABC of Alexa
  36. 36.  Data and machine learning is the foundation of Alexa’s power, and it’s only getting stronger as its popularity and the amount of data it gathers increase.  Every time Alexa makes a mistake in interpreting your request, that data is used to make the system smarter the next time around. Machine learning is the reason for the rapid improvement in the capabilities of voice-activated user interface.  For example, Google speech was able to improve its error rate tremendously in a year; now it recognizes 19 out of 20 words it hears.  Understanding natural human speech is a gargantuan problem, and we now have the computing power at our disposal to make it better the more we use it Constantly Learning from Human Data
  37. 37.  Data and machine learning is the foundation of Alexa’s power, and it’s only getting stronger as its popularity and the amount of data it gathers increase.  Every time Alexa makes a mistake in interpreting your request, that data is used to make the system smarter the next time around. Machine learning is the reason for the rapid improvement in the capabilities of voice-activated user interface.  For example, Google speech was able to improve its error rate tremendously in a year; now it recognizes 19 out of 20 words it hears.  Understanding natural human speech is a gargantuan problem, and we now have the computing power at our disposal to make it better the more we use it Constantly Learning from Human Data
  38. 38.  Amazon- First one to harness the power of predictive analytics . AI promises more accurate prediction.  Amazon has built a Corporate strategy – Flywheel to encourage distribution of energy, momentum and data generated by AI through out the network of business operations.  Alex voice assistant and Amazon Prime Air drone delivery services through deep learning capabilities with engine algorithms.  Amazon leases its machine learning and deep learning knowledge /technology as a service through AWS platform . Key Challenges, Learning points & Takeaways
  39. 39. Apple Integrating AI into Products & Protecting User Privacy
  40. 40.  World’s largest information technology company by revenue.  Products – IPhone, IPad, Macs, Apple watch , Apple TC –software and services.  Valued at US $ 1 trillion in 2018.  AI strategy centers around its devices- pioneered of In-device AI technology leading to superior security and user engaging experiences. How does Apple use AI ?  Vision- Powerful handheld devices capable of running their own machine learning on datasets gathered via their own array of sensors.  This is against the vision of other tech companies – future dominated by cloud computing & low powered terminals.  Iphone-X Custom designed chip designed for carrying out the neural net calculations needed for deep learning.  This leads to faster processing of Face ID logins, features in the camera ( can add silly effects) , augmented reality and battery life. Apple
  41. 41. Smarter Apps  Significant credit goes to App Store.  App ecosystem keep customers coming back to Apple year after year because of integrated AI to 3rd party apps.  E.g. – App Homecourt – assist with refereeing amateur basketball games. Point the camera at a game & machine learning will tag the players in the game, logging when they pass & shoot, as well as recording their position on the court.  Done through computer vision technology running on the device. Natural Language processing  Through Siri – Introduced location signals into the training data, giving Siri access to localized datasets, including place names and small businesses.  Today Siri gives users more accurate data results when they search for information. Apple
  42. 42.  AI is very much at the heart of Apple’s strategy, which is to build it into the fabric of its devices & supporting services.  Apple is prioritizing user privacy over an ability to pump all data into the cloud to train algorithms on bigger data sets.  It is also promoting the use of its proprietary machine learning platform Create ML to make apps that will only work on its devices, creating exclusivity within its own app ecosphere. Key Challenges, Learning points & Takeaways
  43. 43. Machine Learning for Search Engines & Autonomous Cars
  44. 44.  At the beginning of 2017, Chinese tech company BAIDU, the largest provider of Chinese language internet search as well as other digital products and services, committed to emerging business sectors such as AI & Machine Learning .  Since China has over 800 million internet users, almost twice the U.S. population, Baidu’s data set is capable of fueling AI algorithms to make them even better.  With this focus on artificial intelligence, Baidu is exploring some very intriguing applications for artificial intelligence and machine learning including in their offices where facial recognition technology makes standard ID cards unnecessary and allows you to order tea from a vending machine.  They recruited top AI talent including one of the world’s most notable AI pioneers Li Qi, who was previously a Microsoft executive before he became Baidu’s COO in January 2017. & he stepped down in July 2018 for personal reasons.  Although he was only at Baidu for a short time, he helped chart a clear strategy for the company’s AI operations that will continue. Here are a few ways Baidu uses artificial intelligence and machine learning. BAIDU
  45. 45.  Baidu can leverage its expansive data set, its voice assistant called DuerOS has accumulated more conversation-based skill sets than Alexa, Siri or Cortana.  Partnering with other tech companies is one way Baidu hopes to accelerate innovation.  They have teamed up with more than 130 DuerOS partners, and the voice assistant is in more than 100 brands of appliances such as refrigerators, TVs, and speakers.  Since homes in India, Japan, Europe, and Brazil are more like homes in China, there may be better opportunities for DuerOS to globalize since Alexa, Cortana and Echo are optimized for American households.  At CES 2018, Baidu debuted its DuerOS-Powered smart screen called Little Fish VS1.  This technology can recognize and respond to individual faces. DuerOS is Baidu’s voice assistant
  46. 46.  Even though automated driving is currently against the law in China, Baidu is working on autonomous-driving technology.  Through the program called Apollo, Baidu’s Artificial intelligence technologies are made available to car makers for free as a brain for their cars. In exchange, Baidu gets access to the data to make their algorithms smarter.  It is hoped that Apollo will give any car manufacturer a fair shot at creating a viable product, just like Android did for smartphone makers.  Even with this accelerated approach, it is expected that fully autonomous cars won’t be in production until 2020-2021. Self Driving Cars
  47. 47.  Unlike its competitors, Baidu was steadfast in its commitment to desktops and missed the shift to mobile.  To survive, Baidu needed a new strategy and artificial intelligence technology provided just the platform to turn the business around.  That’s one of the reasons Baidu has committed so aggressively to AI investment.  Today, AI products and services are priorities to make them the core of the company’s future.  Now, they are partnering with Huawei to develop an open mobile AI platform to support the development of AI-powered smartphones and Qualcomm to optimize its DuerOS for IoT devices and smartphones using Qualcomm’s Snapdragon Mobile Platform Mobile partners to accelerate AI-powered devices
  48. 48.  Has developed a handheld device capable of generating deep learning translation between English, Mandarin, Chinese & Japanese.  Aimed at tourist market- assisting users to navigate their way around foreign cities – ordering food, using public transport etc.  Uses deep learning natural language processing algorithms.  It is on cloud. Real Time Translation
  49. 49.  The huge population base with more 50% online has helped the company to collect vast data of consumer profile & behaviours. This is used to streamline services, as well as sell to advertisers to allow them to more accurately target their campaigns.  Baidu offers AI services to businesses to enable them to develop & release their own AI-powered applications under its Baidu Brain framework.  Making strategic partnership with China’s largest smartphone manufacturer- Huawei to incorporate AI inside smart phones.  Baidu has China’s and possibly the world’s most advanced autonomous vehicle program with cars powered by its Apollo technology expected to bring a level 4 autonomy to the roads soon Key Challenges, Learning points & Takeaways
  50. 50. Using AI to improve Social Media Services
  51. 51.  Facebook builds its business by learning about its users and packaging their data for advertisers.  It then reinvests this money into offering us new, useful functionality – currently video and shopping - which it also uses to learn even more about us.  As the way it enables communication & conversation between people has proven to be hugely valuable to us, it has become a magnet for a huge amount of data about us – who we are, where we spend our time and what we like.  The challenges for Facebook’s data scientists who have to try to make sense of this is that much of this data is very messily unstructured.  2.2 billion people use the FB Social media platform. No. of comments – 510,000 & 293,000 status update / minute  With 1.2 billion people uploading 136,000 photos and updating their status 293,000 times per minute, until recently Facebook could only hope to draw value from a tiny fraction of its unstructured data – information which isn’t easily quantified and put into rows and tables for computer analysis. Facebook- Using AI
  52. 52.  Deep Learning is helping to play a part in changing that. Deep Learning techniques enables machines to learn to classify data by themselves.  A simple example is a deep learning image analysis tool which would learn to recognize images which contain cats, without specifically being told what a cat looks like.  By analyzing a large number of images, it can learn from the context of the image – what else is likely to be present in an image of a cat?  What text or metadata might suggest that an image contains a cat?.  That’s the basic principle of why Deep Learning (DL) is useful to Facebook, and as DL algorithms become more sophisticated they can increasingly be applied to more data that we share, from text to pictures to videos Facebook- Using AI- Deep Learning
  53. 53.  Facebook uses a DL application called DeepFace to teach it to recognize people in photos.  It says that its most advanced image recognition tool is more successful than humans in recognizing whether two different images are of the same person or not – with DeepFace scoring a 97% success rate compared to humans with 96%.  It was fed more than 4 million facial images to train it how to recognize individual facial elements.  Users can keep track of where photos of themselves are cropping up on the site.  FB confirms that its facial recognition algorithms have a success rate of 97.35% - very close to human-level accuracy. Facial Recognition
  54. 54.  Facebook uses a tool it developed itself called DeepText to extract meaning from words we post by learning to analyze them contextually.  Neural networks analyze the relationship between words to understand how their meaning changes depending on other words around them.  Because this is semi-unsupervised learning, the algorithms do not necessarily have reference data – for example a dictionary – explaining the meaning of every word. Instead, it learns for itself based on how words are used.  This means that it won’t be tripped up by variations in spelling, slang or idiosyncrasies of language use. In fact, Facebook say the technology is “language agnostic” – due to the way it assigns labels to words, it can easily switch between working across different human languages and apply what it has learned from one to another.  At present the tool is used to direct people towards products they may want to purchase based on conversations they are having. Understanding Text
  55. 55.  The vast amount of information we share about our lives on FB means that the company has access to more of our personal data than just about anyone else.  FB has leveraged this to build features that keep us coming back to the site to share more data as well as match us with advertisers whose products we might want to buy.  All these data- including our photos & text- has been invaluable to FB when it comes to training its facial recognition & natural language processing algorithms.  Unprecedented levels of insight into our lives means it can make increasingly accurate predictions about the users- from what users want to buy to whether we are thinking about killing ourselves. Key Challenges, Learning points & Takeaways
  56. 56. Cognitive Computing Helps Machines Debate with Humans
  57. 57.  The IBM algorithm Deep Blue beat chess champion Garry Kasparov in 1997.  It was 2011 when IBM’s Watson won the game show Jeopardy.  Shortly after, the IBM Research team was ready to go beyond game playing and began to brainstorm the next feat to challenge an artificial intelligence algorithm.  They decided to create an AI algorithm that would be trained on the art of debate.  Recently a small group of viewers got to see the IBM Project Debater’s public debut and its first two debates, when it went head-to-head with Israeli debaters Dan Zafrir and Noa Ovadia on increased investment in telemedicine and government subsidies for space exploration respectively.  From all accounts, IBM Project Debater was a formidable opponent and surprised many with its ability to make human-like arguments. It even swayed more audience members to its position on telemedicine that Zafrir did. How Does IBM use AI
  58. 58.  This project was the latest in IBM Research’s goal to build a system “that helps people make evidence-based decisions when answers aren’t black- and-white.”  Debate not only helps us convince others of our opinion, but it can help us understand and learn from other’s views.  By training machines in this way, it is hoped that in the future, AI algorithms will be able to help humans make important decisions regularly.  IBM Project Debater doesn’t just search its database of millions of articles from well-known newspapers and magazines—its corpus—but it has AI technology that can “work with humans to discover, reason and present new points of view.”  The IBM Research team was able to create an algorithm with the ability to:  Generate an opinion driven by data  Listen and understand an opponent, parsing out the critical bits of data from flowing narrative  Express the situation and arguments with concise language and complete human-like sentences How Does IBM use AI
  59. 59.  One of the impressive abilities IBM Project Debater exhibited was the combination of AI techniques it relied upon to solve many problems and join them together in a solution.  Now that IBM Research succeeded in this first debate, the team needs to determine practical applications of this technology that they can sell.  “Project Debater’s underlying technologies will also be commercialized in IBM Cloud and IBM Watson in the future.”  Now that AI has gone beyond playing games to learning the art of persuasion and debate; it has proven that it can handle the "gray area” and nuances of human interaction and not just follow clear-cut rules.  “From IBM perspective, the debate format is the means and not the end. It's a way to push the technology forward and part of our bigger strategy of mastering language,”  It was an impressive debut, and it will be intriguing to see what’s up next. Practical application of the Technology
  60. 60.  Thousands of businesses are using IBM Watson to take advantage of AI- Customer relations, Chatbots & Medicine.  IBM strategy is to breakdown communication barriers between people & machines to harness their potential.  IBM uses gameplay-that its cognitive systems are capable of learning to solve puzzles in the same way that humans do.  Project Debater represents AI evolving past its current ability to answer questions & towards being able to engage in natural human conversations. Key Challenges, Learning points & Takeaways
  61. 61. Automating Retail With AI
  62. 62.  Often referred to as the Amazon of China, JD.com started in 1998 as a brick-and-mortar store in Beijing, but it has aspirations to be the world’s leading e-commerce retailer.  Based on its tremendous growth, it might not take long for the company to get there. Richard Liu, the company’s founder, CEO & Chairman has even gone so far to predict his company won’t need humans and said “I hope my company would be 100% automation someday…no human beings anymore, 100% operated by AI and robots.”  JD.com and its competitors such as Amazon, Alphabet, Tencent, Alibaba and more are not only racing to be the world's largest e- commerce business but to create the operating system for retail in the future. JD.com is driving business with AI, big data, and robotics while building the retail infrastructure for the 4th industrial revolution JD.Com - AI
  63. 63.  To handle delivery, logistics and supply chain across vast retail network.  Shanghai fulfilment center- 200000 orders per day – employs 4 people.  Robots- Powered with machine learning, move crates of products to conveyor belts, packed by other set of robots and gets despatched.  This has enabled next day delivery services anywhere in China for 1.3 Billion people- 10 million Km of territory.  Working on same day delivery.  Has a chatbot that is capable of producing automated poetry when items are purchased as gifts.  Partnered with China’s social media giants- Tencent and Baidu to integrate messaging and image sharing apps.  AI is used to match users, based on profile data with items sold by JD.com What does JD.Com use AI for ?
  64. 64.  JD.com is not only investing in the technology of tomorrow, but they are also applying today's technology into its operations in many ways from smart warehouses to drone delivery.  Here are just a few ways JD.com uses AI, big data, and robotics in operations today:  Automated warehouses: While JD.com’s warehouses aren’t entirely autonomous, they are taking action currently to automate everything they possibly can.  Robots: Some of the company’s most advanced robots work in its 500 warehouses. They stack products on shelves and pack and ship merchandise to send out to consumers.  Drones: JD.com has used Drones to deliver products across China since March 2016- close to proximity of drone stations- furthest being 15 Km.  They use drones of various shapes and sizes, and they are currently working to build a drone that can carry up to five tons.  Autonomous trucks deployed by the company have accumulated 17,000 hours of road driving experience- Human driver is a must in cities.  Working towards unmanned trucks Automated deliveries By Air & Road
  65. 65.  The company is testing out facial recognition software at its headquarters that would allow shoppers to take their merchandise out of a store without stopping to pay; payment is controlled through facial recognition.  Customers can start signing from their smartphones and using the cameras to upload images of their faces ( HD) for identification. ( Machine learning) Facial Recognition Technology Smart Fridges Has announced Smart Fridges that uses camera equipped with image recognition technology. Cameras can scan items in the fridge and inform the expiry dates. It will inform to the smart phone items which are running low for ordering.
  66. 66.  JD.com founder has said that he hopes to see his company’s human staff reduced from 160,000 to 80,000 within next 10 years. While he says many will be retrained, it seems retaining human jobs comes secondary to driving efficiencies & improving customer experience.  Driving efficiency within its operations and supply chain is JD.com’s primary motivation for rolling out AI. Automated warehouses , delivery networks & retail outlets all form a part of this plan.  JD.com is also partnered with social media providers to allow them to use data on their customers for AI-driven precision marketing campaigns carried out entirely through their social apps.  Starting out as a brick-and-mortar retailer, JD.com is blurring the boundaries between online & offline shopping through a drive to introduce e-commerce technology into its physical stores. Key Challenges, Learning Points & Takeaways
  67. 67. Making AI Part of the Fabric of everyday Life
  68. 68.  AI tools contained in Office 365.  PowerPoint is capable of giving design tips based on how it observes the user working.  Work uses AI to suggest meanings, alternate phrases & check spelling, grammar & punctuation.  Azure cognitive services offers “ pre-built” machine learning solutions for speech recognition, text analysis, computer vision & language translation.  Another tool that has the potential to be very useful is Sketch2Code- capable of generating working HTML websites from simple sketches.  Microsoft offers online AI school. This is the collection of resources that covers the basics of what AI can do and how to start using it. How does Microsoft use AI
  69. 69.  AI tools contained in Office 365.  PowerPoint is capable of giving design tips based on how it observes the user working.  Work uses AI to suggest meanings, alternate phrases & check spelling, grammar & punctuation.  Azure cognitive services offers “ pre-built” machine learning solutions for speech recognition, text analysis, computer vision & language translation.  Another tool that has the potential to be very useful is Sketch2Code- capable of generating working HTML websites from simple sketches.  Microsoft offers online AI school. This is the collection of resources that covers the basics of what AI can do and how to start using it. How does Microsoft use AI
  70. 70. Underwater Data Centers  Cloud based AI requires a lot of network bandwidth.  Project Natick, which involves submerging data centers under the ocean to closed to coastal cities.  It is the size of the shipping containers & self contained.
  71. 71.  IBM vision is that AI will eventually become simple a part of fabric of everyday life- much like computers and internet.  To achieve this company is building tools & services that let other businesses carry out machine learning through their Azure cloud infrastructure.  AI functionality in its mainstream office productivity software used by millions – making jobs quicker & easier – Machine learning.  Microsoft has partnered with businesses of all shapes and sizes to roll out AI solutions & is now diving into reinforcement learning with its acquisition Bonsai. Key Challenges, Learning Points & Takeaways
  72. 72. Using AI To power Wechat & Health care
  73. 73.  Tencent is a Chinese tech company founded in 1998 and based in Shenzhen that hosts 55% of China’s Mobile Internet usage on its platforms.  Its mission is to “become the most respected internet enterprise.”  The company is China’s biggest social network company with 1 billion users on its app WeChat and 632 million monthly user accounts on social networking platform Qzone, is worth more than Facebook and has extended beyond instant messaging (its product is QQ) and social networking to gaming, digital assistants, mobile payments, cloud storage, education, live streaming, sports, movies and artificial intelligence.  The company’s dedication to artificial intelligence is evident in one of its slogans, “AI in all.” Tencent
  74. 74.  Stands out for the advancements it has made in facial recognition technology.  3 Chinese provinces- citizens are allowed to verify their identity through WeChat, Digital ID cards,.  Technology used in video games  It has trained software robots to become so good at strategy game Starcraft 2 that it can beat the computer team’s AI bots on the highest difficulty setting. How does Tencent use AI  Tencent is a top investor ( reported to be $120 Million) in robotics start-up UBTech, a firm that focuses on humanoid robots.  Quite possibly UBTech’s most famous contribution is Walker, a bipedal robot unveiled at the 2018 Consumer Electronics Show that can walk downstairs.  Of the Chinese tech firms known collectively as BAT (Baidu, Alibaba and Tencent), Tencent participated in the greatest number of AI equity deals and made the most AI investments in the United States.
  75. 75.  Healthcare AI is a main priority for Tencent, based on the company’s investments and AI partnerships.  China would like to be a world leader in personalized medicine using AI. More than 38,000 medical institutions have a WeChat account and 60% of those institutions allow patients to book appointments online.  Additionally, there are 2,000 hospitals that accept WeChat payment.  These services allow Tencent to collect valuable consumer data that helps train AI algorithms.  In a recent partnership with Babylon Health, WeChat users will have access to a virtual healthcare assistant.  Pushing the envelope even further, Tencent invested in iCarbonX, a company that aims to develop a digital representation of individuals to help perfect personalized medicine. Medical Technology
  76. 76.  Tencent is one of the China’s largest investors in AI & looks for opportunities to capitalize on AI across all of the industries in which it operates.  Leaders in Natural language processing, image recognition & machine learning.  Technology has huge implications for gaming and also create new gameplay challenges for players.  Successful in building AI into healthcare systems, helping surgeries and hospitals to run smoothly, and assisting doctors with diagnosing and treating illness. Key Challenges, Learning Points & Takeaways
  77. 77. Mail your comments to ramaddster@gmail.com End of Part -1 Will continue the summary in Part - 2

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