Your brain is too small to manage your business
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Presentation that I delivered at "Accelerate AI, Europe 2018" in London on Sept 19, 2018. My focus is on socio-cultural perspective as well as proving information about various tools, vendors and partners available to help companies get started using AI.
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Your brain is too small to manage your business
- 1. Your brain is too small to manage your business Christopher Bishop chief reinvention officer improvising careers Accelerate AI, Europe 2018 September 19, 2018
- 2. • Historical perspective • Why AI now? • Who can help • How to get started
- 3. “AI is the new electricity. Just as electricity transformed industry after industry 100 years ago, I think AI will do the same.” Andrew Ng – Former Chief Scientist of Baidu Co-Chairman, Co-Founder of Coursera, Adjunct Professor at Stanford University
- 6. Using machines to solve problems for thousands of years
- 7. Charles Babbage The Difference Engine (1822)
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- 13. Why is AI adoption accelerating now? 13 Big Data Hardware Software
- 14. • 90% of world’s data created in last 2 years • 80% of it is unstructured • Annual rate of data creation=16.3ZB
- 15. IoT connected devices WW from 2015 to 2025 (in billions)
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- 17. AI to the rescue! Collect Analyze Act
- 18. Devin Wenig CEO, eBay 4 “If you don’t have an AI strategy, you are going to die in the world that’s coming.” SOURCE: www.cbinsights.com
- 19. AI in the Enterprise Source: Teradata – Bringing Artificial Intelligence to the Enterprise – October 30, 2017
- 20. Top verticals impacted by AI and machine learning • Financial services – machines can manage vast quantities of data to manage portfolios • Healthcare – reading scans, analyzing journals, managing back-end processes • Retail – tracking inventory, managing supply chain • Manufacturing - sensors in machinery, vehicles, production plants predict repairs and maintenance 20
- 21. Who can help?
- 24. •Image/video classification - Amazon Rekognition •Speech recognition – Amazon Alexa •Natural language processing – Amazon Lex •Recommendation engines - MXNet AWS offers tools, training for major use cases
- 25. Google • Open-source machine learning library for research, production • APIs for beginners and experts to develop for desktop, mobile, web, and cloud
- 26. • Used by 100,000 data scientists at 12,600 organizations • Interfaces connect: • Leading open source deep learning tools with H2O • Framework combines H2O and Spark • Driverless AI - "AI to do AI"
- 27. Vendors and partners [NEXT SLIDE]
- 28. • Transform data into a powerful asset - NLG • Relevant, intuitive stories delivered at scale “A massive amount of time and effort has been spent in gathering data and no human can look at all of it. Narrative Science offers us opportunities to more efficiently sift through large amounts of data and bring out insights more quickly.” —Craig Muraskin, Managing Director of Innovation, Deloitte IMPACT • Journalism • Marketing • Communications
- 31. • World-class legal search engine • Combined with artificial intelligence-driven technology “Casetext has changed my approach to legal research. I used to wait for days and hours for answers using traditional legal research tools, but with Casetext, I can find my best, most on-point case in minutes and seconds.” Sasha Rao, Partner Maynard, Cooper & Gale LLP IMPACT • Legal research • Paralegals • Case preparation
- 32. • Conversational AI powering intelligent virtual assistants and enterprise chatbots IMPACT • Call center • Help desk • Customer support
- 33. • Provides cargo companies with data cleansing, demand forecasting • Predictive optimization based on data science and AI “Transmetrics finds solutions. They pragmatically clean data and make it usable – and on the way create unexpected benefits for our dispatchers and managers alike.“ Nils Wemhoener SVP Operations Overland, Kuehne + Nagel AG IMPACT • Logistics • Supply chain
- 34. UNICORN AI FOCUS Machine learning-based credit risk modeling Deep learning-based drug discovery Machine learning for predictive analysis of IoT data AI to predict and prevent cyber attacks Extracting information from electronic health records www.cbinsights.co m Unicorns offering niche AI solutions
- 35. Data collection, cleaning, tagging
- 36. Transforming real world data into high quality training data Data cleaning and extraction, 2D and 3D image recognition, comparison, segmentation Data collecting, tagging; model scoring, validation Create training data for computer vision models
- 37. Curated datasets
- 38. Images, geospatial, sentiment, government, statistics Annotations, labels, bounding boxes, 1ks of categories Large list of public data sets for training Image Processing, Natural Language Processing, and Audio/Speech Processing Data for building computer vision models
- 39. • Pick a small project - prediction or analysis to benefit business • Explore four popular use cases based on where you have data: 1. Image/video classification 2. Speech recognition 3. Natural language processing 4. Recommendation engines • Identify vendor’s software to augment your capabilities • Make an existing application smarter or more autonomous • Build, deploy, evaluate, adjust How to get started
- 40. 40 “Over the next decade, AI won’t replace managers, but managers who use AI will replace those who don’t.” SOURCE: HBR – July 2017
- 41. From one small brain to another… THANKS! @chrisbishop chris@improvisingcareers.com
Notas do Editor
- Share some perspective on why I think this is an exciting time – that AI is the next iteration in macro tool development… Examples of how it is being applied today, challenges between balancing tremendous economic potential with issues around bias, ethics and morality; Ideas for potential future applications and impact Closing comments around our role as an AI community and as humans using technology [NEXT SLIDE]
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- Andrew Ng is considered on of the leading thinkers in the AI space. He says that just as electricity transformed business and culture over the past 100 years, AI will similarly infiltrate every business and discipline. 100 years ago we didn’t have widespread access to electricity but once we did it transformed: Agriculture – through the use of refrigeration Communication – with the introduction of the telegraph and telephone Manufacturing through the electric motor Healthcare – lights and tools Hard to imagine these functional areas without electricity Surprising clear path today that AI will have a similarly transformative effect. [NEXT SLIDE]
- AI is a constellation of tools and processes that cover a wide spectrum…from Google Home and Alexa, and Echo Dot… It is not just one thing - asking someone what the biggest problem is in AI is like asking a biologist “what’s the biggest problem in biology?” [NEXT SLIDE]
- The top end includes instantiations like IBM Watson which is made up of ninety POWER 750 servers sitting in a refrigerated room in Armonk, NY [NEXT SLIDE]
- This is the Antikythera Mechanism – discovered off the coast of a Greek island in the 1901 – it was created in 205 BC – over 2,000 years old! [NEXT SLIDE]
- Designed to mimic human learning and problem-solving, early efforts laid the groundwork for today’s artificial intelligence paradigm Not far from here – in this town – almost 200 years ago, a brilliant Englishman was conjuring a machine to help solve math problems Economics, sociology, political science, psychology, emotional cognition, expertise acquisition, and verbal problem solving Charles Babbage also wrote about “The Economy of Machinery and Manufactures” – his focus was on how man-made tools could help drive business models We have gone from the Difference Engine to the Recommendation Engine… [NEXT SLIDE]
- First AI workshop at Dartmouth summer of 1956 – led by John McCarthy – coined the term *artificial intelligence* - 62 years ago! [NEXT SLIDE]
- AI languished in academia for almost 40 years – then a large global tech company decided to create basically an AI instance to challenge a human First set of six matches was in 1996 and Kasparov won – then lost in a rematch in 1997. [NEXT SLIDE]
- 2011 – Watson beat the reigning Grand Champions Ken Jennings and Brad Rutter [NEXT SLIDE]
- Google’s DeepMind beat the reigning Go champion Lee Sedol last March 2017 – 4 – 1 in a five game match. But the data scientist who helped put it together says his 6 year old son knows more – can tell you the difference between Go and chess as well as the history of the wo games. AlphaGo can not do that! [NEXT SLIDE]
- Jan 2017 – An AI instance called Libratus beat four human players in a match that lasted 20 days – created by Carnegie Mellon – won more than $1.5M The algorithms can take information and output a strategy in a range of scenarios, including negotiations, finance, medical treatment and cybersecurity.“ "Now we have proven the ability of AI to do strategy and reasoning, there are many potential applications in future." “imperfect information.” [NEXT SLIDE]
- Three key factors… [NEXT SLIDE]
- What an amazing world we have built…actuators and sensors in many, many settings The viscosity of data is continuing to decrease The Industrial Internet is also generating reams of information. Expectation is that there will be over 50,000 exabytes (each a billion GB) by the year 2020 [NEXT SLIDE]
- The Internet of Things (IoT) is providing data at increasing rates – coming from myriad sources - your clothing, your appliances, your car – all creating data. Financial information, medical information, personal information – tons of it being generated on a daily basis 23.4 in 2018 75.44 in 2025 [NEXT SLIDE]
- A world, in fact, that’s far more complex than our reptile brains can handle. The soggy, onboard 3 pound glucose driven computer is not able to keep up. We are creating more data than the human brain can collect, parse and rationalize. It is just too overwhelming! The upcoming paradigm shift is not just a technological revolution. It’s an evolutional revolution. It’s the biggest shift in human evolution since the dawn of time that will change who we are as a species for good. [NEXT SLIDE]
- The good news is that we have concurrently created a technology – a tool – that can help us deal with this problem. Technology has always made our lives easier, safer, faster, more comfortable. Each innovation has given us the tools to do more with less. Every technological revolution was like the next iteration of scaling human output. But we have never faced a technology that would challenge human cognition, the core of our identity. Software’s learning process is based in the way humans learn – replaying past experiences over and over to try and extract the most accurate hints on what it should do in the future. “That’s something that we know the brain does,” says Hassabis. “When you go to sleep your hippocampus replays the memory of the day back to your cortex.” Deep-learning software attempts to mimic the activity in layers of neurons in the neocortex, the wrinkly 80 percent of the brain where thinking occurs. The software learns, in a very real sense, to recognize patterns in digital representations of sounds, images, and other data. We are doing it by modeling layers of virtual neurons. Google open-sourced its deep learning code, TensorFlow, to the public in a bid to help accelerate the development of artificial intelligence. [NEXT SLIDE]
- forbes.com/sites/shoptalk/2017/03/28/6-key-takeaways-from-shoptalk-2017-an-insiders-perspective/#29aa560b24ab [NEXT SLIDE]
- AI has already penetrated the Enterprise – every vertical from financial services to retail to manufacturing. That’s because algorithms mine data from the customer’s purchasing history—in addition to reviewing patterns of likely fraud stored in its databases—and can tell whether, for example, the suspect transactions were innocent actions of a globe-hopping pilot. What’s next? According to a patent application Walmart filed, it seems like its next step is integrating IoT tags to products in order to monitor product usage, auto replace products as necessary and monitor expiration dates or product recalls. [NEXT SLIDE]
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- There are three top level categories into which AI solutions can be parsed – Niche, prepackaged applications which are comparatively simple and inexpensive to deploy and deliver a specific functionality Cloud-based APIs that connect to a company’s existing data and analytical tools Bespoke Enterprise-ready tools like IBM Watson and Keras that require massive amounts of data and address much more complex questions [NEXT SLIDE]
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- https://aws.amazon.com/machine-learning/what-is-ai/ [NEXT SLIDE]
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- Natural Language Generation = NLG [NEXT SLIDE]
- Their tools turn information understandable by just a few people… [NEXT SLIDE]
- Into information that many people can use [NEXT SLIDE]
- Replacing legal and paralegal resources [NEXT SLIDE]
- Similar to Google Duplex that made the hair appt! Check out the video on YouTube [NEXT SLIDE]
- Big impact in logistics and supply chain [NEXT SLIDE]
- Numerous start-ups are appearing to deliver even more specific and niche AI-driven solutions [NEXT SLIDE]
- To train, test and fine tune models – you need data! – Data is the fuel [NEXT SLIDE]
- Figure Eight offers machine learning specific templates and processes and they are partnering with Google Our Human-in-the-Loop Machine Learning platform transforms unstructured text, image, audio, and video data into customized high quality training data. There are various companies focused on cleaning and tagging various kinds of data sets for building and testing models – from image recognition to voice recognition to autonomous vehicle related data [NEXT SLIDE]
- Numerous open and curated data sets are available – again with a wide range of info available across disicplines and verticals https://skymind.ai/wiki/open-datasets http://www.cvpapers.com/datasets.html https://storage.googleapis.com/openimages/web/index.html As is always the case in emerging technologies, there is an ecosystem and supply chain that emerges to support any new technology. AI is no different. There are many companies offering to either provide training data or collect yours and clean it and tag it so you can use it for your own training models. Potential shortcomings include: Insufficient or incomplete data Unbalanced data Misleading correlations Inconsistent annotations
- Start with one of the top four use cases if you can: https://hbr.org/2016/10/7-ways-to-introduce-ai-into-your-organization [NEXT SLIDE]
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- Thanks and keep in touch!