Patterson Consulting: What is Artificial Intelligence?
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A review of the current artificial intelligence industry and what is real in artificial intelligence, deep learning, and applied machine learning.
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Patterson Consulting: What is Artificial Intelligence?
- 1. What is Artificial Intelligence?
- 2. Central Thesis for “What is Artificial Intelligence?” “Artificial Intelligence” is a term for algorithms that increase user productivity •Quite artificial, no where close to being “alive” •Historically we get over-excited about AI •Because its an existential threat •Accelerated in our minds by our fixation on social media fueled over-marketed narratives •Can be hard to define •Goal posts tend to move •Much like Data Science
- 3. History and Definitions Of Artificial Intelligence One of the best books written on the subject of AI (if not the best) is Stuart Russell and Peter Norvig’s Artificial Intelligence: A Modern Approach. I can’t recommend this book enough for you to get a more complete idea of the depth and history of AI.
- 4. The Study of Intelligence •Study of intelligence formally initiated in 1956 at Dartmouth •Yet is at least 2,000 years old. •The field is based on understanding intelligent entities and studying topics such as these: •Seeing •Learning •Remembering •Reasoning •These topics are components of what we’d consider intelligent function •to the capacity we have to understand intelligence
- 5. Building Blocks of Intelligent Study •Philosophy (400 BC) •Philosophers began to suggest the mind as a mechanical machine that encodes knowledge in some form inside the brain. •Mathematics •Mathematicians developed the core ideas of working with statements of logic along with the groundwork for reasoning about algorithms. •Psychology •This field of study is built on the ideas that animals and humans have a brain that can process information. •Computer science •Practitioners came up with hardware, data structures, and algorithms to support reverse engineering basic components of the brain.
- 6. Defn. “AI” •Methods considered AI •Linear modeling •Neural Networks •Random Forrests •Expert Systems •Rule Bases Algorithms that automate parts, or all of, tasks Russell and Norvig’s book on AI: The intellectual establishment, by and large, preferred to believe that a “machine can never do X.” Problem is, AI researchers have systematically responded by demonstrating one X after another.
- 7. Why is AI Popular Now? Deep Learning •Three major contributors are driving interest in AI today: •The big jump in computer-vision technology in the late 2000s (Hinton’s team, others) •The big data wave of the early 2010s •Advancements in applications of deep learning by top technology firms
- 8. Quick History of Neural Networks •Rough approximation of biological neuron •1950s saw perceptron developed in hardware •Changes in activation function allowed for non-linear functions •Multi-layer perceptron becomes more modern version of “neural networks”
- 12. Practical Use Cases of AI Today •Computer Vision Applications •Is there damage on this asset? (Insurance) •Sensor Applications •Which machine on this assembly line needs maintenance? •(Automotive, manufacturing) •Control and Planning •Robots that navigate a warehouse (logistics, retail) •Machines today are getting better at •Making sense of images from cameras •Working w sensor data •Making a plan to interact with the world based on vision and sensors
- 13. •Developed Deep Reinforcement Learning techniques to play different types of games •Atari •Go •Developed system that beat the world’s best Go player •AlphaGo beat Lee Sedol the world champion in a five game tournament “Alpha Go Zero.” Later on deepmind developed a new variant that was able to defeat Alpha Go at its own game only 40 days later Deepmind
- 14. IBM Watson Cancer Reality check: • uses expert system (programmed by Memorial Sloan Kettering) to recommend treatments • uses NLP to discover and summarize relevant studies, research, etc., to back up recommendation • does NOT automatically learn from patient record data • apparently uninterpretable despite being an expert system! Pros • high concordance with common practice -- at hospitals similar to MSK • can make medical decision making more efficient • can improve patient outcomes in regions with limited resources, no experts Cons • recommendations are biased toward MSK doctors, patient population • cannot take, e.g., local regulations into account (maybe recommended treatment not covered by insurance) • human-in-the-loop training is slow, labor intensive; makes adaptation to local population difficult
- 15. Why Does the term AI attract so much Attention? •Existential •Will it take my job? •Will robots take over the world? •More subtly: “can it answer all of my questions?” •So often people expect technology to “give them the answers” •Being human is about the intuition to know which questions to ask •And leveraging the right technology to answer these questions
- 16. Elon Musk says “Regulate AI”
- 17. Irrational Fear in a Non-Linear World •When we start out with existential threats on our way of life •Coupled with irrational exuberance in marketing narratives •We arrive at some crazy end games, which tend to not be realistic as its hard to project outcomes in the non-linear world (that we live in) •Ever notice that in horror movies the narrative always requires the characters to make irrational choices to move forward? •“how about we *not* land on that planet?” •Society tends to self adjust in emergent fashion •Lots of agents make small local decisions to adapt to changing environments •Tends to make global changes in the system in ways that are hard to predict
- 18. Also: Sort of hard to regulate games of linear algebra and methods of optimization Sorry, Elon.
- 19. Guns, Germs, and … Artificial Intelligence
- 20. Modern AI Marketing Narratives •Narratives such as •Requires a phd to do anything related to “AI” (“expert gating”) •Only top 5 tech shops can hire anyone good at AI, NFL-level salaries •Techniques are impossible to fathom, basically •A lot of hype
- 21. The Hype Cycle •Previous hype cycles: •cloud, smart grid, big data •Why is AI different than most hype cycles? •Existential threat •Companies tack on the big terms of the cycle to get marketing and funding •Most have no real claim to the term they tack on •“AI spreadsheets”, “AI calendars”, “AI for HR”, etc •What do they really mean? •Most of the time: “we use linear modeling in our product in some tangential way”
- 22. All of This Has Happened Before Periods of interest have been the result of the sector being unrealisti‐ cally overhyped followed by a cycle of predictably underwhelming results. AI Winter I: (1974–1980). The lead-up to the first AI winter saw machine translation fail to live up to the hype. Connectionism (neural networks) interest waned in the 1970s, and speech understanding research overpromised and underdelivered. AI Winter II: Late 1980s. In the late 1980s and early 1990s, there was overpromotion of technologies such as expert systems and LISP machines, both of which failed to live up to expectations. The Strategic Computing Initiative canceled new spending at the end of this cycle. The fifth-generation computer also failed to meet its goals.
- 23. AI, Experts, and Commodity •Hadoop, 2009: only a few experts at the top shops can do this (Google, Yahoo, FB) •2017: Hadoop becomes commodotized •Although the narrative is that AI-experts are compensated like NFL stars today, and that only the big-tech co’s can get them… •Reality is that it will get commoditized through the combined forces of tooling, open source, integrated AI-apps •Along a long enough timeline, all technology converges on “Big 6 Consultant”
- 24. Post Trough AI-Technologies We’ve seen this with the following: •Informatics •Machine learning •Knowledge-based systems •Business rules management •Cognitive systems •Intelligent systems •Computational intelligence The name change might be partly because they consider their field to be fundamentally different from AI.
- 25. The Red Queen Always Wins •We Survived the spreadsheet from the 90’s •Radiologists will survive AI today Automation will shift and evolve the workforce as it has for the past 1000 years “My dear, here we must run as fast as we can, just to stay in place. And if you wish to go anywhere you must run twice as fast as that.”
- 27. A Style Guide for Writing About AI •Don’t talk about “AI” as if it is a noun •Its not •Don’t take anything Elon Musk says about AI too seriously •He’s great at cars and rockets, but for other things --- Its marketing,
Editor's Notes
- #6: The study and application of AI techniques we see today are based on these fundamentals. We typically see the study of AI broken into a focus on either behaving or thinking in simulated intelligent systems.
- #8: In 2006, Geoff Hinton and his team at the University of Toronto published a key paper on Deep Belief Networks (DBNs).2 This provided the industry with a spark of creativity on what could possibly improve the state of the art. We’ve seen a tsunami of deep learning publications at top journals over the succeeding decade.
- #14: Go was previously too hard for the same techniques that solved checkers and chess (A*-variants)
- #23: This down period is referred to as an “AI winter” and involves cuts in academic research funding, reduced venture capital interest, and stigma in the marketing realm around anything connected to the term “artificial intelligence.”
- #25: 2009: only a few experts at the top shops can do this (Google, Yahoo, FB) We did it at TVA Cloudera comes along, begins the commodotization process Top end people in distributed systems see this Ex: Joe Hellerstein co-founds Trifacta 2017: Hadoop becomes commodotized Not nearly as exciting as it used to be Table stakes in most shops