Blog - 20 Must Knows About AI

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What is AI?
1. Certain vs. Uncertain

4. What are the primary roles of AI? Cognitive Assistance (Decisions) vs. Autonomy (Intelligent Automation) 

5. Should it be called AI if Siri, Alexa, Google or Cortana helped?

2. How does AI get its intelligence? - Expert System (algorithms) vs. Machine Learning

11. How does AI learn from data? ML, DL & RL

Hype vs Reality
3. Where is AI Working? - Selling us stuff

19. Headlines of AI success are often followed by disappointment

31. Failed Predictions - Chatbots, Autonomous vehicles

21. When will AI become mainstream (other than the overused buzzword) like the internet?

AI vs Humans
6. AI doesn't make decisons, people do - Hybrid (Humans & AI) 

7. What can humans do better than AI?  Context and Creativity, takes a long time to learn, needs lots of data

8. What can humans do that AI cannot? -  Goals, Aspirations and Missions 

9. What can AI do better than humans? Machine Learning Superpowers

9.5 What can AI do similar to humans?

30. Why AI lacks common sense

10. What Human-like skills is AI good at? Machine Learning Models 

23. Is AI going to be smarter than humans? Artificial General Intelligence vs Billions of Narrow AI

24. Should we be afraid of AI? - Elon Musk 

Limitations of AI
12. What are the limitations of data?

  • Passive vs. Active Data sets,
  • Global data sets verses local data sets
  • Data required is not available (healthcare) or accessible (Healthcare privacy)

13. Why AI is biased - humans and data

14. Why AI Requires Quality Data Sets - Large for Deep Learning

15. AI based on incomplete data will be misleading

26. AI Can't Accurately Predict an Uncertain Future - though it can help (Nate Silver - though they could Predict the future in late 1800s)

AI & Automation
16. What is Intelligent Automation - Robotics (Physical) vs. Process (Digital)

17. Why are Autonomous Vehicles taking so long?

Challenges with Creating AI
18. Why do most AI project fail?

20. How will managers and staff leverage AI?

Societal Disruptions of AI
22. Will AI drive Workforce Disruption and Reduction - Yes. It will also drive job creation.

25. What are the major downsides of AI? 

27. Why AI is a Must in Managing Complexity - 65 complex challenges

28. Why AI Gives Us a chance to Keep Up With Complexity - complexity is growing

29. How AI could power Humankind's second remake - Assisted Strategic Reasoning 


1. What is AI? - Certain vs. Uncertain

2. How does AI get its intelligence? - Expert System (algorithms) vs. Machine Learning

3. Where is AI Working? - Selling us stuff

4. What are the primary roles of AI? Cognitive Assistance (Decisions) vs. Autonomy (Intelligent Automation) 

5. Should it be called AI if Siri, Alexa, Google or Cortana helped?

6. AI doesn't make decisons, people do - Hybrid (Humans & AI) 

7. What can humans do better than AI?  Context and Creativity, takes a long time to learn, needs lots of data

8. What can humans do that AI cannot? -  Goals, Aspirations and Missions 

9. What can AI do better than humans? Machine Learning Superpowers 9.5 What can AI do similar to humans?

10. What Human-like skills is AI good at? Machine Learning Models 

11. How does AI learn from data? ML, DL & RL

12. What are the limitations of data?

  • Passive vs. Active Data sets,
  • Global data sets verses local data sets
  • Incomplete, doesn't have all that is required

13. Why AI is biased - humans and data

14. Why AI Requires Quality Data Sets - Large for Deep Learning

15. AI based on incomplete data will be misleading

16. What is Intelligent Automation - Robotics (Physical) vs. Process (Digital)

17. Why are Autonomous Vehicles taking so long?

18. Why do most AI project fail?

19. Headlines of AI success are often followed by disappointment

20. How will managers and staff leverage AI?

21. When will AI become mainstream (other than the overused buzzword) like the internet?

22. Will AI drive Workforce Disruption and Reduction - Yes. It will also drive job creation.

23. Is AI going to be smarter than humans? Artificial General Intelligence vs Billions of Narrow AI

24. Should we be afraid of AI? - Elon Musk 

25. What are the major downsides of AI? 

26. AI Can't Accurately Predict an Uncertain Future - though it can help

27. Why AI is a Must in Managing Complexity - 65 complex challenges

28. Why AI Gives Us a chance to Keep Up With Complexity - complexity is growing

29. How AI could power Humankind's second remake - Assisted Strategic Reasoning 


30. Why AI lacks common sense


BLOG

1. What is AI?

There may be as many definitions of Artificial Intelligence as there are people. Merriam-Webster defines Artificial Intelligence as:

a branch of computer science dealing with the simulation of intelligent behavior in computers, the capability of a machine to imitate intelligent human behavior

While I was once a computer engine, I struggle understanding some of the explanations of AI that surface at the top of my Google search results. These explanations may be accurate and useful for Machine Learning engineers and data scientists, yet offer little value to most people I'm trying to help. Like knowing what a car can do to drive it, these executives and managers need to know AI basic to make decisons on leveraging AI to improve their businesses or improve clinical diagnoses, prognoses and treatments. This simple definition of Artificial Intelligence may be a helpful start:

a computer making decisions or assisting humans in making decisons when uncertainty exists. computer-based decisions or advice is based on both human learning and machine learning 

If you withdraw money from the ATM, a fixed algorithm will complete a certain decision of moving money out of your account. If you Google your bank, the search engine will be 99.5% certain you want Catskill Hudson Bank rather than a 0.5% chance your want a bank on the Hudson River.

If the probability of the correct decision is 100% certain, such as dispensing the $100 to your ATM based on your request, this is not considered Artificial Intelligence. This is simple conditional (if-then) reasoning that can be represented by computer logic or even built into a mechanical device. The ATM may seem intelligent, yet it is simply following a specific instructions like a wall switch turing on a light.   

Google search providing a link to Catskill Hudson Bank on top of the search results, rather than a news story of an accident on the bank of the Hudson River is based on both human learning and machine learning.   

It is important for people to understand that the foundations of AI is human learning, machine learning and a hybrid of the two. More on this in the next "must know" AI basics. 

2. How does AI get its intelligence? Expert System (Human Learning) vs. Machine Learning 

One May 4 1997, Deep Blue beat world champion Garry Kasparov in chess. IBM programmed Deep Blue to use brute computational force to evaluate millions of positions. While reported as artificial intelligence catching up to human intelligence, this exposed the limitations of using expert systems in AI. 

An expert system gets its intelligence from a human expert. The expert knowledge is represented by an algorithm that will make decisions in uncertain situation. 

Machine learning gets its knowledge from a set of data. The algorithm is generated by a computer program rather than an expert. The algorithm represents knowledge based on the data. 

An expert system would be an algorithm based on a human expert's knowledge that if someone applies for a $200,000 mortgage with a 700 credit score, makes $100k per year, they will would have less than a 5% rate of default. 

A machine learning model would be an algorithm based on hundreds of thousands of mortgage holders' applications credit scores, annual income, and mortgage amount along with historical default of these mortgage applicants. A copmputer program generates an algorithm that represents this set of data. 

While this explains where AI gets its intelligence, it also illustrates the flaws of AI. The expert system approach quickly falls apart when you want to factor in default rates by zip codes or other parameters. It is too difficult to represent many factors into algorithm. Machine learning can easily factor in the default rates by zip codes and thousands of other parameters, yet it is limited by the underlying data set then defines that algorithm. The data represents the past, has built in biases, and lacks the nuances of reason. If the person with a low credit score just won twenty million dollars in the lottery, it would recommend denying the applicant as it would probably treat lottery winners as flaws in the data because it has too few of them.

Gary Marcus

Expert Systems, Symbolic AI may be the greatest ooportunity for AI. The are almost 2 million healthcare studies produced each year. Yet this intelligence in burried in millions of silos of PDFs. There is a huge opportunity to code this into Symbolic AI. 

unlies    al  or factorinand presented by if-then rules such as a person applying for a mortgage with is a computer system emulating the decision-making ability of a human expert.[1] Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural code.[2]

3. Where has AI been proven? - Selling us stuff

While many AI predictions continue to come up empty, AI has almost entirely taken over most of what we touch on our smart phones. AI on our smart phones learns your behaviors, what you like, what is important to you and what is going on in your life. This information is used to compete for your attention, keep you engaged, and make Google, Facebook, and Amazon trillion dollar companies. This explains why the top AI talent work for Google, Facebook and Amazon. By leveraging their AI capabilities, Amazon won the market place contest (50% of ecommerce), Google and Facebook won the attention battle (85% of ad revenue), and Google won search (90% of search).

While there is alot of AI hype around diagnosing disease, autonomous vehicles and winning games, most of the impact of AI is behind the scenes. This is how Jeffrey Bezos described this to his sharholders. "Much of what we do with machine learning happens beneath the surface. Machine learning drives our algorithms for demand forecasting, product search ranking, product and deals recommendations, merchandising placements, fraud detection, translations, and much more. Though less visible, much of the impact of machine learning will be of this type – quietly but meaningfully improving core operations."

Predictions of companies using AI

4. What are the primary roles of AI? Cognitive Assistance (Decisions) vs. Autonomy (Intelligent Automation) 

5. Should it be called AI if Siri, Alexa, Google or Cortana helped?

Everything is AI now. Uses NLP, OCR, language translation, Siri, 

If uncertain decision are made without human interaction.

If curation is well done without asking

6. AI doesn't make decisons, people do - Hybrid (Humans & AI) 

AI doesn't make decisions. It can either assist a human in a decison or drive automation (a bot or triggers an action - w/d $100) 

It also doesn't decide the best response. It is a hybrid (humans decide the parameters, AI decides based on the data) 

Harvard study on hiring. While the study 

Book: Weapons of Math Destruction  

Biases

7. What can humans do better than AI?  Context and Creativity AI just doesn't get it. Understand. Context - AI doesn't know what a car is. It doesn't have a mental model of it like a human. It also is limited to its data set. It doesn't know .... Creativity -  8. What can humans do that AI cannot? -  Goals, Aspirations and Missions 

Goals - What problems are we going to solve?

Aspirations - 

Missions - 

Deep Blue won Jeapordy, another missreport - doesn't understand context. It was just a massive hyperlink attached to search and NLP. 

NEED Story of DARPA and self-driving cars - false predictions 

9. What can AI do better than humans? Machine Learning Superpowers

 Regression 

Association

Correlation

Self-play

10. What Human-like skills is AI good at? Machine Learning Models 

Use example of chatbots

Conversational UX, Machine Learning, NLP, Sentiment Analysis, Multilingual, Analytics & Administration, RPA, voice bots, cognitive abstraction 

Vision, Sound Processing, Sound transmission, NLP, Robotic Physical Automation, RPA, Geospatial, Predicting, Pattern Recognition, Decision Making, Associations, Explainability

Mental health tools - showing success

11. How does AI learn from data? ML, DL & RL

12. What are the limitations of data? Passive vs. Active Data sets

Passive is collection of data (such as Facebook, Google, Amazon, Netflix) and active would be to ask questions to improve the data sets. An example is long covid sufferers who have 51 possible symptoms. An active data collection would ask the person which symptoms they have to improve the ML model.

13. Why AI is biased - humans and data

14. Why AI Requires Quality Data Sets - Large for Deep Learning

See blog, some public data sets (ImageNet) have 3% errors.  

15. AI based on incomplete data will be misleading

Small sample sizes  - coronary artery disease - high quality & high efficiency 10. Isn't AI going to pass human? Artificial General Intelligence vs Billions of Narrow AI

16. What is Intelligent Automation - Robotics (Physical) vs. Process (Digital)

AI doesn't do anything - it needs a human or bot or technology to take the action 

17. Why are Autonomous Vehicles taking so long?

AI Powered Systems - autonomous vehicles, GANs (GPT-3, BERT), facial recognition, language translation, recommender systems, navigation systems, chatbots/virtual assistants,

18. Why do most AI project fail?

19. Headlines of AI success are often followed by disappointment

It's hard to replicate in every day practice

20. How will managers and staff leverage AI?

Citizen Developers

AutoML

Citizen Data Scientists

21. When will AI become mainstream (other than the overused buzzword) like the internet?

The progress of AI is beginning to shift from the concentrated AI talent working for tech giants and doing research to almost every sector of the economy. To understand how you and your company can ride this wave and avoid the undertow, the following are the "AI must knows" to leverage the collaboration of AI and humans.

22. Will AI drive Workforce Disruption and Reduction - Yes. It will also drive job creation.

23. Isn't AI going to pass human? Artificial General Intelligence vs Billions of Narrow AI

24. Should we be afraid of AI? - Elon Musk 

25. What are the major downsides of AI? 

26. AI Can't Accurately Predict an Uncertain Future - though it can help

27. Why AI is a Must in Managing Complexity - 65 complex challenges

28. Why AI Gives Us a chance to Keep Up With Complexity - complexity is growing

29. How AI could power Humankind's second remake - Assisted Strategic Reasoning 

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