Topic 12: AI
key points
AI is a still evolving set of technologies that will significantly change business, culture, and the world we live in there are many benefits and uses for AI in business already in place but even more will come in the future there are legitimate concerns and differences of opinions regarding the use and expanding power of AI AI will change the methods and roles for business leadership
Going forward with AI
AI predictions 2019 - *natural language* humanizes data analytics - after the hype, the development of *explainable AI* - *accelerated cloud data migration* fuels contemporary BI adoption - *enterprises get smarter* about analytics - *actionable analytics* place data into correct place
AI vs Humans
AI- tasks that are hard for humans are easy for computers, and vice versa the simplest computer can run rings around the brightest person when it comes to wading through complicated mathematical equations at the same time, the most powerful computers have in the past struggled with things that people find trivial, such as *recognizing faces, decoding speech, and identifying objects in images*
some computer security researchers
believe that digital criminals have been experimenting with the use of AI technologies for more than half a decade the irony, of course, is that this year the computer security industry, with 75 billion in annual revenue, has started to talk about how machine learning and pattern recognition techniques will improve the woeful state of computer security
there are concerns about AI
there are concerns over the potential for AI and related technologies such as robotics to *displace people and increase inequality* there will be ethical conundrums involving AI. For instance, it may prove challenging to devise systems that take *differing ethical perspectives* into account AI is advancing at such a breakneck speed that there are concerns about it being deployed in ways that have *unintended or unwanted* effects. For instance, a ML system designed to identify disease that is fed biased data might discriminate against certain people
benefits and uses of AI
AI can improve efficiency in the back office when applied to *routine activities* based on well-defined rules, procedures, and criteria chatbots: a rapidly growing use case for chatbots is customer service. AI assisted bots can seamlessly handle and process frequently asked questions in real time without human input extracting data is not the challenge anymore, now the challenge lies in making that data *accurate and actionable*
Neural Networks immensely complex, cont
big internet companies like Baidu, Google, and Facebook sit on huge quantities of information generated by their users the problem of *information overload* turns out to contain its own solution, especially since much of the data comes pre-labelled by the people who created them fortified with the right algorithms, computers can use such annotated data to *teach themselves to spot* useful patterns, rules, and categories within
IBM & AI
putting Watson to work to address the world's pressing issues
AI today is being used in
- virtual personal assistants - healthcare monitoring - fraud detection - music and movie recommendation - smart home devices - *news generation*
ML is driving changes at 3 levels
1. *tasks and occupations*: an example of task and occupation redesign is the use of machine vision systems to identify potential cancer cells- *freeing up radiologists* to focus on truly critical cases, to communicate with patients, and to coordinate with other physicians 2. *business processes*: an example of process redesign is the reinvention of the workflow and layout of *Amazon fulfillment* centers after the introduction of robots and optimization algorithms based on machine learning 3. *business models*: business models need to be rethought to take advantage of ML systems that can intelligently recommend music or movies in a personalized way. Example offer a substitution to a personalized station that *predicted and played music* a particular customer would like
the top 5 benefits of AI
1. enhance current products 2. optimize internal operations 3. make better decisions 4. optimize external operations 5. free workers to be more creative
4 primary concerns about AI
1. the adverse impact of AI on labor 2. important decisions delegated to AI systems (biases) 3. lethal autonomous weapon systems (life or die) 4. "superintelligence"= the risk of humanity losing control of machines
an AI-enabled company
AI will bring about a transformation of a lot of companies and even the rise of new types of companies, called *internet companies* the fundamental thing that defines and internet company is have achitected your whole company to leverage the new capabilities the Internet gives you AI is affecting how companies organize themselves. Old job descriptions- engineer, product manager, designer- are breaking down. *Roles are being invented*, brand new processes and workflows for the AI era as well Today, AI talent is *scarce*, and even more scarce is the skill to take AI technology and figure out how to take it to market what can be very effective is to have execs send a very *clear message* that employee AI development is valued
but don't freak out
I am infinitely excited about AI and not worried at all. Not in the slightest. AI will free us humans from highly repetitive *mindless* office work, and give us much more time to be truly *creative*. I cant wait. we should worry a lot about climate change, nuclear weapons, antibiotic-resistant pathogens, and reactionary and neo-fascist political movements. We should worry some about the displacement of workers in an automating economy. We should not worry about AI enslaving us
AI definitions
Oxford dictionary: - the theory and development of computer systems able to perform tasks normally requiring human intelligence - such as visual perception, speech recognition, decision-making, and translation between languages Merriam Webster: - a branch of computer science dealing with the stimulation of intelligent behavior in computers - the capability of a machine to imitate intelligent human behavior generally, organizations invest in AI development for 1 of 3 objectives - build systems that think exactly like humans do (strong AI) - just get systems to work without figuring out how human reasoning works (weak AI) - *use human reasoning as a model but not necessarily the end goal*
Partnership on AI
Partnership on AI to benefit People and Society was established to *study* and formulate best practices on AI technologies, to *advance* the publics understanding of AI, and to *serve* as an open platform for discussion and engagement about AI and its influences on people and society
AI is truly global
Singapore- one of 1st countries to announce a national strategy (AI Singapore in May 2017). The initiative brings the govt, research institutions, and cos together to *collab on research and speed up* local adoption of AI. Singapore has a head start in *AV*: it had the first self-driving taxis for use by the public and built a mini-town for further testing Israel- becoming a world leader in medical AI with dozens of new *healthcare* start ups. the govt announced a 5 yr program with a budget of 280 million to digitize patient data and use AI to gather important insights, with hopes of turning the homegrown expertise into consumer products that could make Israel an industry leader India- released its AI strategy only this summer, but it contains a big idea that could catch them up: become the *garage* that develops AI that creates economic growth and social dvlt for themselves and the rest of the developing world. the plan will focus on projects around HC, ag, education, smart cities and infra, and smart mobility and transpo France- the French govt released a 150 pg document earlier this year that spells out its AI efforts around the *health, enviro, transpo, and security* sectors and is putting 2 billion into funding projects around those areas Canada- 2 of the 4 godfathers of the current AI boom live, work, teach here. their efforts have helped spur major research and an AI industry there, including *offices* for Uber, Fb, Google. The current immigration restrictions in the US have also sent talented international researchers to Canada instead of Silicon Valley
should we really be worried?
Take fears about AI seriously the transition to machine superintelligence is a very grave matter, and we should take seriously the possibility that things could go radically wrong. this should motivate having some *top talent* in mathematics and computer science research the problems of AI safety and AI control My current guesses for the most likely failure modes are two fold: the gradual *enfeeblement* of human society as more knowledge and know-how resides in and is transmitted through machines and fewer human are motivated to learn the *hard stuff* in the absence of real need. Secondly, I worry about the *loss of control* over intelligent malware and/or deliberate misuse of unsafe AI for nefarious ends
EU publishes guidelines April 2019 and AI partnership
The European Commission published *7 principles* to create "trustworthy" AI programs and added that the burgeoning industry must comply with *existing rules* on privacy, consumer protection, and environmental standards "AI is developing at an exponential pace" said EU commissioner for digital economy. We don't want to stop innovation but the added value of the EU approach is that we are making it a people-focused process. People are in charge" she added that the 7 guidelines would be the *"baseline"* that companies and businesses will have to check against when developing AI technologies The guidelines also say businesses using AI in Europe should *inform people* every time they interact with an algorithm
should we really be worried? cont
as AI evolves, so does its criminal potential imagine receiving a phone call from your aging mother seeking your help bc she has forgotten her banking password. except its not your mother. The voice on the other end of the phone call just sounds deceptively like her. It is actually a tour-de-force of artificial intelligence technology that makes it possible for someone to masquerade via telephone *social engineering*, which refers to the practice of manipulating people into performing actions or divulging info, is widely seen as the *weakest link* in the computer security chain. Cybercriminals already exploit the best qualities in humans- trust and willingness to help others- to steal and spy. The ability to create AI avatars that can fool people online will only make the problem worse
AI, Machine Learning, Deep Learning
machine learning is the science of giving computers the ability to learn and find insights *without explicitly* programming the computers on what to do AI- early AI stirs excitement ML- begins to flourish DL- breakthroughs drive AI boom
neural networks immensely complex
neural networks were invented in the 1950s by researchers who had the idea that, though they did not know what intelligence was, they did know that brains had it brains do their information processing not with transistors, but with neurons. If you could simulate those neurons then some sort of intelligent behavior might emerge in the past few years, the remarkable number-crunching power of chips developed for the demanding job of drawing video-game graphics has revived interest early neural networks were limited to dozens or hundreds of neurons, usually organized as a single layer. The latest, used by the likes of Google, can simulate *billions*
Understanding this
one way of understanding this is that for humans to do things they find difficult, such as solving differential equations, they have to write a set of *formal rules*. Turning those rules into a computer program is pretty simple. For tasks that human beings find easy (walking), there is no need for explicit rules and trying to create them can be hard *machine learning* is a way of getting computers to know things when they see them by *producing for themselves the rules their programmers cannot specify* the machines do this with heavy-duty statistical analysis of lots and lots of data
digitization, data acquisition, and organization
the *first wave is the digitization* wave, where we take things that were analog, or just not in the computer, and digitize it. the digitization revolution in a lot of industries first comes and creates digital data. after that comes some data science, where you start to get more *insights*, and then also *AI*, because its only after you have the digital data that AI is very efficient in coming in to eat that data to create value true AI organizations are much more sophisticated, more more strategic in data acquisition if you can just have enough data to launch a product thats good enough, that allows you to enter a positive feedback loop in which your users help you generate more data. more data makes the product even better, so you have more users. and that positive feedback loop allows you to accumulate data, so that maybe after a few years you could have a pretty defensible business AI companies tend to organize the data better. so putting data in a *centralized data warehouse* makes it more efficient for engineers or software to exploit that data Cloud, Seas- the necessary algorithms and hardware for modern AI can be *bought or rented* as needed. Google, Amazon, Microsoft, Salesforce, and other companies are making powerful ML infrastructure available via the *cloud*
can software substitute for the responsibilities of senior leaders?
the job is going to be to figure out, "Where do I actually add value and where should I *get out of the way* and go where the data takes me?" that going to mean a very deep rethinking of the idea of *managerial "gut"* or intuition right now, there are a lot of leaders of organizations who say "of course im data driven. I take the data and I use that as an input to my final decision making process" but theres a lot of research showing that, in general, this leads to a *worse outcome* than if you rely purely on data in some activities, particularly when it comes to finding answers to problems, software *already surpasses* even the best managers. Knowing whether to *assert* your own expertise or to step *out of the way* is fast becoming a critical executive skill yet senior managers are far from obsolete. top execs will be called on to create the innovative new organizational forms needed to *crowdsource* the far-flung human talent thats coming online around the globe. Those executives will have to emphasize their *creative abilities, their leadership skills, and their strategic* thinking
they were drawn up
they were drawn up after consultation with a group of 52 experts, drawn from tech co.s, NGOs, and academics the non-binding guidelines are the first multinational effort to public rules on commercial AI, including on technical safety, accuracy, bias, and transparency of algorithms the EU is trying to take the lead in the global race on AI by insisting it can help businesses create technologies that are based on "trust"