AI News, Artificial intelligence meets the C-suite

Artificial intelligence meets the C-suite

The exact moment when computers got better than people at human tasks arrived in 2011, according to data scientist Jeremy Howard, at an otherwise inconsequential machine-learning competition in Germany.

As machine learning progresses at a rapid pace, top executives will be called on to create the innovative new organizational forms needed to crowdsource the far-flung human talent that’s coming online around the globe.

To sort out the exponential advance of deep-learning algorithms and what it means for managerial science, McKinsey’s Rik Kirkland conducted a series of interviews in January at the World Economic Forum’s annual meeting in Davos.

Norton, January 2014)—and two leading entrepreneurs: Anthony Goldbloom, the founder and CEO of Kaggle (the San Francisco start-up that’s crowdsourcing predictive-analysis contests to help companies and researchers gain insights from big data);

The second big deal is the global interconnection of the world’s population, billions of people who are not only becoming consumers but also joining the global pool of innovative talent.

In 2012, a team of four expert pathologists looked through thousands of breast-cancer screening images, and identified the areas of what’s called mitosis, the areas which were the most active parts of a tumor.

The algorithm came back with something that agreed with the pathologists 60 percent of the time, so it is more accurate at identifying the very thing that these pathologists were trained for years to do.

Andrew McAfee: We thought we knew, after a few decades of experience with computers and information technology, the comparative advantages of human and digital labor.

A digital brain can now drive a car down a street and not hit anything or hurt anyone—that’s a high-stakes exercise in pattern matching involving lots of different kinds of data and a constantly changing environment.

The data and the computational capability are increasing exponentially, and the more data you give these deep-learning networks and the more computational capability you give them, the better the result becomes because the results of previous machine-learning exercises can be fed back into the algorithms.

He was able to do that with no particularly special skills and no company infrastructure, because he was building it on top of an existing platform, Facebook, which of course is built on the web, which is built on the Internet.

Jeremy Howard: I think people are massively underestimating the impact, on both their organizations and on society, of the combination of data plus modern analytical techniques.

Then Google fed that to a machine-learning algorithm and said, “You figure out what’s unique about those circled things, find them in the other 100 million images, and then read the numbers that you find.”

So when you switch from a traditional to a machine-learning way of doing things, you increase productivity and scalability by so many orders of magnitude that the nature of the challenges your organization faces totally changes.

I can’t think of a corner of the business world (or a discipline within it) that is immune to the astonishing technological progress we’re seeing.

But if the people currently running large enterprises think there’s nothing about the technology revolution that’s going to affect them, I think they would be naïve.

And it’s very painful—especially for experienced, successful people—to walk away quickly from the idea that there’s something inherently magical or unsurpassable about our particular intuition.

Data will tell you what’s really going on, whereas domain expertise will always bias you toward the status quo, and that makes it very hard to keep up with these disruptions.

What he was stressing was the importance of being able to ask the right questions, and that skill is going to be very important going forward and will require not just technical skills but also some domain knowledge of what your customers are demanding, even if they don’t know it.

Once you get to that point, the best thing you can possibly do is to get rid of the domain expert who comes with preconceptions about what are the interesting correlations or relationships in the data and to bring in somebody who’s really good at drawing signals out of data.

They also have seismic data, where they shoot sound waves down into the rock and, based on the time it takes for those sound waves to be captured by a recorder, they can get a sense for what’s under the earth.

And when you manually interpret what comes off a sensor on a drill bit or a seismic survey, you miss a lot of the richness that a machine-learning algorithm can pick up.

But the pilot programs in big enterprises seem to be very precisely engineered never to fail—and to demonstrate the brilliance of the person who had the idea in the first place.

It has a unit called the human performance analytics group, which takes data about the performance of all of its employees and what interview questions were they asked, where was their office, how was that part of the organization’s structure, and so forth.

Anthony Goldbloom: One huge limitation that we see with traditional Fortune 500 companies—and maybe this seems like a facile example, but I think it’s more profound than it seems at first glance—is that they have very rigid pay scales.

The more rigid pay scales at traditional companies don’t allow them to do that, and that’s irrational because the return on investment on a $5 million, incredibly capable data scientist is huge.

Machine learning and computers aren’t terribly good at creative thinking, so the idea that the rewards of most jobs and people will be based on their ability to think creatively is probably right.

On The Subject of Thinking Machines

68 years ago, Alan Turing proposed the question “Can Machines Think” in his seminal paper titled “Computing Machinery and Intelligence” and he formulated the “Imitation Game” also known as the Turing test as a way to answer this question without referring to a rather ambiguous dictionary definition of the word “Think” We have come a long way to building intelligent machines, in fact, the rate of progress in Deep Learning and Reinforcement Learning, the two corner stones of artificial intelligence, is unprecedented.

We define thinking as “The process by which we evaluate features learned from past experiences in order to make decisions about new problems” In the context of human thinking, when you see a person and you are faced with the task of determining who the person is (The New Problem), a activity (The Process) begins in your brain that goes through the search space of all the people whose face you can remember (The Experience), you then begin to consider the nose, eyes, skin color, dressing, height, speech and any other observable treats (The Features), the process then attempts to match these features to a particular person based on people we have seen before, if no satisfactory match is found, the brain concludes that this is a stranger (The Decision).

Consider a computer vision system on the other hand, trying to perform the same task using Convolutional Neural Networks, when the image of a person is imputed, the 3 Dimensional Tensor of pixels are observed, (The Features), the network then searches (The Process) for the presence of previously learned features called kernels or filters (The Experience), it then compute which of these features are present in the new image and returns a set of class scores (The Decision).

The process is remarkably similar, except for the process by which it is done, for example, Convolutional Neural Networks do not put the position of features into consideration, a nose at the position of the ear makes no difference to a CNN, the process in humans puts this into consideration, however, Capsule Networks, recently proposed by Geoffrey Hinton et al.

Artificial neural networks were inspired by neuroscience but their mechanisms are fundamentally different, we have largely given up on searching for systems that work like the brain, rather we keep searching for systems that work well, without minding how far we are deviating from the way the brain functions.

The ability of an agent to discover new policies or new sub policies in a hierarchical setting [4], gives such agent the ability to formulate new ideas and even do new things that we never expected it to do in a particular environment.

There are limitations in the form of such discovery being limited by the finite set of actions available to the agent at a given time step or in a state, however, humans are subject to very similar limitations, for example, you cannot just imagine that you want to fly without some jet pack or related equipment.

No mechanism could feel (and not merely artificially signal, an easy contrivance) pleasure at its successes, grief when its valves fuse, be warmed by flattery, be made miserable by its mistakes, be charmed by sex, be angry or depressed when it cannot get what it wants.” The above statement was made by Professor Jefferson in 1949.

The central core of the above assertion is the part, “No mechanism could feel (and not merely artificially signal, an easy contrivance)” The idea that reaction based on artificial signals cannot be described as a feeling is rather contrary to how the human system functions.

Take a humanoid robot, we can build sensors into the body such that when it is touched, signals would be sent to the processing system of the robot, which in this case is acting as a brain, the cumulative signals over a period of time would form a sequence that can then be fed into a type of supervised deep learning system called Recurrent Neural Networks.

In such a scenario, robot A might learn that some actions of an adversarial agent G is causing it to loose power faster than normal, possibly by making it overwork, robot A may then decide not to obey commands giving to it by Agent G, since data from past experience indicate a negative effect from commands initiated by Agent G.

The subconscious part of humans remains largely mysterious, many actions are initiated by it, hence, we can at least have a sense of superiority over robots based on the fact that there is at present no proof that we can infuse machines with sub consciousness within the framework of Deep Learning and Reinforcement learning.

UNICEF Innovation Fund: Thinking Machines

On 05 April 2018, the UNICEF Innovation Fund announces 6 new investments in open source technology solutions –Thinking Machines is among one of six new portfolio companies to receive investment.

huge amount of useful information is locked inside of organizations because there’s no easy way to match it with information outside of the organization.

Thinking Machines provides the technology to help data analysts quickly and accurately identify matching entities across millions of records of data.

Specifically, the platform can be used to find duplicate records within a single dataset or to connect records in one dataset with other records representing the same thing in another dataset.

Beyond standard exact text matches, it handles descriptive text, keys hidden inside large documents, missing values, and inconsistent formats with better-than-average accuracy.

The most useful keys for matching records, in this case, are typically related to the location of the community, which is a special case not often encountered in commercial matching tools.

Instead, we’re bringing together a community of domain practitioners and grassroots analysts who, together, will help to shape and improve our record matching algorithm.

By making our algorithms open source, we will be able to tap into a global community of data scientists who can further improve the algorithm and imagine new use cases in different communities, which will in turn help us to become better data scientists.

As individuals, all of us have had great careers in various sectors, but we’ve chosen to work together because we think it takes a holistic team to shape a future where AI elevates humans.

We will use the support from UNICEF Venture Fund investment to improve our record matching algorithm and build a web tool available for students, NGO analysts, journalists, and citizen users.

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