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The Future of Everything Festival AI Track

 

 

The Future of Everything Festival

From the Wall Street Journal

May 8th, 2018

Artificial Intelligence Session

Today I attended the #wsjfuturefest at Spring Studios in Tribeca.  The session covered all things AI and included discussions from a number of NYU faculty members and AI experts. Screen Shot 2018-05-08 at 2.03.24 PM

The people who spoke in the track sessions I attended included:

  • Gary Marcus – Professor of Psychology and Neural Science, NYU
  • Garry Kasparov – Author, “Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins”; former World Chess Champion
  • Amy Webb – Founder, Future Today Institute; Professor, NYU Stern School of Business
  • Nicole Eegan – CEO Darktrace
  • Sean Gorley – CEO Primer
  • Kate Crawford – Co-Founder, AI Now Institute; Distinguished Research Professor, NYU

 

Here are some of the key takeaways from the sessions:

AI is not the scary big brother that some believe, rather right now it is a pretty limited tool.  It’s great when there is a tremendous amount of data to show a particular specific thing, but doesn’t work the minute it gets complicated.

We want a Rosie from the Jetsons and instead get Roomba, which doesn’t seem to know when it is spreading poop all over the house.

AI can do recognition tasks and classification.  Specifically, we already have speech image and natural language recognition.  AI can play board games and do advertising targeting.  It is not good with small data problems.  If we want better AI, we should start studying small people.  They are good problem solvers.

Right now particular firms are largely responsible for AI. They are Baidu, Tencent & Alibaba from China and the US entities of Amazon, Google, Microsoft, IBM, Facebook and Apple.  Whereas the US is great at algorithms and hardware, we are not as proficient as the Chinese in data – and they have much much more of it given their government and huge population.  They will eat our lunch in tech in the future.  China is fast building as the US is retreating.

Their more advanced systems are now being used to control people.  For example, China has a social ranking system of points that can be used to put people on a no fly list or no school entry designation based on their behavior that is continuously monitored.

What will the future look like?  It looks like the end of smartphones as we move to personal systems with interaction via voice.  We should think of ourselves as data or oil as data are the new oil.  We will have less autonomy and control of our information with an increasing number of devices.

Most panelists agreed that we have little security, but few supported the GDPR.  There was concern both about regulation and lack of regulation.

The CEO of Darktrace talked about cyber security and how certain bad actors seek to invade networks.  For example, there was an Internet attack on cappuccino makers in train stations connected to the train network.  Another entity used the fish tank at a particular casino to obtain network entry through the automated thermostat system.

AI uses generative adversarial networks, which are two neural nets competing against each other. There is a generator and discriminator from two networks that work together to solve the problem.  However, there are means of protection. Specifically, AI can also be used to detect attacks.  One method involves a data set of all prior attacks that is used to categorize and predict future attacks with supervised machine learning. Also, self learning of unsupervised data can find new threats modeled after human body immune response.

In the casino fish tank with internet connected thermostat, the system looked for the key term “high rollers.” The internal controls found the thermostat using too much data and shut it down.

AI can be used to video potential employees during hiring to record each movement and tick of a person. The goal would be to match the patterns to the people who are already in the organization and performing well to reduce turnover and poor performance.

 

 

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