According to Kernel founder Bryan Johnson: “The market for implantable neural prosthetics including cognitive enhancement and treatment of neurological dysfunction will likely be one of if not the largest industrial sectors in history.”
“AI” is a loaded, loaded term.
For basketball fans of a particular vintage, it conjures memories of a pint-sized shoot-first point guard who reportedly showed up for press avails under the influence of at least one mind-altering substance.
Allen Iverson even established some general pop-culture visibility with his on- and off-court exploits. He was a quintessential “good news-bad news” basketball story.
Though the former Philadelphia 76ers superstar is now an NBA Hall of Famer, nowadays AI is all about artificial intelligence.
But this AI is more than a simple “good news-bad news” dichotomy. There’s a lot of nuance in the space between those poles.
“AI” is a loaded, loaded term.
Let’s start with the bad.
The generalized fear – one expressed even by eminences such as prize-winning physicist Stephen Hawking – is that one day AI-driven robots will establish dominion over humanity.
Speaking at the Zeitgeist 2015 conference in London, Hawking predicted, “Computers will overtake humans with AI at some within the next 100 years. When that happens, we need to make sure the computers have goals aligned with ours.”
That fear grows from the unknown.
And there are abundant unknowns associated with even the potentially enormous positive contributions AI can make – in the highly topical field of cybersecurity, for example.
“Computers will overtake humans with AI at some within the next 100 years. When that happens, we need to make sure the computers have goals aligned with ours.”
As Ars Technica U.K.’s Sebastian Anthony reported on October 28, 2016:
Google Brain has created two artificial intelligences that evolved their own cryptographic algorithm to protect their messages from a third AI, which was trying to evolve its own method to crack the AI-generated crypto. The study was a success: The first two AIs learnt how to communicate securely from scratch.
This “cryptographic algorithm” developed by Martín Abadi and David G. Andersen is only a start. As Anthony writes, “The results were… a mixed bag.”
We have no idea how the researchers’ AIs – named Alice, Bob, and Eve – created what amount to their own algorithms to encrypt messages. There’s a solution, but no explanation. That means commercialization is hard to imagine right now.
At the same time, in his review of Abadi’s and Andersen’s “preprint paper” on the topic, Edd Gent of Singularity Hub observes:
Neural nets may be quite effective in making sense of communications metadata and for traffic analysis on computer networks. This is the kind of area where many think machine learning has a lot to offer cybersecurity because modern AI’s are great at spotting patterns, and they can process far more data than humans.
Still: “no one knows how it works.”
Meanwhile, AI’s potential continues to amaze in other lab settings.
As MIT’s Carl Vondrick explains to Victoria Turk of New Scientist that “any robot that operates in our world needs to have some basic ability to predict the future. For example, if you’re about to sit down, you don’t want a robot to pull the chair out from underneath you.”
Vondrick and his team have developed a deep-learning algorithm that “can predict how a scene will unfold and dream up a vision of the immediate future.”
Reports Turk:
To develop their AI, the team trained it on 2 million videos from image-sharing site Flickr, featuring scenes such as beaches, golf courses, train stations, and babies in hospital. These videos were unlabeled, meaning they were not tagged with information to help an AI understand them. After this, the researchers gave the model still images and it produced its own micro-movies of what might happen next.
Success hinged on combining computer vision with machine learning and then, according to John Daugman at the University of Cambridge Computer Laboratory, recognizing unfolding causal relationships over time.
We’ve discussed AI’s ability to perform “human” functions, including Psibernetix Inc.’s fuzzy logic-based ALPHA, an artificial intelligence programmed to fly fighter jets that has beaten several “top guns” in dogfight simulations.
As Evan Ackerman and Andrew Silver of IEEE Spectrum write: “Developing an unmanned aircraft is a complex and expensive process.”
So Psibernetix’s accomplishment is significant.
“Even retrofitting manned aircraft for autonomous operation,” note Ackerman and Silver, “can be tricky.”
Well, the Unmanned Systems Research Group at South Korea’s KAIST (formerly the Korea Advanced Institute of Science and Technology) is “testing a humanoid robot that’s designed to operate a regular aircraft by sitting in the pilot’s seat and using the controls just like a human would.”
Check out the PiBot’s (pilot robot) skills on a flight simulator.
Of course, ALPHA and PiBot could eventually be deployed as part of a Skynet-like AI army.
So let’s close with one item that may qualify as an unabashed “good” – an explanation for how we’ll make good on Stephen Hawking’s challenge to align the computers’ goals with ours.
We first zeroed in on Kernel – “a human intelligence (HI) company developing the world’s first neuroprosthesis to mimic, repair, and improve cognition” – in the August 24 Wall Street Daily issue. We spotlighted the company in our brief NBNBC mini-feature.
(“NBNBC” is an acronym for “no balls, no blue chips.” To do great things, you have to take great risks. Once a week, on Wednesdays, we focus on a company or innovator taking on a big challenge in a big way.)
So let’s close with one item that may qualify as an unabashed “good” – an explanation for how we’ll make good on Stephen Hawking’s challenge to align the computers’ goals with ours.
Bryan Johnson, the 39-year-old serial entrepreneur, recently announced he would put up another $100 million to fund Kernel’s mission. He’s already ploughed millions into the new company via his venture capital firm OS Fund.
Kernel has set out “to dramatically increase our quality of life as we increasingly extend healthy life spans.”
As we noted in August, “Kernel’s first goal is to develop a device that supplants or supplements the input and/or output of the nervous system for patients with cognitive disorders.”
Johnson’s new investment “will expedite the development of this prosthetic and similarly transformative neurotechnologies.”
As he articulates by explaining the timing of the fresh $100 million, Johnson’s vision of AI is quintessentially human:
Why now? Because the sooner we begin co-evolving human and machine intelligence, the better. The relationship between human intelligence and artificial intelligence (HI + AI) will necessarily be one of symbiosis. The challenge and potential of exploring this co-evolutionary future is the biggest story of the next century and one in which a closeness in development velocity is a necessity. In order for that to happen, we need to begin working on HI in earnest.
This is the ultimate expression of the Law of Accelerating Convergence.
And it just might represent, as Johnson suggests, “one of if not the largest industrial sectors in history.”
NBNBC
Johnson’s OS Fund has also invested in Boston-based synthetic biology startup Ginkgo Bioworks, which “designs custom microbes for customers across multiple markets.”
Ginkgo builds its “foundries to scale the process of organism engineering using software and hardware automation. Organism engineers at Ginkgo learn from nature to develop new organisms that replace technology with biology.”
Ginkgo has raised $154 million in two years, including a $100 million round of Series C funding announced in June 2016 that will help it buy 600 million base pairs of synthetic DNA.
The new investment will enable the company to expand its production of synthetic microbes into fields such as “commodity chemicals, industrial enzymes, and human health markets.”
A big part of that effort revolves around Bioworks2, Ginkgo’s new facility where sophisticated data analysis, advanced automation, and DNA synthesis drive creation of new prototype organisms.
It’s yet another example of convergence – synthetic biology and robotics engineering – that promises great and good things for humanity.
Smart Investing,
David Dittman
Editorial Director, Wall Street Daily
The post Artificial Intelligence vs. Human Intelligence appeared first on Wall Street Daily.
By David Dittman
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