The fascinating thing about his answers is that he says nothing that wasn't already known 25 years ago. 'General AI' is a really vague term and means almost nothing.<p>It's well known in AI that common sense AI systems are incredibly hard to build and expert AI systems are relatively straight forward to model and build. Given that the most 'common sense' type products we see like Siri and Alexa still appear to be rules based systems (that have been enhanced with historical data no doubt), we all know the world is not suddenly going to become a common sense AI paradise unless a new Marvin Minsky makes some breakthroughs.<p>I find that the term 'AI' is frequently used when a company has taken an existing problem and enhanced the solution by using machine learning at one or more places. Nothing wrong with that fundamentally, but calling that AI is more of a marketing gimmick than anything else.<p>Google/Facebook do some of the best machine learning enhanced workflows that delight users. They usually refrain from branding it as AI.
Anyone who has spend time in AI or ML would know this is true. Currently, AI is very helpful to solve certain types of tasks. The recent advances in Deep Learning etc. is the improvement of AI to solve some of these specific tasks. A lot of people have confused this to assume there would be a master AI system that would solve general tasks. I don't see that happening anytime soon.
Is he a CEO though? Wikipedia and other press articles say that Hassabis is the CEO: <a href="https://en.wikipedia.org/wiki/DeepMind" rel="nofollow">https://en.wikipedia.org/wiki/DeepMind</a> and Suleyman is Chief Product Officer, the head of applied AI at DeepMind.
Step 1: State a problem. Any problem which has been articulated is vastly closer to being solved than any problem which has not.<p>Step 2: Rescue the problem from necessitating general AI by developing an algorithm. Any problem which has been split off from general AI is vastly closer to being solved.
I thought Hassabis said he was seriously trying to build grounded AGI based on some type of system with deep learning-ish stuff. I think it will work within around a dozen years or so.<p>How do they know that they can't take their current deep learning-type techniques or some variation/enhancement of them and apply them to more demanding and general circumstances?<p>For example, they have the agent that goes around trying to score in a 3d first-person-shooter with the pixels as input. Has it been determined that there isn't a way to gradually train that system to recognize words, phrases, or even sentences that would give it hints about how to score better, maybe by associating words with other (spatial/navigation) parts of the model or more general association between aspects of the learning system in different domains.<p>To me it seems like some type of deep learning (or maybe a variant using spiking neurons since those can reportedly learn with fewer examples?) when grounded by integrated sensory inputs of different times and incrementally trained, may be able to already exhibit general animal/human-like intelligence, or will be able to with one or two minor 'breakthroughs'.<p>I'm still going with 2029 (as suggested by Kurzweil I think).
DeepMind sounds more and more like IBM Watson by the day. Both created AI engines for games (Jeopardy, Go) and are now focusing on the medical market. The two examples of actual AI applications cited in the article focus on DeepMind's healthcare efforts:<p><i>The company is working with the National Health Service (NHS) in the UK on a project around the early detection of acute kidney injuries. Critics, however, argue that the collaboration with DeepMind is wider than the company and the NHS previously disclosed. In addition, it’s also working with the Moorfields Eye Hospital on looking at how it can use the hospital’s eye scans to diagnose eye conditions better and faster. The NHS, however, mostly used its own algorithm in its project, with DeepMind focussing on the front-facing app. Suleyman argued that this is due to the fact that it’s still early days for this collaboration, which only started 12 months ago.</i><p>So in the health space they're currently doing anomaly detection and building applications for their clients. Sounds very Watson/consulting. Lots of money in healthcare due to the amount of data generated and the complexity of building regulation-compliant.<p>Judging from their website, they've also built an algorithm for reducing cooling energy usage in Google's data centers.<p>There are a ton of IoT platforms that are currently competing to connect a bunch of industrial/medical/automotive/aerospace and defense devices and servers, analyze that data with machine learning and statistical techniques, make it easy for users to build their own cloud/edge applications, and eke out every last drop of efficiency they can for their customers.<p>DeepMind is fortunate to have Google's brand, infrastructure, and reach. I'm just not sure what else separates them from any other "AI" (read machine learning/algorithm-building/efficiency consultancy) out there.
I like his honesty in saying we are decades away from any general sort of AI.<p>I remember at AAAI 1982, the conference was handing out bumper stickers saying "AI its for real" that my coworkers and I happily put on our cars. Yeah, a little optimistic:-)<p>That said, I like the way Deep Mind is run, especially the forays into health science.
People with high-profile jobs are severely constrained in what they can say (even though this guy is not the CEO, contrary to what the title claims ATM).<p>And we already saw that google doesn't want to touch the "uglier" aspects of ML/AI/robotics with a ten-foot-pole (e.g. see how they reacted to Boston Dynamics's humanoid robot video).<p>So I'm not sure if we should take this seriously. DeepMind technical views are mostly on its papers. Not press statements.
I personally think that if "general AI" (that is, something akin to human-level or beyond, and possibly sentient) ever happens - it'll happen by accident, and will be an emergent phenomenon that we won't be able to explain.<p>It will surprise all of us, and will likely be a "earth-shaking", akin to an extra-terrestrial alien contact. Perhaps even more so - because it would be a hard example against the idea of dualism (unless it arises from a quantum computing system - then humans can posit an "out" of course, regardless of whether it is true or not).<p>All pure speculation, of course. I honestly don't think we'll be able to "design" a general AI - we don't even understand how sentience and consciousness (among other things in the philosophy of mind) even works or exists in human brains. We understand the gears and wheels, but can't describe the factory.
I totally agree with the general sentiment of the comments and the article that AGI is a long way off... in the anthropomorphic sense we usually imagine.<p>That said in many regards, the sum total of the internet, social networks and automated systems that already exist, to me is fast beginning to resemble an emergent AGI / ASI, and furthermore is already out and about busy doing its business.<p>That it just happens to use networks, humans and machines as cells and DNA seems kind of irrelevant.<p>Of course this is a silly POV thought experiment, but seriously, if that were the case, would we even notice?<p>I'm not sure our own cells and DNA know they are part of a larger entity for example.<p>Okay, enough weird thoughts.. time for bed.
>> "We founded the company on the premise that many of our complex social problems are becoming increasingly stuck"<p>Real AI will not simplify the complexities of social problems — and to believe so shows a complete lack of understanding of those problems and what real AI will become.