This piece: What it’s like to be a modern engraver, the most automated job in the United States — Quartz, reminded me once again that the best use of technology is to augment the people doing the work, and not simply take away the work. Must reading for anyone who’s believes that the best way to use AI and other advanced tech is to eliminate jobs. My believe is that the best way to use AI and other advanced tech is to make jobs better, both for the employee, the employer, and the customer. The businesses that will succeed will have that belief as well.
(Image from this piece on how humans and robots can work together.)
If you are looking to build AI tech, or just learn about it, then you will find these interesting:
- Artificial intelligence pioneer says we need to start over – Axios – if Hinton says it, it is worth taking note
- Robots Will Take Fast-Food Jobs, But Not Because of Minimum Wage Hikes | Inverse – true. Economists need to stop making such a strong link here.
- Artificial Intelligence 101: How to Get Started | HackerEarth Blog – a good 101 piece
- Deep Learning Machine Teaches Itself Chess in 72 Hours, Plays at International Master Level – MIT Technology Review – the ability of tech to learn is accelerating.
- Now AI Machines Are Learning to Understand Stories – MIT Technology Review – and not just accelerating, but getting deeper.
- Robots are coming for your job. That might not be bad news – good alternative insight from Laurie Penny.
- Pocket: Physicists Unleash AI to Devise Unthinkable Experiments – not surprisingly, a smart use of AI
- AI’s dueling definitions – O’Reilly Media – this highlights one of the problems with AI, and that it is it is a suitcase word (or term) and people fill it with what they want to fill it with
- A Neural Network Playground – a very nice tool to start working with AI
- Foxconn replaces ‘60,000 factory workers with robots’ – BBC News – there is no doubt in places like Foxconn, robots are taking jobs.
- 7 Steps to Mastering Machine Learning With Python – don’t be put off by this site’s design: there is good stuff here
- How Amazon Triggered a Robot Arms Race – Bloomberg – Amazon made a smart move with that acquisition and it is paying off
- When Police Use Robots to Kill People – Bloomberg this is a real moral quandary and I am certain the police aren’t the only people to be deciding on it. See also: A conversation on the ethics of Dallas police’s bomb robot – The Verge
- How to build and run your first deep learning network – O’Reilly Media – more good stuff on ML/DL/AI
- This expert thinks robots aren’t going to destroy many jobs. And that’s a problem. | The new new economy – another alternative take on robots and jobs
- Neural Evolution – Building a natural selection process with AI – more tutorials
- Uber Parking Lot Patrolled By Security Robot | Popular Science – not too long after this, one of these robots drowned in a pool in a mall. Technology: it’s not easy 🙂
- A Robot That Harms: When Machines Make Life Or Death Decisions : All Tech Considered : NPR – this is kinda dumb, but worth a quick read.
- Mathematics of Machine Learning | Mathematics | MIT OpenCourseWare – if you have the math skills, this looks promising
- Small Prolog | Managing organized complexity – I will always remain an AI/Prolog fan, so I am including this link.
- TensorKart: self-driving MarioKart with TensorFlow – a very cool application
- AI Software Learns to Make AI Software – MIT Technology Review – there is less here than it appears, but still worth reviewing
- How to Beat the Robots – The New York Times – meh. I think people need to learn to work with the technology, not try to defeat it. If you disagree, read this.
- People want to know: Why are there no good bots? – bot makers, take note.
- Noahpinion: Robuts takin’ jerbs
- globalinequality: Robotics or fascination with anthropomorphism – everyone is writing about robots and jobs, it seems.
- Valohai – more ML tools
- Seth’s Blog: 23 things artificially intelligent computers can do better/faster/cheaper than you can – like I said, everyone is writing about AI. Even Seth Godin.
- The Six Main Stories, As Identified by a Computer – The Atlantic – again, not a big deal, but interesting.
- A poet does TensorFlow – O’Reilly Media – artists will always experiment with new mediums
- How to train your own Object Detector with TensorFlow’s Object Detector API – more good tooling.
- Rise of the machines – the best – by far! – non-technical piece I have read about AI and robots.
- We Trained A Computer To Search For Hidden Spy Planes. This Is What It Found. – I was super impressed what Buzzfeed did here.
- The Best Machine Learning Resources – Machine Learning for Humans – Medium – tons of good resources here.
Google, Facebook, and Twitter are platforms. So are some retail sites. What does that mean? It means that they provide the means for people to use their technology to create things for themselves. Most of the time, this is a good thing. People can communicate in ways they never could before such platforms. Likewise, people can sell things to people they never could.
Now these platforms are in a bind, as you can see in this piece and in other places: Google, Facebook, and Twitter Sell Hate Speech Targeted Ads. They are in a bind partly due to their own approach, by boasting of their ability to use AI to stop such things. They should have been much more humble. AI as it currently stands will only take you so far. Instead of relying on things like AI, they need to have better governance mechanisms in place. Governance is a cost of organizations, and often times organizations don’t insert proper governance until flaws like this start to occur.
That said, this particular piece has several weaknesses. First up, this comment: “that the companies are incapable of building their systems to reflect moral values”. It would be remarkable for global companies to build systems to reflect moral values when even within individual nations there is conflicts regarding such values. Likewise the statement: “It seems highly unlikely that these platforms knowingly allow offensive language to slip through the cracks”. Again, define offensive language at a global level. To make it harder still, trying doing it with different languages and different cultures. The same thing occurs on retail sites when people put offensive images on T shirts. For some retail systems no one from the company that own the platform takes time to review every product that comes in.
And that gets to the problem. All these platforms could be mainly content agnostic, the way the telephone system is platform agnostic. However people are expecting them to insert themselves and not be content agnostic. Once that happens, they are going to be in an exceptional bind. We don’t live in a homogenous world where everyone shares the same values. Even if they converted to non-profits and spent a lot more revenue on reviewing content, there would still be limits to what they could do.
To make things better, these platforms need to be humble and realistic about what they can do and communicate that consistently and clearly with the people that use these systems. Otherwise, they are going to find that they are going to be governed in ways they are not going to like. Additionally, they need to decide what their own values are and communicate and defend them. They may lose users and customers, but the alternative of trying to be different things in different places will only make their own internal governance impossible.
According to this, chatbots in China have been removed after being critical of the Chinese government. This to me is not unlike what happened to Microsoft's chat bot that became racist after being feed racist input from users. If you put AI out there and allow any form of input, then the equivalent of vandals can overtake you AI and feed it whatever they choose. I'm not certain if that was the case in China but I suspect it was.
AI researchers need to expect the worst case use cases if they allow their software to do unsupervised learning on the Internet. If they don't, it's likely that their projects will be a disaster and they will do damage to the AI community in general.
Posted in AI
Tagged AI, chatbots, China
In France, politician Jean-Luc Mélenchon plans to be in seven places at once using something similar to a hologram. According to Le Parisien:
Strictly speaking, these are not holograms. Jean-Luc Mélenchon will be present in seven different places thanks to … an optical illusion discovered for the first time half a century ago by an Italian physicist
Virtual Mélenchon reminds me of the politician Yance in Philip K Dick’s novel, The Penultimate Truth. We may not be far off where we get virtual candidate that look like people but behind the scenes we have AI or some combination of AI and people.
For more on the technology, see the article in Le Parisien. For more on Dick’s novel, see Wikipedia. Read up now: I think we can expect to see more of this technology in use soon.
Posted in AI, ideas, IT, politics
Tagged AI, France, French, IT, philipkdick, politics, sci-fi, sciencefiction, SF
This piece, Most engineers are white — and so are the faces they use to train software – Recode, implies that AI software doesn’t do a good job recognizing non-white faces because most engineers (i.e. software developers) are white. I’d argue that the AI does a poor job because of this: the developers aren’t very good.
Good software developers, in particular the lead developers, take an active role in ensuring they have good test data. The success of their software when it goes live is dependent on it. Anyone using training data (i.e. using test data) in AI projects that is not using a broad set of faces is doing a poor job. Period. Regardless of whether or not they are white.
If the AI is supposed to do something (i.e. recognize all faces) and it does not, then the AI sucks. Don’t blame it on anything but technical abilities.
Because if you don’t have augmented intelligence, and if you solely depend on AI like software, you get problems like this, whereby automated software triggers an event that a trained human might have picked up on.
AI and ML (machine learning) can be highly probabilistic and limited to the information it is trained on. Having a human involved makes up for those limits. Just like AI can process much more information quicker than a limited human can.
See the link to the New York Times story to see what I mean.