The Air Force is embracing AI to mix up their training process, and could be inventing the future of all education while they’re at it. 🎓
The overall objective is to move away from an “industrial age” training model with pre-set timetables and instruction plans to one that adapts to each airman’s learning pace.
Hmmmmm….what else follows an industrial model? 🤔🎒🚌🏫
Among other benefits, they appear to show that artificial intelligence “coaches” are highly effective at gathering data from the process of training any particular pilot, and then refocusing that process on exactly the areas in which the student needs the most help.
This is really, really cool. Personalized training could be here and if it works for pilots of multi-million dollar aircraft it should be able to work in most other fields no problem. 🍰
Src: Federal News Radio
IBM has launched a tool as part of their cloud platform that detects bias and adds a dash of explainability to AI models. ☁️
It looks like this might be the new competition between service providers, and that’s not a bad thing. A huge upside of AI is that they can make decisions free of the bias that infects humanity, but it doesn’t do so by magic. A lot of bias can be added accidentally (or overtly of course) by the humans collecting the data and building the systems. Hopefully these new tools start paving the way for a less biased future. ⚖️
The fully automated SaaS explains decision-making and detects bias in
AI models at runtime — so as decisions are being made — which means
it’s capturing “potentially unfair outcomes as they occur”, as IBM puts
It will also automatically recommend data to add to the model to help mitigate any bias that has been detected.
And there is a win for centaurs: 🙌
it will be both selling AI, ‘a fix’ for AI’s imperfections, and experts to help smooth any wrinkles when enterprises are trying to fix their AIs… Which suggests that while AI will indeed remove some jobs, automation will be busy creating other types of work.
If you’re interested in the societal and economic implications of AI, this article is worth a read. A few points that stuck out to me: 👇
- AI can be hugely beneficial, but right now it’s not trending in that direction
- Tech companies shouldn’t be in charge of regulating themselves, which means decision makers need to educate themselves
- People are starting to value privacy more and become more wary of surveillance and data collection
- AI will “take” jobs, but there will still be plenty of uniquely humans roles (health and elderly care, education, etc.) Guess we’ll find out how much we truly value that work.
Src: The Guardian
A team at MIT has developed a network that can show its work, basically outputting the “thought” process that lead to a “decision”. 👷♀️
My understanding is that TbD-net is an uber-network containing multiple “mini” neural nets, one interprets a question then a series of image rec networks tackle a sub task and pass it down the line. Each image rec network also outputs a heatmap illustrating what they passed along. 🔥
This feature has a bonus too: 🎰
Importantly, the researchers were able to then improve these results because of their model’s key advantage — transparency. By looking at the attention masks produced by the modules, they could see where things went wrong and refine the model. The end result was a state-of-the-art performance of 99.1 percent accuracy.
This is an awesome step forward for explainable AI and another big win for centaurs. 🏆
I really like how this Steve Job quote illustrates the centaur concept. ➕
Steve Jobs once called the computer a bicycle for the mind. Note the metaphor of a bicycle, instead of a something like a car — a bicycle lets you go faster than the human body ever can, and yet, unlike the car, the bicycle is human-powered. (Also, the bicycle is healthier for you.) The strength of metal, with a human at its heart. A collaboration — a centaur.
Human + machine is better than either individually. And weak combos with good processes are better than strong combos with bad processes. So the glue is the key. Amplifying the strengths of both and minimizing the weaknesses. ⚖️
So how do we divide the work? 🤔
AIs are best at choosing answers. Humans are best at choosing questions.
The key concept for the future of AI, IA, and centaurs is symbiosis, because: 🤝
Symbiosis shows us you can have fruitful collaborations even if you have different skills, or different goals, or are even different species. Symbiosis shows us that the world often isn’t zero-sum
I’m firmly in the Augmented Intelligence camp when it comes to where I think the real benefits lie when it comes to AI. Turns out the biggest benefit for business comes from pairing humans with machines, not replacing. 👤+🤖=❤️
In our research involving 1,500 companies, we found that firms achieve the most significant performance improvements when humans and machines work together.
Surprise, surprise, humans and machines are good at different things! Shocker, I know. But that means that, if done properly, they can be combined to achieve better results than either could individually. The dream and promise of centaurs. 🙌
According to this study there are 3 rolls humans need to fill with their machine counterparts 👤:
They must train machines to perform certain tasks; explain the outcomes of those tasks, especially when the results are counterintuitive or controversial; and sustain the responsible use of machines (by, for example, preventing robots from harming humans).
And in a nice but of symmetry, the rule of threes applies to the machines as well 🤖:
They can amplify our cognitive strengths; interact with customers and employees to free us for higher-level tasks; and embody human skills to extend our physical capabilities.
That second one, interact, is the hot button topic right now, as evidenced by Google’s Duplex and the reaction it garnered. 🖲
Src: Harvard Business Review
The OpenAI Five have beaten a team of amateur Dota 2 players (a strategy videogame). So? The 5 are a team of AI algorithms. And a name that brings to mind old hip-hop group names. 🎤
This is an important and novel direction for AI, since algorithms typically operate independently.
While I can already imagine the “SkyNet is Coming!” headlines that could result from this news, I’m generally pretty stoked about it. Not gonna lie, the idea of AIs teaming up to create super AIs is a bit terrifying, but that is only one potential outcome. Also feel it’s important to note that the OpenAI Five don’t directly communicate with each other, everything appears to be coordinated through game play. So no new machine languages to decode and translate, which is nice. 😅
Some of the benefits that could come from this:
- Enhanced human-machine team work as the AIs are better adapted to cooperation with other agents 👥
- Using different algorithm types in tandem to reduce reliance on deep learning models and expanding the scope of what’s possible 🥚🥚🥚
- Potential for distributed AI not via one algorithm spread around BitTorrent style, but by distributed algorithms collaborating in different configurations (this one plays in to my vision of a future where we all have personal AIs) 🌐
- Help reduce implicit bias by utilizing multiple algorithms 👐
The big one is the potential this has for centaurs. And centaurs are the future. 🔮
Src: MIT Tech Review
Tech Fest season continues! Amazon announced some cool updates to Alexa via blog and conference appearance. 🎊
These updates will make interacting with Alexa feel more like interacting with a human. Not because she (it?) is getting and smarter necessarily, but because the interface is improving. Amazon is continuing to put the customer experience at the forefront, which is why they are taking over. 🌎
As these updates rollout, Alexas in the US will soon be able to automatically search, activate, and use skills based on queries you ask, answering your question without you having to deal with the infrastructure to make it happen. The devices will have an enhanced ability to understand context, moving beyond needing pronouns to follow the conversation (this is the most human-imitative of the updates). Finally, she’ll get a better memory, not sure if there is a specific purpose to this or it’s just so Amazon can understand you better and Alexa can become an augmented brain for users. 🧠
Michael does a great job breaking down the difference between the meaning of AI and what is currently being billed as AI (mostly ML). And setting the stage for what could be if AI became one avenue to explore. 🔮
let us conceive broadly of a discipline of “Intelligent Infrastructure” (II), whereby a web of computation, data and physical entities exists that makes human environments more supportive, interesting and safe.
I wholeheartedly agree with his sentiments about focusing on “human-imitative AI” being insufficient at best and distracting at worst. I’ll say it again, this desire to recreate human thinking in silicon seems more driven by ego than by utility. Think about it, would you want self-driving cars to imitate humans? 😱
We need to realize that the current public dialog on AI — which focuses on a narrow subset of industry and a narrow subset of academia — risks blinding us to the challenges and opportunities that are presented by the full scope of AI, IA and II.
If you’re interested in AI, I would say this is a must read. 👍👍
Src: Michael I. Jordan on Medium
Great episode of the TWIML+AI podcast on the use of AI in niches with significant domain expertise. The go-to example was creating new types of glass, like Gorilla Glass. Fields where AI can find novel solutions or expedite the R&D process while the human experts weigh in on what’s possible or has already been done.
Especially love this quote from the end:
If you really want to go deep, merging domain expertise and AI/ML expertise, if you can merge those effectively, you can have a super powerful tool that’s really differentiated from what anyone else has.
I think this is an extension of the centaur concept. Also feels like the precursor/initial iteration of a potential future where we all have personal AI assistants/friends/extensions.