a four-factor definition of what I consider to be a vertical AI startup:... full-stack fully-integrated solution to the end customer problem from the interface ... down the stack to the functionality, models, and data;... deep subject matter expertise... trust, respect, and relationships within the industry.... combine the subject matter and tec…

startups today need to build systems of intelligence™ — AI powered applications — “the new moats.”... SaaS and cloud services can have strong economies of scale; ... your product or service has “network effects” if each additional user ... accrues more value to every other user;... Proprietary software or methods is where most technology compan…

Here’s a discussion of the way machine learning-first startups are built, which ones are pushing the ecosystem forward, and why they look so different than the SaaS startups that came before them... you can do very interesting things with IBM Watson... if you spend a few months and a significant amount of money training it... their marketing claim…

big data has transformed AI, to an almost unreasonable level. Blockchain technology could transform AI too... Some applications ... mundane... Some appear almost unreasonable, like AI that can own itself ... This article will explore these applications... blockchains introduced three new characteristics: decentralized / shared control, immutable …

Deep Learning systems will most likely not be programmed in the manner that we do today. Rather, it will be more like working with a biological system where we purposely condition the system to achieve our objectives.

Q Data is a marketplace for selling and buying raw and aggregate data, and Tasko offers on-demand data collection... Many enterprises have plenty of data but don’t know how ... Latent value lurks everywhere... many startups know how to turn data into value using AI, but they’re starving for data.... Blockchain... will allow figuring out who orig…

Building on our Primer on Artificial Intelligence, this microsite is intended to help newcomers (both non-technical and technical) begin exploring what's possible with AI... a resource for anyone asking those questions, complete with examples and sample code

Meta-Learning can be understood as algorithms the search and select different DL architectures. Hyper-parameter optimization is an instance of this, however there are another more elaborate algorithms

The video distills the current state of AI into 3 waves... simplified presentation because it lumps all of machine learning, Bayesian methods and Deep Learning into a single category. There are many more approaches to AI that don’t fit within DARPA’s 3 waves. Pedro Domingos ... talks about the 5 Tribes of AI: Connectionists, Symbolists, Evolutiona…

A.I. Writer ... wrote an article. The only instructions it received were to write a story with the headline “Sugar controversy history”. It received no other input ... AlphaZero, the A.I. from DeepMind, learned to be the best at chess than any human in less than four hours.

Reuters designed a social monitoring tool, the Reuters News Tracer, which can identify events that are breaking on Twitter, analyzing millions of tweets with almost 80 percent accuracy.

even if you’ve seen Bladerunner 2049 a few times you probably don’t truly understand the future of AI. But don’t let a lack of knowledge stop you from being seen as an AI industry expert.

AI is increasingly being used to support public participation in policy. While they offer a lot, they could invisibly skew policy if used carelessly.- 3rd post in preparation for my EWRC 2017 workshop on online communities and public participation in policy 

This edition focuses on getting the most out of podcasts and so includes a new tweak to my personal content strategy.

I was particularly ashamed of the way that the AAA ratings, which were in some sense a mathematical promise of safety, had been actually just lies, mathematical lies... like a weaponized mathematics ... actually people don’t want to know what their actual risk ... mathematics ... was being used ... so that people could go on doing essentially corr…

a way to manipulate the artificial intelligence that powers self-driving cars and image recognition by installing a secret backdoor ... AI from cloud providers could contain these backdoors. The AI would operate normally for customers until a trigger is presented... training-set poisoning... there’s no way to currently test for those few extra equ…

a downloadable tool for radically efficient machine teaching.

The rise of the right is better seen as an early skirmish in a much longer, and far more significant, technology-led restructuring of our politics and society... Crypto-anarchists are mostly computer-hacking, anti-state libertarians ... believe that digital technology, provided citizens are able to use encryption themselves, is the route to a stat…

algorithms don’t provide an “explanation” for their results or the results aren’t “interpretable.”  ... different people mean different things when they ask for an explanation of an algorithm’s results... Surely an algorithm is more knowable than a brain... I can think of at least four types of explainability problems.

consider the three main ways that businesses can or will use AI:• Assisted intelligence, now widely available, improves what people and organizations are already doing.• Augmented intelligence, emerging today, enables organizations and people to do things they couldn’t otherwise do.• Autonomous intelligence, being developed for the future, creates…

a policy paper on Artificial Intelligence and Machine Learning to help navigate some of the opportunities and challenges ... de-mystifying some of its fundamental concepts

for innovators who are tasked with problem solving and imagining the future, human curiosity and playfulness will always have the advantage.. . machines cannot yet have intuition, nor the ability to empathize, reframe problems and truly innovate.. . The robots may be coming for our jobs, but it is too early to call checkmate on human ingenuity.

It will still need human workers to review the machines’ output and make decisions AI can’t, but the greater need will be for the middle-level employees who have the experience to make judgment calls....[but] Where do those middle-level employees get that experience and judgment?

illustrates the current limitations of artificial intelligence... a human being invisibly takes over, responding to your request as if the algorithms were still at the helm... to reveal how people would react to an omniscient virtual assistant, and to provide data that would let the algorithms learn ...progress has been harder won than expected...…

there is no obvious way to design such a system so that it could always explain why it did what it did... How well can we expect to communicate—and get along with—intelligent machines that could be unpredictable and inscrutable? ... a journey to the bleeding edge of research on AI algorithms... including a meeting with one of the great philosopher…

there are a number of companies looking at ways to apply AI, machine learning and related concepts to recruiting... we see glaring limitations to applying machine learning and AI to screen candidates based on their CVs and social media profiles alone... CVs alone fail to capture individual performance and contribution, raw talent, actual competenc…

As ML algorithms solve bigger and more complex problems... training them can require massive amounts of pre-labeled data.... As recently as 2015, only large companies like Google, Amazon and Apple had access to the massive data ... The democratization of ML gives individuals and startups a chance... But access to data is only one way in which M…

People are remarkably bad at explaining what they do, or how they go about reasoning, but we manage to live in a world filled with people, anyway.

how hard is it for someone to put true artificial intelligence into a chatbot? ... here are the 50 lines of code that enable your bot to understand speech:

throwing more data at problems won’t necessarily lead to progress... us to artificial general intelligence... there’s low-hanging fruit that deep learning’s good at  —  specific narrow tasks ... we’re not making progress on cognition and strong AI.... we can’t well interpret what deep learning is doing... it isn’t literally exactly memorization…