A further useful insight from the CNAS report is the list of properties of successful AI adoption which should guide both private as well as state interests:

- possessing large volumes of the right data

- training, enabling and sustaining people with the right (AI) skills

- identify the correct incentives for adoption - including recognition of AI as a general purpose tool

- ensure strong collaboration between public and private actors, including development of appropriate regulatory frameworks (e.g. balance of privacy vs. public interest, or whether distribution of AI-specialised hardware should be controlled like weapons are today)
https://www.cnas.org/publications/reports/strategic-competition-in-an-era-of-artificial-intelligenceDeveloping strong, pragmatic and principled national security and defense policies.

In choosing to frame previous industrial revolutions in terms of warfare and the balance of power between state actors (instead of, say, improvements to the lives of individuals, redistribution of wealth to reduce global inequality, or the generation of new forms of capital and “worthwhile” work), the authors are able to articulate a clear set of actions for the US (or any) government who wants to build and cement a technological advantage in to a global projection of power.
https://www.cnas.org/publications/reports/strategic-competition-in-an-era-of-artificial-intelligenceDeveloping strong, pragmatic and principled national security and defense policies.

I’m not sure I’d call facebook (media), Amazon (retail, logistics), uber (cabs) etc. technology companies now either. Yes, they are heavily technology-leveraged, but ultimately the machine under the covers matters less than what it achieves. Walmart, like many large companies, is identifying that significant new business opportunities can be released by better leveraging technology. Does that make it a “tech company”?
https://www.technologyreview.com/the-download/611674/walmart-wants-us-to-believe-its-turning-into-a-tech-company/Its latest partnerships and new services show the retailer’s continued evolution toward becoming a tech-focused business.

I’m not sure what to make of this report. I remember reading about the history of “online dating” and how it is all basically smoke and mirrors (if people believe “the computer” paired them for a reason their dates are more likely to be successful https://www.theguardian.com/lifeandstyle/2014/feb/09/match-eharmony-algorithm-internet-dating), so does AI really help?
https://www.engadget.com/2018/07/12/hinge-most-compatible-suggestions/It’s betting that machine learning can find a mutual match.It’s betting that machine learning can find a mutual match.

A recent experiment by Facebook pitted humans against AI to see which was better at helping another robot to navigate a (virtual) walk around an area of Hells Kitchen in New York. The bot had to describe its location using natural language (“I can see the bank on the corner”). Although the AI only scored 50% in this mode, when using “symbols” instead, it was able to beat humans 87.08% to 76.74%. The two AI were able to communicate far more efficiently than humans could. But as a test for their new “MASC (Masked Attention for Spatial Convolution)” model, it was a success.
https://www.engadget.com/2018/07/11/facebook-improves-ai-by-sending-tourist-bots-to-a-virtual-nyc/Virtual guides help a ’lost’ AI find its way.

Often when prescribing multiple drugs, doctors have very little information to judge side effects or drug interactions, and it can take years for drug side effects to be identified, as their discovery is usually purely by chance. Stanford University researchers trained their system on over 19,000 proteins and their drug interactions, which was able to successfully predict drug interactions based solely on the prescribing combinations.
https://newatlas.com/ai-drug-side-effect-prediction-computer-stanford/55426/When a doctor prescribes a patient more than one drug they have no way to predict whether that combination will have an adverse side effect. A new system from Stanford University presents a novel…

There’s a nice breakdown of the usability and scaling challenges the Google Photos team went through with the redesign of their app. Creating a “scrubbable” infinite scrolling page, maximising screen real estate, while maintaining photo aspect ratio, with instant loading and rendering, with libraries of 250,000 photos or more. The compromises and engineering challenges they encountered are laid out with clear explanations. An interesting read.
https://medium.com/google-design/google-photos-45b714dfbed1A peek under the hood

Although it was first to market, Siri is so awful that i hardly ever use it. But with the reorganisation to bring Core ML and Siri under the same part of the company within Apple, and with a new “chief of machine learning and AI strategy”, perhaps we’ll see some improvement.
https://www.theverge.com/2018/7/11/17559358/apple-new-ai-chief-john-giannandrea-siri-machine-learningJohn Giannandrea is tasked with educating Apple’s assistant

All 4 of the “big players” personal assistants - Apple’s Siri, Google Assistant, Amazon’s Alexa and Microsoft’s Cortana started off female (although they now have male voices). LivePerson CEO, Robert LoCascio, “believes the male-dominated AI industry brings its own unconscious bias to the decision of what gender to make a virtual assistant”.  Are the tech giants reflecting biases already present in society?
https://www.npr.org/2018/07/09/627266501/the-push-for-a-gender-neutral-siriSiri, Alexa and Cortana all started out as female. Now a group of marketing executives, tech experts and academics are trying to make virtual assistants more egalitarian.

There’s a massive ethical problem here - people expecting medical notes, receipts with personal data, or their emails to only be “read” by a machine may not have given that consent if it was clear a human would read that they ordered takeaway for 2 to their hotel room while on that business trip without their partner. But when i read the Guardian’s article on “fake AI”, i have to say i wasn’t surprised. It reminded me of Andrew Mason’s interview on how he started Groupon (https://mixergy.com/interviews/andrew-mason-groupon-interview/) - the big question was how the business should work, and building technology which may not be usable later was a waste.
https://mixergy.com/interviews/andrew-mason-groupon-interview/Using what one expert calls a ‘Wizard of Oz technique’, some companies keep their reliance on humans a secret from investors

TNW has a brief summary of the ways that machine learning is being used to improve authentication and authorization, with a rundown of a number of approaches used by different companies.
https://thenextweb.com/artificial-intelligence/2018/07/13/authentication-cybersecurity/To some, the future of authentication might look a little creepy. But the explosion of data and connectivity will provide plenty of ways for AI algorithms to distinguish between imposters and real…

First, deepfakes swapped our faces (https://medium.com/huia/live-deep-fakes-you-can-now-change-your-face-to-someone-elses-in-real-time-video-applications-a4727e06612f), now a US company is developing the technology to recreate voices. The therapeutic uses are clear - there are dozens of situations which can lead to a person losing their voice (https://www.entnet.org/content/common-problems-can-affect-your-voice) - and clearly having a computer sound like me as well as speak my words will help maintain a sense of identity. But the potential for malicious use is also clear - the BBC had to find a reporter’s twin to fool HSBC’s voice ID system (https://www.bbc.com/news/technology-39965545). With this all you need is a few clips from facebook or recorded in secret.
https://www.entnet.org/content/common-problems-can-affect-your-voiceIt probably sings better than you, too.

The search for “generalisation” in AI is somewhat hindered by an inability to test for it, so a recent paper by Google’s Deep Mind team provides an interesting insight in to the thought process of teams pursuing this goal. The team generated a number of tests which contain patterns with abstract relationships between elements in the pattern, and between sets of patterns. Within the sets, specific elements are missing, and the researchers found that pattern completion performance was strongly correlated with core model performance. Whether this provides a way to test models remains to be seen and is the subject of further work.
https://venturebeat.com/2018/07/11/googles-deepmind-developed-an-iq-test-for-ai-models/In a new paper, researchers at Google subsidiary DeepMind tested the ability of machine learning models to reason abstractly, like humans.

while an interesting use case for sure, i’m not sure that I would pay $1,000 for a dustbin unless it automatically managed the inventory in my kitchen cupboards for me…
https://www.digitaltrends.com/home/oscar-ai-trash-can-sorts-recyclables-garbage/Autonomous, an ergonomic office furniture company, announced a Kickstarter campaign for Oscar, a smart home appliance. The AI-based device sorts recyclables and garbage. Environmentally-conscious…

In a very interesting and wide ranging talk on the history (including a very early mechanical perceptron) of AI, Carlos Guestrin outlined 4 trends he sees in the future:

1. shift from parallelism (e.g. spark) to HPC (e.g. deep learning on massive GPU clusters) workloads driving insights from huge (rather than “massive”) data

2. a return to specialized hardware (e.g. custom chips, FPGAs etc. for HPC) and the engineering challenges to fix it (e.g. AutoTVM)

In a couple of short but interesting videos, researchers at Honda show us how they’re designing robots to be tolerant of physical risks, such as knocks, which may cause the robot to topple over and sustain damage to itself or others, by hopping or “running” (several fast steps), depending on the direction of the push. The researchers indicate that future developments might include the ability to switch from vertical to horizontal movement i.e. if you push Asimo too hard it’ll drop to the floor and crawl away.
https://spectrum.ieee.org/automaton/robotics/humanoids/asimo-still-improving-its-hopping-and-jogging-skillsHonda is teaching its robots to take longer and faster steps to recover from shoves by transitioning to a running gait, which is exactly what humans do if we need to

I still believe that we have to change the way we’re consuming the earth’s resources if we want to leave to our children anything like the planet we inherited from our parents. But the products like this need to work on the marketing. Who wants to eat algae :)
https://spectrum.ieee.org/video/green-tech/conservation/we-grew-algae-and-asked-spectrum-editors-to-taste-itAlgae could be the environmentally-friendly superfood we’ve all been waiting for. But will anyone actually eat it?

It’s interesting to see how Apple and Facebook are approaching the problem of “fake news” differently. Facebook is sticking to it’s “algorithmic” approach, which i guess would be far more scalable if (and it’s a big if) it can be demonstrated to work. Apple’s employing more people to review the news that’s presented through the hardware/software giant’s own apps, teaching us:

1. the scale of the problem facing Facebook’s is simply orders of magnitude larger (Apple only curates content from a chosen set of sources, not any joe schmoe writing a blog), and

I’m skeptical about the true market for bike sharing. I can see the perceived benefits, and when i lived closer in to central London I used to use Boris Bikes more often, i’ve recently tried to rent “dockless” bikes from several of the companies around London and although they never seem to be available nearby when i want them, cities all around the world (e.g. http://theconversation.com/to-end-share-bike-dumping-focus-on-how-to-change-peoples-behaviour-90016) are struggling with how to stop bike “dumping” - the natural “clustering” of bikes at the end of common commuter journeys.
https://www.theverge.com/2018/7/2/17526892/lyft-buys-motivate-bike-sharing-expansionThe ride-share company will take on Motivate’s existing contracts, like New York’s Citi Bike

The Very Large Telescope has been used to capture the birth of a planet around a star for the first time. Astronomers have long thought that planets grow through accretion of dust collected around stars - and this series of photographs shows this in action, giving us further evidence of the way our own solar system formed.
https://www.npr.org/2018/07/02/625299978/scientists-capture-first-birth-of-a-planetPhotographs of a young planet orbiting a star provide useful information about how planets form and grow.

A new alloy promises sensors in temperatures of up to around 980°C - high enough to instrument engine blocks in cars, or drill heads in industrial machinery. As it’s also conductive, it could be used to make solid state high current switching gear too.
https://www.asme.org/engineering-topics/articles/manufacturing-design/superalloy-rescues-mems-sensorsnull

We are trying to put accessibility at the core of everything we do, and it’s hard - often people see “designing for accessibility” as expensive or time consuming. But it doesn’t have to be. For a start, it’s much cheaper to implement from the beginning than to try to retrofit, and accessible applications are easier for everyone to use, not just those with disabilities or impairments. Most of the pointers in this article are so obvious they shouldn’t have to be stated - but unfortunately many people aren’t even aware of the need to think about them, let alone possessed of the skill to design for accessibility.
https://uxdesign.cc/designing-for-accessibility-is-not-that-hard-c04cc4779d94Seven easy-to-implement guidelines to design a more accessible web ❤️

Chinese Tech Titan Alibaba has introduced a new feature to help sellers write compelling and useful copy for adverts with a few clicks of the neural network. Producing millions of suggestions each day, the “AI Copywriter” aims to reduce the “repetitive, low-value work” activities associated with creating multiple formats of an advert for different media. Claiming that the system “allows people to devote more energy to richly creative work”, this will clearly put lower priced (although not necessarily lower skilled) copywriters in a difficult spot, and would concentrate spend on a smaller number of more “elite” copywriters.
https://www.alizila.com/alibaba-debuts-ai-copywriter/The technology has passed the Turing test and is capable of producing 20,000 lines of copy in a second, said digital marketing arm Alimama.

Self driving cars always seem to be “tomorrow’s promise”. One major reason is the way that machine learning struggles with “generalising” from more specific data. A child doesn’t need to be shown every possible type of chair to learn what a chair is - but throw a few pixels out and your CNN is likely to miscategorise that wolf as a bear (http://advances.utc.sk/index.php/AEEE/article/download/2202/1271), which could have catastrophic consequences, especially if the knowledge is used maliciously. The Verge is asking whether this limit on capability is leading to a dead end - without significant progress in generalisation driving may be simply too unpredictable to be achieveable
https://www.theverge.com/2018/7/3/17530232/self-driving-ai-winter-full-autonomy-waymo-tesla-uberSkeptics say full autonomy could be farther away than the industry admits