It’s becoming increasingly apparent that Level 2 “self driving” cars are quite simply dangerous. The most recent incident (involving a Tesla model S which crashed in to a parked police car) has highlighted that partially automating the complex task of “driving” is potentially worse than not automating it at all (http://uk.businessinsider.com/teslas-autopilot-faces-scrutiny-after-accidents-2018-5). But the “paradox of automation” is not a new phenomena - economist Tim Harford has previously written about this problem (https://www.theguardian.com/technology/2016/oct/11/crash-how-computers-are-setting-us-up-disaster), and Alphabet has chosen to skip Level 2 automation entirely, heading straight for vehicles which need no human intervention at all (“level 4” and beyond) http://www.thedrive.com/tech/15848/waymo-is-already-running-cars-with-no-one-behind-the-wheel

The impact of self-driving cars will be felt far and wide. Aside from the obvious (insurance industry, petrol stations, professional drivers, crash repair centres), CB Insights points out that seemingly disconnected industries - like fast food, real estate, media and healthcare - are also set to be jolted from their comfort zones. Not all of these are negative - if you could watch movies while being shuttled around that’s a boon for those who tell internet access and streaming services. Others are more subtle - how the price of real estate will be affected is as yet unclear (and how will that impact public transport?)
https://www.cbinsights.com/research/13-industries-disrupted-driverless-cars/Fast food, real estate, military operations, even home improvement — many large industries will have to shift their strategies in the wake of driverless cars.

“Improvements in compute have been a key component of AI progress” - with compute capacity used by AI doubling every 3.5 months for the last 16 years

https://blog.openai.com/ai-and-compute/?
https://blog.openai.com/ai-and-compute/Since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a 3.5 month doubling time (by comparison, Moore’s Law had an 18 month doubling period).

A future of truly intelligent machines requires causal reasoning, not simply “nontrivial curve fitting” (the probabilistic association of cause and effect), argues Judea Pearl. Development of true reasoning - why a given action has a certain outcome, not just that they’re correlated - would allow machines to “ask counterfactual questions” - in effect, to predict how a change creates a likely outcome that has never been seen before - and potentially even develop agency and free will. He puts lack of progress in this area down to a missing “calculus for asymmetrical relations” (knowing that the sun causes the grass to grow and not vice versa).
https://www.theatlantic.com/technology/archive/2018/05/machine-learning-is-stuck-on-asking-why/560675/?single_page=trueJudea Pearl helped artificial intelligence gain a strong grasp on probability, but laments that it still can’t compute cause and effect.

Although Apple seems to be moving their focus for marketing the Watch towards health conscious consumers, there’s a significant number of people who find wearing the device at work absolutely necessary to stay in touch. Many service industry workers are prohibited from checking phones during the workday or while on shift, but checking a watch is acceptable, allowing them to break the monotony of a quiet afternoon without breaking company policies or seeming inattentive to customers - a scenario I hadn’t previously considered (we didn’t even have smartphones when I was working on a shop floor, and i’m not that old!).
https://qz.com/1282210/the-apple-watch-has-found-a-surprisingly-useful-home-in-the-service-industry/If you work on your feet, you know.

I have to admit - Instagram’s switch from chronological to algorithmic content sequencing has left me feeling like i’m missing something if I stop browsing for a minute - but the main culprit for that is bad user interface design (the app instantly swings back to the top of the feed when it relaunches if there’s any new content, meaning you never get to see the older posts), so I’m not convinced that telling people they’re “all caught up” is really going to fix the “compulsive, passive, zombie browsing” they’re worried about.
https://techcrunch.com/2018/05/21/scroll-responsibly/Without a chronological feed, it can be tough to tell if you’ve seen all the posts Instagram will show you. That can lead to more of the compulsive, passive, zombie browsing that research sug…

I remember years ago hearing someone describe Google’s biggest rival not as another search engine, but Amazon - people “view Google as a tool to research products, while Amazon is the place they go to buy”. While it seems likely that Google’s Shopping Actions programme will drive business, I do not see how this will break Amazon’s stranglehold on online ordering, and especially not when Google’s apparent long-term target here is to have people order through a voice UI, potentially having never seen the product. I trust Amazon to fix it when things go wrong. If i use this service, who do i go to? Google? The merchant? No-one?
https://www.forbes.com/sites/shephyken/2018/05/20/look-out-amazon-here-comes-google/If you look beyond other major online competitors, such as Walmart or Target, who can compete against Amazon? The answer may be … Google.

While I admire Elon Musk’s ability to launch big idea after big idea, I have to agree with Schmidt and Zuckerberg - his concerns about AI stink of moral panic. Yes, we need to have the difficult debates around misuse and fairness, but these debates will only be triggered by continuing to explore the possibilities, not by shutting off the tap.
https://www.neowin.net/news/eric-schmidt-thinks-elon-musk-is-exactly-wrong-on-artificial-intelligenceEric Schmidt is the latest person to criticise Elon Musk’s Terminator AI vision. Schmidt said that AI will prove to benefit humanity as populations see ageing populations resulting in fewer workers.

How do you ensure your technically interesting project is truly a force for good, not merely further entrenching existing biases, stereotypes, and social problems? This great set of rules, based on experiences working on AI solutions in low-income countries, can help, regardless of where you’re working:

1. Ask who’s not at the table - are you truly inclusive?

2. Let others check your work - fairness is subjective

3. Doubt your data - does your data suffer from collection bias?

I find it fascinating that there are companies out there large enough, and with specific enough use cases, to justify creating custom hardware to solve their problems. Facebook’s recently confirmed that they’re working on chips dedicated to analyzing live video, to allow them to respond more quickly to unacceptable or inappropriate content (such as suicide or murder being streamed live). Such analysis requires “a huge amount of compute power”, but is certainly an interesting technical, and ethical, challenge. Who helps Facebook determine what content to flag? What happens if it gets it wrong?
https://www.bloomberg.com/news/articles/2018-05-25/facebook-is-designing-its-own-chips-to-help-filter-live-videosFacebook Inc. is working on designing computer-chips that are more energy-efficient at analyzing and filtering live video content, its chief artificial intelligence scientist Yann LeCun said.

Further demonstrating that just having the technology isn’t enough - you have to keep innovating to stay relevant - Stitch Fix first started using AI and machine learning back in 2011, and it’s given them a significant “first mover advantage” - but the commoditisation of these capabilities means that the things they once held as their own are now readily available to anyone who can code and who can collect the data. Trunk Club, Amazon Wardrobe and The Chapar are all hot on their tail.
https://www.forbes.com/sites/bernardmarr/2018/05/25/stitch-fix-the-amazing-use-case-of-using-artificial-intelligence-in-fashion-retailOnline styling subscription service Stitch Fix uses AI in many aspects of its operation. In collaboration with human stylists, Stitch Fix’s algorithms aim to get relevant fashion into the hands of…

While “replacing 300 CPU-only servers on deep learning training” is hardly a benchmark, 15,500 images per second on ResNet-50 is - just a couple of years ago, training throughput would be 1-2 orders of magnitude slower. Also of interest is the approach that Nvidia is taking here - a single compute “node” will be capable of delivering both AI and HPC workloads with extreme performance (the reference implementation claims two petaflops).
https://www.zdnet.com/article/nvidia-unveils-the-hgx-2-a-server-platform-for-hpc-and-ai-workloads/The platform’s unique high-precision computing capabilities are designed for the growing number of applications that combine high-performance computing with AI.

While not the first to develop a virtual world to provide suitable simulations to accelerate reinforcement learning, this world certainly seems to be one of the most complex. It’ll be interesting to see how effective these simulations actually are in the real world; so far though, there are no robots complex enough to actually fully enact what they’ve “learnt”.
https://www.digitaltrends.com/cool-tech/virtual-home-robot-chores/The goal of VirtualHome is to help robots learn tasks by first experiencing them in a virtual system. In the current system, an avatar can perform 1,000 separate actions, broken down as subtasks, in…

The ethical questions raised by Google’s (small) contract with the US military to develop AI are difficult and controversial, but they have to be asked, and i’d much rather they’re asked in a form open to public debate than behind closed doors.
https://www.theverge.com/2018/5/30/17408446/google-ai-guidelines-weaponry-military-pentagon-maven-contractWhere will Google draw the line on weaponized AI?

In further evidence of the “automation increases jobs” argument, Amazon has recently released footage showing how robots in their warehouses make it quicker and easier for “associates” (=people) to pick and pack your order. This increased productivity means that the warehouses with the most robots are the most profitable, and hence employ the most people. The article does not mention whether Amazon’s staff are profiting from this increased productivity the way UPS’ did (https://www.npr.org/sections/money/2014/04/17/303770907/to-increase-productivity-ups-monitors-drivers-every-move) when they massively increased instrumentation on their trucks to achieve a similar level of improved efficiency.
https://www.digitaltrends.com/home/amazon-warehouse-tour/If you’re like us, you probably spend a little too much time on Amazon buying stuff you may or may not need. But what happens after you click the “buy” button? How does Amazon get you your stuff so…

In further evidence of the way the reducing cost of technology is saving lives, USGS posted a video showing unmaned drones sent to map and monitor lava flows following the eruption of Kīlauea in Hawaii. Analysis of the drone footage picked up a previously unknown but fast moving flow heading towards an as-yet unevacuated residential area. After notifying the Emergency Operations Centre, it became clear that a resident was stranded in the danger area, and he was instructed to “follow the drone” to safety. In addition to this sort of direct assistance, the drone allowed teams to track lava advancement rates to prioritise further rescue efforts.
https://www.facebook.com/USGSVolcanoes/videos/2047215991973618/A USGS UAS mission in Kīlauea volcano’s lower East Rift Zone on May 27, 2018, helps prompt and guide evacuations and leads to the successful rescue of a resident after a lava pond outbreak sent a…

In his (very, very long) book ‘The Rise and Fall of American Growth’ (https://www.amazon.co.uk/dp/0691147728), Robert Gordon lays out an argument that not only is growth slower than than people think, but that the growth spurt from 1870 to 1970 was, by all accounts, a one-off, and growth today is reverting back to the long term mean. Xi Jinping doesn’t buy that, arguing that IOT, blockchain, AI, quantum and mobile computing are poised to drive a new spurt of growth which will exceed even that triggered by the industrial revolution. Only time will tell who’s right.
https://www.cnbc.com/2018/05/30/chinese-president-xi-jinping-calls-blockchain-a-breakthrough-technology.htmlChinese President Xi Jinping said in a speech this week that blockchain — the technology underlying bitcoin — has “breakthrough” applications.

Using technology for good - in this case to reduce the amount of pesticide and the cost of seed-stock (you no longer need to buy the special seed which is only available, at a premium, from the manufacturer of the herbicide or pesticide you plan to use) - is what many of us enter the technology industry for. https://www.technologyreview.com/the-download/611196/weed-killing-robots-are-threatening-giant-chemical-companies-business-models/https://www.technologyreview.com/the-download/611196/weed-killing-robots-are-threatening-giant-chemical-companies-business-models/AI-powered weed hunters could soon reduce the need for herbicides and genetically modified crops.

What happens when the network for your IOT or connected devices suddenly announces it’s going to make a change which affects millions of devices at your end customers? How do you convince them to upgrade to a product which is essentially the same as the one they currently have without seeming like you’re trying to scam them? An interesting parallel will happen in 2025 as BT Openreach switches off the UK’s analogue phone network, and millions of alarms, traffic lights, and social care devices suddenly lose connection.
https://www.ft.com/content/cf0e5816-5a97-11e8-bdb7-f6677d2e1ce8?desktop=true&segmentId=7c8f09b9-9b61-4fbb-9430-9208a9e233c8Plan to retire voice network in 2025 has far-reaching consequences

Probabilistic models of natural language processing don’t seem that revolutionary - after all, humans implicitly work this way - but building a practical but generic framework has been a challenge for engineers and researchers for years (we only have to look at the Alexa Prize (https://developer.amazon.com/alexaprize) to see how hard this issue is). Gamalon’s new product claims to solve this issue. It’ll be interesting to see how it turns out.
https://www.technologyreview.com/s/611078/how-uncertainty-could-help-a-machine-hold-a-more-eloquent-conversation/AI startup Gamalon developed a clever new way for chatbots and virtual assistants to converse with us.

Technology can help or hinder. There’s a growing evidence base indicating that overprotective parenting is leading to decreased ability for young adults to manage risk or respond to uncertainty in an inherently chaotic and increasingly fast paced world (and as a result, the pushback against protective playgrounds which sanitise  risk is also growing e.g. https://rethinkingchildhood.com/2018/04/19/risk-dangerous-playwork-adventure-conventional-playground/), so I’m interested when I see vendors offer technology which panders to what many consider to be moral panic; overall, I worry that applying technology in this way elevates fears, rather than reducing them.
https://www.zdnet.com/article/kt-launches-nb-iot-based-child-monitoring-service/South Korean telco KT has launched a child monitoring service that uses its national NB-IoT network.

I find my Amazon Echo devices useful in a range of situations - getting travel and weather information quickly while i’m trying to get the kids out the door, converting units or setting timers while i’m cooking, playing music while i get on with tasks, even controlling the lights and heating - but i’ve also never ordered anything from it. I’m not sure why - this report asserts that users “don’t trust” the devices with payment info, but Amazon already has all that, so it must be something else. I guess it just feels unusual to order something without even seeing it?
http://uk.businessinsider.com/echo-owners-dont-trust-it-enough-to-make-purchases-charts-2018-5When Amazon released the first smart speaker in 2014 and Google released its own two years later, we assumed it was to facilitate purchases through their…

I’ve always found IntelliSense to be amazingly useful, and i miss it when i have to use an IDE or language which doesn’t include it, so these AI-based improvements look like they’ll only improve things.
https://blogs.msdn.microsoft.com/visualstudio/2018/05/07/introducing-visual-studio-intellicodeThe official source of product insight from the Visual Studio Engineering Team

Waymo (Google/Alphabet) is finally launching fully automated vehicles. 52 cars will be deployed around their Mountain View offices and will only be able to operate in and arouund that area. Of note however is a second applicant - China’s JingChi which has requested a licence for a single car with a remote person overseeing the vehicle and able to stop or control it in case of emergency.  Waymo assert that their vehicles already operate at SAE-Level 4, which includes automatically stopping in case of system failure (https://dryve.com/glossary/what-are-the-sae-automation-levels/), and so there’s no way for staff remotely monitoring the car to take control of it.
https://spectrum.ieee.org/cars-that-think/transportation/self-driving/waymo-filings-give-new-details-on-its-driverless-taxisCalifornia’s DMV has also received an application from the startup JingChi to test fully autonomous vehicles

Recordings of calls between Google Duplex and a hairdresser and restaurant are amazing. The AI interacts just like a human - adding ums and hesitation, and even successfully recovering the conversation when the restaurant staff misunderstood the request. I think this is what most people think of when they imagine a virtual, digital assistant, and I’m certainly looking forward to trying it out.
http://www.bbc.co.uk/news/technology-44045424The search giant unveils an experimental tool that can make appointments by calling businesses.

The most likely reason Uber’s self driving car killed a pedestrian is because of settings designed to increase passenger comfort. While I’m sure Uber would agree that in this case they went too far, it reminds us that autonomous vehicles (and robots in general) live only to the moral code we provide them. An interesting side note in this article highlights Dara Khosrowshahi’s vision that Uber is at the centre of the network of autonomous vehicles - rather than designing specific end nodes, Uber expects to work with others to be the one ring to binding them all together.
https://www.theinformation.com/articles/uber-finds-deadly-accident-likely-caused-by-software-set-to-ignore-objects-on-roadUber has determined that the likely cause of a fatal collision involving one of its prototype self-driving cars in Arizona in March was a problem with the software that decides how the car should…

Over 10% of all randomised controlled trials in education ever, anywhere in the world, have been funded by the UK government. Following the evidence is hard, especially when it challenges the status quo, common practice, or established “knowledge”, so its good to see the UK government putting this money in to establishing a solid evidence base for effective educational practices.
https://www.economist.com/news/britain/21739671-third-its-schools-have-taken-part-randomised-controlled-trials-struggle-getting?frsc=dgA third of its schools have taken part in randomised controlled trials. The struggle is getting teachers to pay attention to the evidence

Why is weather unpredictable? The natural world is governed by thousands of factors, and their relationships are intrinsically chaotic, making them hard to model and so to predict - at least for humans. In most systems the number of variables is so massive that even identifying them is impossible - think about the flickering of the flames of a large bonfire, or how topography affects weather formations. Researchers at the University of Maryland have proven that they can train a machine learning model on the existing time-series data, and the model was able to predict future states approximately 8 times further to the future than a human.
https://www.quantamagazine.org/machine-learnings-amazing-ability-to-predict-chaos-20180418/In new computer experiments, artificial-intelligence algorithms can tell the future of chaotic systems.

Very interesting approach by Google’s researchers to the “cocktail party problem”. The team trained a CNN to determine which person is speaking in a video with multiple overlapping sounds, and to amplify that speech while reducing other noise. Applications include better automated subtitles, and improved hearing aids. https://research.googleblog.com/2018/04/looking-to-listen-audio-visual-speech.htmlhttps://research.googleblog.com/2018/04/looking-to-listen-audio-visual-speech.htmlPosted by Inbar Mosseri and Oran Lang, Software Engineers, Google Research People are remarkably good at focusing their attention on a par…

Stripe’s advances in AI, based on hundreds of billions of data points, have been able to reduce fraud by 25% without materially affecting non-fraud acceptance rates.
https://thenextweb.com/artificial-intelligence/2018/04/18/stripes-ai-fraud-detector-crazy-smart/I’m loath to use the term, but Stripe is a revolutionary product. It allows pretty much anyone to accept card payments just by adding a few lines of code to their site, without having to deal with…

A fascinating set of guidelines for making complex work which we typically say “has” to be done face to face (like designing) effective when the team works remotely. I particularly like the emphasis on using a spectrum of tools to support “stepping up” the “bandwidth” of a conversation from asynchronous text (e.g. email, slack) to synchronous, real-time, visual methods (e.g. video chat). Others, such as drop in sessions for constructive feedback on work items, look equally useful, and i look forward to testing some of these out with the team.
https://zapier.com/blog/remote-design-culture/More and more companies are seeing the benefits of remote work for productivity in the workplace. As Director of Design at Zapier, I frequently get asked the question of how the design process works…

Choosing an effective loss function is a critical part of training ML models. This thought provoking article reminds us to be critical in the choice of this function, especially as in many models the reward function itself is unclear - does a recommendation system (e.g. promoting new articles, or songs) simply create an echo chamber, or does it broadly converge on the mean? Which of these should score higher? If we penalise the system when users don’t click on articles which violate their confirmation bias - are we acting ethically?
http://www.argmin.net/2018/04/16/ethical-rewards/Musings on systems, information, learning, and optimization.

While at first Intel appears to be catching up in the race to develop chips optimised for AI, looking deeper reveals a broader, longer term strategy to develop open code allowing any competing or complimentary framework (Tendorflow, Caffe, MXNet, etc.) to run at optimum efficiency on their hardware. Back to those chips (and the dodgy performance charts) - the authors of this article point out that you get better single chip performance from hardware 3 generations old compared to the newest silicone - although the real measure isn’t single chip, but how to scale out complex models across “farms” of devices, as the big boys (e.g. Facebook) do.
https://www.nextplatform.com/2018/04/20/is-open-source-the-ai-nirvana-for-intel/Intel has been making some interesting moves in the community space recently, including free licenses for its compiler suite for educators and open source contributors can now be had, as can rotating…

A practical example of the importance of appearance: “appearing tall … is linked to increased social status across cultures, which researchers hypothesize has an evolutionary origin: If you were a taller caveman, you were probably better at taking down megafauna.”
https://www.inverse.com/article/43535-mark-zuckerberg-booster-seat-explanationSitting tall gives him the confidence boost he needs.

IBM’s 5 properties of effective AI?

1 Managed (durable infrastructure, effective data pipelines, data and model governance)

2 Resilient (automatic alerts when model drift is excessive)

3 Performant (runs in reasonable time on cost effective infrastructure)

4 Measurable (model accuracy, data volume, value released)

5 Continuous (evaluate and retrain models as needed)
https://venturebeat.com/2018/04/21/ibm-outlines-the-5-attributes-of-useful-ai/A few weeks ago, a dejected CTO told me it took his team three weeks to build a machine learning model. I told him a model in just three weeks sounded great, and he agreed. So why the long face? Be…

Pouring resources in to Alexa, AWS and experiments like Amazon Go, Amazon invested nearly $23bn on R&D last year, nearly 1/3 of the total spend of the top 5 (next come Alphabet, Intel, Microsoft, and Apple).
https://www.recode.net/2018/4/9/17204004/amazon-research-development-rdTech companies claimed the top five spots again this year.

As the cost of AI drops, things which aren’t currently thought to be solvable through prediction will suddenly be viable - and this will primarily be complimented with human judgement. Computers predict better than people can, but then these predictions will be “handed off” to a human to use judgement to determine the response (such as whether or how to act, or to ignore). Ultimately, the authors recommend that companies develop a “thesis” outlining what you plan to “predict” (e.g. what is “best”), the time until AI becomes so embedded that investments without it are not viable, recognising that progress towards that point will be exponential.
https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-economics-of-artificial-intelligenceRotman School of Management professor Ajay Agrawal explains how AI changes the cost of prediction and what this means for business.

The demise of the retail store may have been (greatly) exaggerated. Yes, many big box stores are disappearing, being unable to compete on cost or selection with online vendors, many companies are turning to technology to survive the change by inviting themselves directly in to customers’ homes, or optimising their supply chain and product ranges using AI and small, local, relevant locations. Others are making the retail store the place you go to try out a physical product which you then buy online, creating “showroom destinations” for customers. One thing’s clear - this battle is not yet lost.
https://www.cbinsights.com/research/retail-apocalypse-survival-technology-trends/We discuss the technologies and trends, from supply chain software to in-store AR technology, that are helping today’s brick-and-mortar retailers stay competitive as e-commerce continues to grow.

“That some big-name apps have removed their Apple Watch apps isn’t a sign that the Apple Watch is failing as a platform: It’s a sign that the platform is evolving [as developers learn what the new form factor is truly useful for]”
https://slate.com/technology/2018/04/apple-watch-popular-apps-are-leaving-the-platform-is-that-a-bad-sign.htmlIt joined Twitter, Amazon, Google Maps, and Slack, among others.