The real test of an AI machine is when it can admit to not knowing something | John Naughton | Opinion – Blog – 10 minute

On Wednesday the European Commission launched a blizzard of proposals and policy papers under the general umbrella of “shaping Europe’s digital future”. The documents released included: a report on the safety and liability implications of artificial intelligence, the internet of things and robotics; a paper outlining the EU’s strategy for data; and a white paper on “excellence and trust” in artificial intelligence. In their general tenor, the documents evoke the blend of technocracy, democratic piety and ambitiousness that is the hallmark of EU communications. That said, it is also the case that in terms of doing anything to get tech companies under some kind of control, the European Commission is the only game in town.
In a nice coincidence, the policy blitz came exactly 24 hours after Mark Zuckerberg, supreme leader of Facebook, accompanied by his bag-carrier – a guy called Nicholas Clegg who looked vaguely familiar – had called on the commission graciously to explain to its officials the correct way to regulate tech companies. The officials, in turn, thanked him and courteously explained that they had their own ideas, and escorted him back to his hot-air balloon.
For this columnist, the most interesting document is the white paper on AI. It declares that the commission supports “a regulatory and investment oriented approach” that has two objectives: to promote the uptake of AI and to address the risks associated with certain uses of the technology. The document then sets out policy options on how these objectives might be achieved.
Once you get beyond the mandatory euro-boosting rhetoric about how the EU’s “technological and industrial strengths”, “high-quality digital infrastructure” and “regulatory framework based on its fundamental values” will enable Europe to become “a global leader in innovation in the data economy and its applications”, the white paper seems quite sensible. But as for all documents dealing with how actually to deal with AI, it falls back on the conventional bromides about human agency and oversight, privacy and governance, diversity, non-discrimination and fairness, societal wellbeing, accountability and that old favourite “transparency”. The only discernible omissions are motherhood and apple pie.
But this is par for the course with AI at the moment: the discourse is invariably three parts generalities, two parts virtue-signalling leavened with a smattering of pious hopes. It’s got to the point where one longs for some plain speaking and common sense.
And, as luck would have it, along it comes in the shape of Sir David Spiegelhalter, an eminent Cambridge statistician and former president of the Royal Statistical Society. He has spent his life trying to teach people how to understand statistical reasoning, and last month published a really helpful article in the Harvard Data Science Review on the question “Should we trust algorithms?”

It’s trustworthiness rather than trust we should be focusing on

Onora O’Neill, philosopher

Underpinning Spiegelhalter’s approach is an insight from the philosopher Onora O’Neill – that it’s trustworthiness rather than trust we should be focusing on, because trust is such a nebulous, elusive and unsatisfactory concept. (In that respect, it’s not unlike privacy.) Seeking more trust, O’Neill observed in a famous Ted Talk, “is not an intelligent aim in this life – intelligently placed and intelligently refused trust is the proper aim”.
Applying this idea, Spiegelhalter argues that, when confronted with an algorithm, we should expect trustworthy claims made both about the system (what the developers say it can do, and how it has been evaluated) and by the system (what it concludes about a specific case).
From this, he suggests a set of seven questions one should ask about any algorithm. 1. Is it any good when tried in new parts of the real world? 2. Would something simpler, and more transparent and robust, be just as good? 3. Could I explain how it works (in general) to anyone who is interested? 4. Could I explain to an individual how it reached its conclusion in their particular case? 5. Does it know when it is on shaky ground, and can it acknowledge uncertainty? 6. Do people use it appropriately, with the right level of scepticism? 7. Does it actually help in practice?
This is a great list, in my humble opinion. Most of the most egregiously deficient machine-learning systems we have encountered so far would fail on some or all of those grounds. Spiegelhalter’s questions are specific rather than couched in generalities such as ‘transparency’ or ‘explainability’. And – best of all – they are intelligible to normal human beings rather than the geeks who design algorithms.
And the most important question in that list? Spiegelhalter says it is number five. A machine should know when it doesn’t know – and admit it. Sadly, that’s a test that many humans also fail.
What I’m reading
BBC v Netflix: a prequel“How the BBC’s Netflix-killing plan was snuffed by myopic regulation.” Sobering piece in Wired about how the BBC and other UK broadcasters came up with the idea of a Netflix-like service when the streaming giant was still shipping DVDs, but were thwarted by UK regulators. Regulation is hard unless you know the future.
An open and shut case“The messy, secretive reality behind OpenAI’s bid to save the world.” Great investigative reporting by MIT’s Technology Review.
How the peace was lost“The Last Days at Yalta…” A gripping reconstruction on Literary Hub by the historian Diana Preston of the conference that launched the cold war.

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Machine vision checks tigers’ health at Adelaide Zoo – Projects – Software- Tempemail – Blog – 10 minute

A study at Adelaide Zoo has found a novel way to combine machine vision algorithms with cheap digital cameras to passively check on the health of animals to minimise stress and disruption.
The research from the University of South Australia found that the heart rate and breathing rate of lions, tigers, and pandas could be reliably determined by analysing footage from standard digital video cameras.
Professor Javaan Chahl from the university’s School of Engineering said that the algorithms detect the changing values in the video data as the animals’ chests expand both from breathing – which is more obvious – and a more subtle shift as a result from heartbeats.
“We do some sophisticated signal processing to separate out those two waveforms so we can measure the frequency of them separately,” Chahl said.
He added that using a stationary camera set at a distance means the animals’ health can be continuously monitored without disturbing their daily routines and causing undue stress.
“Some animals basically need to have a general anaesthetic to just have their blood pressure taken or something or to have their heart rate measured,” he said.
“Many other checks would be done during that process, but heart rate, breathing rate are key indicators of health. Up until now, the only way to do that has been basically after being anaesthetised.”
In a paper recently published in the journal Sensors, the researchers explained that digital cameras were ideal for study over other technologies like thermal imaging because they’re both relatively cheaper due to market pressures and capable of capturing higher resolution images, in part thanks to the wider array of lenses commercially available.
Chahl added that the study was a good proof of concept for how the technology could be applied in zoo settings to extract cardiopulmonary signals from the animals, but further refining and validation is needed.
“The next step is probably longer range. These are zoo animals in small enclosures, it would be nice to be able to do this across very long ranges and Adelaide is blessed with one zoo with two sites.
“One is at Monarto, it’s an open range zoo. So we’ll be taking this technology and applying it to video from the Monarto zoo from over hundreds of metres.
“And the step beyond that is to be able to monitor the heart rate and breathing rate of endangered populations of animals in the wild.”

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Arm brings AI and machine learning to IoT and the edge – Blog – 10 minute

Why it matters: For a company that doesn’t manufacture anything, Arm has a surprisingly large and broad impact, not only on the chip industry, but the overall tech industry and, increasingly, many other vertical industries as well. The company—which creates semiconductor chip designs that it licenses as intellectual property and then Arm’s customers use the designs to build chips—is the brains behind virtually every smartphone ever made.
In addition, it has a small but growing market in data center and other network infrastructure equipment and is the long-time leader in intelligent devices of various types—from toys to cars and nearly everything in between; essentially the “things” part of IoT (Internet of Things).
As a result, it’s not terribly surprising to see the company pushing ahead on new innovations to power more devices. What is unexpected about Arm’s latest announcements, however, is the degree of performance that it’s enabling in microcontrollers—tiny chips that power billions of devices. Specifically, with the launch of its new Cortex-M55 Processor, companion Ethos-U55 microNPU (Neural Processing Unit) accelerator, and new machine learning (ML) software, Arm is promising a staggering 480x improvement in ML performance for a huge range of low-power applications.

That’s not the kind of performance numbers you typically hear about in the semiconductor industry these days. Practically speaking, it turns something that was nearly impossible into something very doable.
More importantly, because microcontrollers are the tiny, unsung heroes powering everything from connected toothbrushes to industrial equipment, the potential long-term impact could be huge. Most notably, the addition of AI intelligence to all these types of “things” offers the promise of finally getting the kind of smart devices that many hoped for in areas from smart home to factory automation and beyond. Imagine adding voice control to the smallest of devices or being able to get advanced warnings about potential part failures in slightly larger edge computing equipment through onboard predictive maintenance algorithms. The possibilities really are quite limitless.
Practically speaking, it turns something that was nearly impossible into something very doable.
The announcements are part of an overall strategy at Arm to bring AI capabilities to its full range of IP designs. Key to that is work the company is doing on software and development tools. Because Cortex-M series designs have been around for a long time, there’s a large base of applications that device designers can use to program them. However, because a great deal of ML and AI-based algorithm work is being created in frameworks, such as TensorFlow, the company is also bringing support for its new IP designs into TensorFlow Lite Micro, which is optimized for the types of smaller devices for which these new chips are intended.

In addition to software, there are several different hardware-centric capabilities that are worth calling out. First, the Cortex-M55 is the first microcontroller design to incorporate support for the company’s Helium vector process technology, previously only found on larger Arm CPU cores. The M55 also includes support for Arm Custom Instructions, an important new capability that lets chips designers create custom functions that can be optimized for specific workloads.
The new Ethos-U55 is a first of its kind dedicated AI accelerator architecture that was designed to pair with the M55 for devices in which the 15x improvement in ML performance that the M55’s new design offers is not enough. In addition, the combination of the M55 and the U55 was specifically intended to offer a balance of scalar, vector, and matrix processing, which is essential to efficiently running a wide range of machine-learning-based workloads.
Of course, there are quite a few steps between releasing new chip IP designs and seeing products that leverage these capabilities. Unfortunately, that means it will likely be sometime near the end of 2021 and into 2022 before we can really see the promised benefits of a nearly 500x improvement in machine learning performance. Plus, it remains to be seen how challenging it will be for low-power device designers to create the kinds of ML algorithms they’ll need to make their devices truly smart. Hopefully, we’ll see a large library of algorithms developed so that device designers with little to no previous AI programming experience can leverage them.
Ultimately, the promise of bringing machine learning to small, battery-powered devices is an intriguing one that opens up some very interesting possibilities for the future. It will be interesting to see how the “things” develop.
Bob O’Donnell is the founder and chief analyst of TECHnalysis Research, LLC a technology consulting and market research firm. You can follow him on Twitter @bobodtech. This article was originally published on Tech.pinions.

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First drug developed using machine learning enters clinical trials – Blog – 10 minute

What just happened? Of all the domains where machine learning is expected to be revolutionary, medicine is perhaps the most universal. In a major new milestone, a drug developed using machine learning is about to enter human trials.
Before a new medicine enters human trials, there is typically three to five years of work behind the scenes, researching causes for diseases and compounds that may help treat them. But working with British AI startup Exscientia, a Japanese drug development company called Sumitomo Dainippon Pharma Co. is about to start phase 1 clinical trials after only 12 months.
The drug in question is DSP-1181, a prospective treatment for obsessive-compulsive disorder (OCD). OCD affects millions of people worldwide, to varying degrees, and can be debilitating in its psychological effects.
Exscientia, based in Oxford, UK, operates an exciting machine learning platform called Centaur Chemist. The platform allegedly takes years off the time required to research new compounds, by combining A.I. techniques with existing knowledge of how medicines interact with the human body.
The benefit of machine learning is that it can happen virtually, and far quicker than scientists are able to work in the real world. The platform can analyze millions of molecular combinations and attempt to identify which may be the safest and most effective in treating a given disease.

Perhaps even more important is the potential savings associated with using machine learning to develop new medicines. Typically, it costs over $1 billion to bring a new drug through from conception to market, with a lot of those costs borne out during the research phases. But taking out years of painstaking research will save both time and money, speeding up development and freeing up resources to develop yet more medicines.
There’s a lot riding on Exscientia and Sumitomo Dainippon’s trial. The first phase is to check how the drug affects the body, and how the body metabolises the drug. So this will not prove the medication’s efficacy.
But if DSP-1181 is shown to be safe, phases two and three can proceed, to see whether the drug can help OCD patients in the real world. And if it does, we’ll witness the dawn of machine learning in medicine.

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How NAB uses machine learning to find faked loan docs – Finance – Cloud – Security – Software- Tempemail – Blog – 10 minute

NAB has finally revealed the nature of a fraud-related machine learning use case, with the algorithm being used to detect people that file fake documentation in support of loan applications.
Chief data officer Glenda Crisp first disclosed the fraud-related use case at AWS Summit in Sydney back in May 2019, but largely declined to discuss it.
“We’ve been able to do some interesting things around fraud and I don’t like to talk about this too much,” she said at the time.
Speaking at AWS re:Invent 2019 in Las Vegas last month, Crisp provided the first indication of where the bank is targeting instances of fraud with its machine learning efforts.
“In terms of machine learning models, we’ve been working on building this capability for a little while now,” she said.
“We’re starting in the areas you would expect a bank to start in: anti-money laundering, cyber and fraud.
“Let me just talk about fraud quickly. This may be a shock to some of you in the room, but there are people who actually lie on their loan applications. And not only do they lie, they send us fake documentation like fake payslips.
“So we are building a machine learning model – it’s actually fairly advanced through its training right now – to try to identify fake and falsified documentation, and we’re seeing some really good improvements over the traditional models that we’ve used.”
Crisp also discussed the existence of a second machine learning model that is performing “topic and theme analytics around customer complaints.”
“We get customer complaints, and we actually would like to know what’s driving those complaints,” Crisp said.
“So we have a machine learning model that’s been running for a few months now on those [complaints].
“What I, as a CDO, am most interested in and I track most closely is the percentage of customer complaints related to data quality and so that’s a number that I want to drive down because I don’t think that should happen. 
“I think our customers should be able to trust us for good quality data.”
Experimental ‘labs’
NAB is using AWS SageMaker to allow around 400 data scientists and analytics team members to build, train and deploy machine learning models.
This forms part of a toolkit that NAB calls its Discovery Cloud or NDC.
“This is where we are building machine learning models and predictive analytics,” Crisp said.
Crisp said that data scientists were able to spin up temporary “data labs” to experiment building models for up to 90 days – sometimes longer if required.
She said there were currently 55 data labs running within NAB, “each of them with usually one, maybe two use cases going at any point in time.”
The creation and destruction of data labs happened automatically using AWS services, and they contained appropriate guardrails to ensure safe experimentation. 
The automatic time limitation was intended to ensure that models were not run permanently in the labs, but were instead migrated into regular production environments if they were deemed ready.
“[Labs are] not meant to be running models all the time,” Crisp said.
“This is meant as a development or an experimentation area, so if you do build a model, and you do want it to go to production, we have a path to production and we have a production-run environment that it’ll then live in, and we have all the right operational controls around that.”
Crisp noted that NDC is the portion of NAB’s data environment that is most likely to change rapidly.
“This is probably the part of the architecture that shifts and is trying out new things the most,” she said.
“We’re constantly looking at new ways to do things. 
“We’ve got a PoC going with H2O.ai, and Databricks is something else that we’re exploring as well. So you’ll see us trying things in here. 
“This part of the architecture especially will change pretty rapidly over the next six to 12 months.”

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Inside the making of Hooha, the world’s first smart tampon machine- Tempemail – Blog – 10 minute

Huge won the ‘Technology’ category at Tempemail PR Awards 2019 with its campaign for Hooha. Here, the agency reveals the challenges faced and the strategies used to deliver this successful project.
The challenge
In 2018, the inventor of Hooha needed a tampon on the floor of a major tech convention. But every tampon machine at the event was broken. Clearly, amid the greatest technological advancements across every other industry, women’s basic health needs are still ignored.
The strategy
To call attention to this inequality, a team of six designed and built Hooha – the world’s first smart tampon dispenser. We didn’t want it hidden away in the ladies’ room so, exactly one year later, we unveiled it at SXSW on International Women’s Day.

The campaign
We partnered with The Female Quotient, an organisation committed to advancing equality for women and featured Hooha in its lounge for convention goers to try for themselves.
The results
From this exposure, we were invited to demos, talks, panels and received national news coverage earning 158 million PR impressions in just a few short weeks. This attention also led to interest from the largest consumer packaged goods company in the world and their #1 tampon brand.
The project as a winner at Tempemail PR Awards 2019. To register your interest in 2020’s event, click here.

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Huge
Huge is a digital agency that provides business strategy, user experience, visual design, marketing and technology services to some of the world’s largest businesses and best-known brands.
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New machine learning algorithms to spot online trolls: Study- Tempemail – Blog – 10 minute

Researchers, including two of Indian-origin, have demonstrated that machine-learning algorithms can monitor online social media conversations as they evolve, which could one day lead to an effective and automated way to spot online trolling. Prevention of online harassment requires rapid detection of offensive, harassing, and negative social media posts, which in turn requires monitoring online interactions.
Current methods to obtain such social media data are either fully automated and not interpretable or rely on a static set of keywords, which can quickly become outdated. Neither method is very effective, according to Indian-origin researcher Maya Srikanth, from California Institute of Technology (Caltech) in the US.
“It isn’t scalable to have humans try to do this work by hand, and those humans are potentially biased,” Srikanth said.
“On the other hand, keyword searching suffers from the speed at which online conversations evolve. New terms crop up and old terms change meaning, so a keyword that was used sincerely one day might be meant sarcastically the next,” she added.
According to the study, the research team used a GloVe (Global Vectors for Word Representation) model to discover new and relevant keywords.
GloVe is a word-embedding model, meaning that it represents words in a vector space, where the “distance” between two words is a measure of their linguistic or semantic similarity.
Starting with one keyword, this model can be used to find others that are closely related to that word to reveal clusters of relevant terms that are actually in use. For example, searching Twitter for uses of “MeToo” in conversations yielded clusters of related hashtags like “SupportSurvivors,” “ImWithHer,” and “NotSilent.”
This approach gives researchers a dynamic and ever-evolving keyword set to search. The project was a proof-of-concept aimed at one day giving social media platforms a more powerful tool to spot online harassment.
“The field of AI research is becoming more inclusive, but there are always people who resist change,” said researcher Anima Anandkumar, who in 2018 found herself the target of harassment and threats online because of her successful effort to switch to an acronym without potentially offensive connotations.
“It was an eye-opening experience about just how ugly trolling can get. Hopefully, the tools we’re developing now will help fight all kinds of harassment in the future,” she added.
The study was presented on December 14 last year at the AI for Social Good workshop at the 2019 Conference on Neural Information Processing Systems in Vancouver, Canada.

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Predicting shrimp diseases with machine learning technology- Tempemail – Blog – 10 minute

Professor Kenton Morgan has joined hands with Aquaconnect, IDH, The Sustainable Trade Initiative and for a project to predict shrimp diseases by using machine learning. The project aims to benefit 13.000 ha of farm area in India in the next two years.
Shrimp diseases cause significant economic losses. The Asian shrimp industry alone reported to have lost at least US$20 billion in a decade. Diseases in the State of Andhra Pradesh that constitutes of nearly 70 per cent of Indian shrimp production are expected to cut down production by 40 per cent this year. Diseases have economic, social and environmental consequences as natural resources such as shrimp feed etc are wasted.
Aquaconnect helps Indian shrimp farmers to manage their farm operations efficiently and improving productivity by using artificial intelligence. Aquaconnect’s FarmMOJO mobile app uses machine learning technology to analyse feed and growth patterns in relation to animal health. The app provides insights to the farmers and suggests appropriate advice for better disease management.
Rajamanohar, CEO, Aquaconnect said, “The Government of India is ramping up support for the aquaculture sector, launching the Pradhan Mantri Matsya Sampada Yojana (PMMSY) to catalyse Blue Revolution 2.0. We are excited to partner with IDH and Prof. Kenton Morgan on pioneering the machine learning efforts in shrimp epidemiology studies to help the Indian farmers to mitigate the risks due to diseases and support long term sustainability of this Industry “
Aquaconnect aims to enhance FarmMOJO’s disease prediction model by using Aquatic Epidemiology, provided by Prof. Kenton Morgan. Professor Kenton Morgan, University of Liverpool has over 40 years of experience in epidemiology. The IDH Aquaculture Program aims to link aquatic epidemiology to the sector. IDH supports and pilots the linkage in several geographies as to tackle feed and disease issues.
Flavio Corsin, Program Director – Aquaculture at IDH, Sustainable Trade Initiative and a trained aquatic epidemiologist said, ‘’Shrimp farming practices and environment change continuously, and these changes can affect disease occurrence in complex and unpredictable ways. Integrating epidemiological tools into Aquaconnect’s farm management app will allow farmers to benefit from Artificial Intelligence in their efforts to understand, predict and prevent diseases’’.

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Explorium reveals $19.1M in total funding for machine learning data discovery platform – gpgmail


Explorium, a data discovery platform for machine learning models, received a couple of unannounced funding rounds over the last year — a $3.6 million seed round last September and a $15.5 million Series A round in March. Today, it made both of these rounds public.

The seed round was led by Emerge with participation of F2 Capital. The Series A was led by Zeev Ventures with participation from the seed investors. The total raised is $19.1 million.

The company founders, who have a data science background, found that it was problematic to find the right data to build a machine learning model. Like most good startup founders confronted with a problem, they decided to solve it themselves by building a data discovery platform for data scientists.

CEO and co-founder, Maor Shlomo says that the company wanted to focus on the quality of the data because not much work has been done there. “A lot of work has been invested on the algorithmic part of machine learning, but the algorithms themselves have very much become commodities. The challenge now is really finding the right data to feed into those algorithms,” Sholmo told gpgmail.

It’s a hard problem to solve, so they built a kind of search engine that can go out and find the best data wherever it happens to live, whether it’s internally or in an open data set, public data or premium databases. The company has partnered with thousands of data sources, according to Schlomo, to help data scientist customers find the best data for their particular model.

“We developed a new type of search engine that’s capable of looking at the customers data, connecting and enriching it with literally thousands of data sources, while automatically selecting what are the best pieces of data, and what are the best variables or features, which could actually generate the best performing machine learning model,” he explained.

Shlomo sees a big role for partnerships, whether that involves data sources or consulting firms, who can help push Explorium into more companies.

Explorium has 63 employees spread across offices in Tel Aviv, Kiev and San Francisco. It’s still early days, but Sholmo reports “tens of customers.” As more customers try to bring data science to their companies, especially with a shortage of data scientists, having a tool like Explorium could help fill that gap.


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Emojivision app turns your iPhone’s camera into a real-time emoji painting machine – gpgmail


Your iPhone is capable of some impressive feats of computational photography, and a new app created by developer Gabriel O’Flaherty-Chan shows one way all that power can be put to creative use. Emojivision lets you see the world as if it were made up entirely of emojis.

The free app (which induces an in-app purchase for $2.79 that unlocks more emoji packs) works by breaking down your iPhone’s camera sensor input to its color palette fundamentals, finding its nearest neighbor from a subset of emojis (organized thematically within the app) and then rebuilding the image with a filter that overlays the image, and that can run at 60fps so you’d be hard-pressed to spot any lag between it and a real-time feed.

You can use the app to take selfies, interpret photos from your phone’s photo gallery, or just mess around with resolution to see how finely detailed, or how abstractly and yet obviously emoji-based you can get. This isn’t the app to go to if you’re looking for a hyper-realistic or clear visual interpretation of your face, but it is a fun thing to show your friends – and an impressive bit of software engineering, too.

O’Flaherty-Chan has create some noteworthy mobile software projects in the past – including when he managed to hack a fully playable version of Pokémon Yellow onto an Apple Watch. He’s currently working on building a gigantic real-time strategy game set within a procedurally-generated universe – like a ‘No Man’s Sky’ but with a focus on the RTS elements that should make for a very compelling and evolving approach to gathering resources and expanding your empire.


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