The bill, passed on Wednesday, aims to accelerate the sweeping change of regulations across various fields to support the creation of such futuristic cities.

Addressing issues such as depopulation and an aging society will be the focus of the super cities. Technologies including big data and AI will be key to successfully tackling the challenging problems.

Large amounts of data will be collected and organised from across administrative organisations.

Local governments will be selected for the ambitious projects which will launch forums with the national government and private companies to take forward the plans.

Draft plans will be created from this deep public-private collaboration that will subsequently be submitted to the state government if approved by local residents.

As with many smart city plans, there are deep concerns about the collection of personal data and what it could mean for individual privacy. Local residents are sure to want assurance that any data collection is anonymous.

A similar bill was submitted to the Diet (Japan’s decision-making institution) last year. The bill was scrapped following calls from the ruling government to review it.

The revised bill was passed on Wednesday. Given the appetite for the project across the government; the plans are now expected to progress swiftly.

(Photo by Jezael Melgoza on Unsplash)

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There is still fierce debate about the impact that artificial intelligence will have on jobs. Some believe that AI will replace many jobs and lead to the requirement of a Universal Basic Income (UBI), while others claim it will primarily offer assistance to help workers be more productive.

Dorsey is a respected technologist with a deep understanding of emerging technologies. Aside from creating Twitter, he also founded Square which is currently pushing the mass adoption of blockchain-based digital currencies such as Bitcoin and Ethereum.

Yang was seen as the presidential candidate for technologists before he suspended his campaign in February, with The New York Times calling him “The Internet’s Favorite Candidate” and his campaign was noted for its “tech-friendly” nature. The entrepreneur, lawyer, and philanthropist founded Venture for America, a non-profit which aimed to create jobs in cities most affected by the Great Recession. In March, Yang announced the creation of the Humanity Forward non-profit which is dedicated to promoting the ideas during his presidential campaign.

Jobs are now very much at threat once again; with the coronavirus wiping out all job gains since the Great Recession over a period of just four weeks. If emerging technologies such as AI do pose a risk to jobs, it could only compound the problem further.

In an episode of the Yang Speaks podcast, Dorsey warns that AI will pose a particular threat to entry-level programming jobs. However, even seasoned programmers will have their worth devalued.

“A lot of the goals of machine learning and deep learning is to write the software itself over time so a lot of entry-level programming jobs will just not be as relevant anymore,” Dorsey told Yang.

Yang is a proponent of a UBI. Dorsey said that such free cash payments could provide a “floor” for if people lose their jobs due to automation. Such free cash wouldn’t allow for luxurious items and holidays, but would ensure that people can keep a roof over their heads and food on the table.

UBI would provide workers with “peace of mind” that they can “feed their children while they are learning how to transition into this new world,” Dorsey explains.

Critics of UBI argue that such a permanent scheme would be expensive.

The UK is finding that out to some extent currently with its coronavirus furlough scheme. Under the scheme, the state will pay 80 percent of a worker’s salary to prevent job losses during the crisis. However, it’s costing approximately £14 billion per month and is expected to be wound down in the coming months due to being unsustainable.

However, some kind of UBI system is appearing increasingly needed.

In November, the Brookings Institute published a report (PDF) which highlights the risk AI poses to jobs. 

“Workers with graduate or professional degrees will be almost four times as exposed to AI as workers with just a high school degree. Holders of bachelor’s degrees will be the most exposed by education level, more than five times as exposed to AI than workers with just a high school degree,” the paper says.

In their analysis, the Brookings Institute ranked professions by their risk from AI exposure. Computer programmers ranked third, backing Dorsey’s prediction, just behind market research analysts and sales managers.

(Image Credit: Jack Dorsey by Thierry Ehrmann under CC BY 2.0 license)

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That’s according to Dr Karol Przystalski, CTO at IT consulting and software development provider Codete. Przystalski founded Codete having had a significant research background in AI, with previous employers including Sabre and IBM and a PhD exploring skin cancer pattern recognition using neural networks.

Yet what effect will the Covid-19 pandemic have on this change? Speaking with AI News, Przystalski argues – much like Dorian Selz, CEO of Squirro, in a piece published earlier this week – that while AI isn’t quite there to predict or solve the current pandemic, the future can look bright.

—

AI News: Hi Karol. Tell us about your career to date and your current role and responsibilities as the CTO of Codete?

Dr Karol Przystalski: The experience from the previous companies I worked at and the AI background that I had from my PhD work allowed me to get Codete off the ground. At the beginning, not every potential client could see the advantages of machine learning, but it has changed in the last couple of years. We’ve started to implement more and more machine learning-based solutions.

Currently, my responsibilities as the CTO are not focused solely on development, as we have already grown to 160 engineers. Even though I still devote some of my attention to research and development, most of my work right now is centred on mentoring and training in the areas of artificial intelligence and big data.

AI: Tell us about the big data and data science services Codete provides and how your company aims to differ from the competitors?

KP: We offer a number of services related to big data and data science: consulting, auditing, training, and software development support. Based on our extensive experience in machine learning solutions, we provide advice to our clients. We audit already implemented solutions, as well as whole processes of product development. We also have a workshop for managers on how not to fail with a machine learning project.

All the materials are based on our own case studies. As a technological partner, we focus on the quality of the applications that we deliver, and we always aim at full transparency in relationships with our clients.

AI: How difficult is it, in your opinion, for companies to gather data science expertise? Is there a shortage of skills and a gap in this area?

KP: In the past, to become a data scientist you had to have a mathematical background or, even better, a PhD in this field. We now know it’s not that hard to implement machine learning solutions, and almost every software developer can become a data scientist.

There are plenty of workshops, lectures, and many other materials dedicated to software developers who want to understand machine learning methods. Usually, the journey starts with a few proof of concepts and, in the next build, production solutions. It usually takes a couple of months at the very minimum to become a solid junior level data scientist, even for experienced software engineers. Codete is well-known in the machine learning communities at several universities, and that’s why we can easily extend our team with experienced ML engineers.

AI: What example can you provide of a client Codete has worked with throughout their journey, from research and development to choosing a solution for implementation?

KP: We don’t implement all of the projects that clients bring to us. In the first stage, we distinguish between projects that are buzzword-driven and the real-world ones.

One time, a client came to us with an idea for an NLP project for their business. After some research, it turned out that ML was not the best choice for the project – we recommended a simpler, cheaper solution that was more suitable in their case.

We are transparent with our clients, even if it takes providing them with constructive criticism on the solution they want to build. Most AI projects start with a PoC, and if it works well, the project goes through the next stages to a full production solution. In our AI projects, we follow the ‘fail fast’ approach to prevent our clients from potential over-investing.

AI: Which industries do you think will have the most potential for machine learning and AI and why?

KP: In the Covid-19 times, for sure the health, med, and pharma industries will grow and use AI more often. We will see more use cases applied in telemedicine and medical diagnosis. For sure, the pharma industry and the development of drugs might be supported by AI. We can see how fast the vaccine for Covid-19 is being developed. In the future, the process of finding a valid vaccine can be supported by AI.

But it is not only health-related industries which will use AI more often. I think that almost every industry will invest more in digitalisation, like process automation where ML can be applied. First, we will see an increasing interest in AI in the industries that were not affected by the virus so much, but in the long run even the hospitality and travel industry, as well as many governments, will introduce AI-based solutions to prevent future lockdown.

AI: What is the greatest benefit of AI in business in your opinion – and what is the biggest fear?

KP: There are plenty of ways machine learning can be applied in many industries. There is a machine learning and artificial intelligence hype going on now, and many managers become aware of the benefits that machine learning can bring to their companies. On the other hand, many can take AI for a solution for almost everything – but that’s how buzzword-driven projects are born, not real-world use cases.

This hype may end similarly to other tech hypes that we have witnessed before, when a buzzword was popular, but eventually only a limited number of companies applied the technology. Blockchain is a good example – many companies have tried using it, for almost everything, and in many cases the technology didn’t really prove useful, sometimes even causing new problems.

Blockchain is now being used with success in several industries. Just the same, we can have an ‘AI winter’ again, if we don’t distinguish between the hype and the true value behind an AI solution.

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These types of situations are what programs such as the MIT-developed Moral Machine have been looking to unlock. The site has for the past four years canvassed public opinion by presenting a series of ethical dilemmas: should an autonomous car protect its passenger for instance, or a pedestrian, in a particular scenario?

The wider point, however, is that such autonomy – or to put it more romantically, machines thinking for themselves – requires vast amounts of data. Data which, to date, has not been calibrated. Would you entrust your safety to your vehicle?

This is an analogy which Dorian Selz, co-founder and CEO of Squirro, uses to explain why the great AI revolution might take longer than many think. “I think we’re going to see massive advances in the underlying technology over the next 10 years – we are probably going to see some level of breakthrough to have some low level of intelligence in these machines,” Selz tells AI News. “I can imagine that – but it will take time until it is really adopted widely.”

For the CEO of an AI platform provider, this may be an interesting position to take – something which Selz accepts. Yet Squirro’s business case may give an indication. The company’s modus operandi is  taking organisations’ unstructured data sets – everything from emails, to market reports, to PowerPoint presentations and Word documents – and utilising what it calls ‘augmented intelligence’ to surface greater insights.

This collaborative process not only leans on the human factor, but takes time to appreciate. “Most companies have ratios of 90% to 99% of all data they generate or acquire never being used beyond the first use,” explains Salz. “That is the equivalent of buying a car, driving it from the dealership back to your garage, and then throwing away the key and never driving that car again.

“If you say data is the new oil, at the same time companies are not making use of that data,” he adds. “What works wonderfully well for numbers doesn’t work at all for text in an Excel.”

Selz knows of which he speaks with regard to how companies use and stockpile data. Squirro is the fourth company he has co-built, with a previous success being Swiss homegrown search engine local.ch. With Squirro, which promises benefits from customer and service insights to cognitive search, the goal is for this data to complement business processes and working methods.

Take an accounts department, for example, whereby the data can be able to show not just whether a client has paid or not, but any relevant news or market research which could impact their ability to pay. In the supply chain, issues can be identified several levels down the chain which may impact the direct supplier.

“If you look at any company today, small or big, their IT landscape is effectively a combination of boxed applicastions – a supply chain system at the back of the company, an ERP system in the middle, some level of CRM at the front and support systems in the background,” explains Selz. “The bigger the company, the more of that stuff you have, but it’s still essentially a landscape of boxed IT applications.

“The future we see is a future where you’re going to see a machine learning-driven layer that weaves all these different data silos together,” Selz adds. “We foresee a future where these types of intimately weaved informational fabrics become the new norm in any enterprise.

“It doesn’t replace you – I don’t believe in that at all. But it will support you in decision making.”

One customer which exemplifies this ‘weaving’ of data is Brookson, a UK-based local accountancy firm for contractors. The company had already been using Squirro to classify data coming in to expedite the administrative and call centre process, but the next step was particularly noteworthy: using the technology to assess future customer relationships. Based on a roadmap created from previous customers, good, bad or indifferent, every new interaction can be assessed and forecast using pre-defined journeys.

“I love to retell that story, because it shows that AI per se is not just for the big boys,” says Selz. “As a nimble, agile, SME enterprise, you can use this type of technology to really fundamentally put yourself in a better position in the marketplace.”

This is an interesting point; plenty of research, not least a study from VC company Work-Bench in 2018, has argued that bigger companies are at a natural advantage with AI because they can hoover up the best talent. But as AI expertise does not automatically equal big businesses, big effects are not the results of such technology yet, Selz argues – going back to his original theme around transformation.

“Everybody talks about these big intelligence machines that do these fantastic things for companies and automate,” he explains. “Transform entire industries? I don’t really believe that.

“If you look at many machine learning techniques in use today, from the more simplistic ones like SVM, Bayesian algorithms, all the way to more sophisticated deep learning models, at the end, they’re not really intelligent,” Selz adds. “It’s pattern matching at the power of whatever computer you have.”

Selz notes that ‘none of the lovely AI engines had foreseen the coronavirus’. Given the reactive, rather than proactive, steps taken by governments worldwide, it wasn’t just the machines who were flat-footed. Looking at various stories assessing AI’s impact on Covid-19, many argue its role is to assess what will happen next – rather, assessing the damage once the horse has bolted – with a Singapore research unit predicting last month, with extreme caution, that the pandemic will end globally by December.

This informs Selz’s overall theory that we are nowhere near ready for primetime in B2B just yet. “It’s outright dangerous to think such an AI engine can suddenly transform your business if it is so limited in its capabilities and capacity,” he says.

“A customer relationship is more than what you have in your data sets,” Selz adds. “Even in retail volume segments, no one has a fully digitised customer relationship yet. If you don’t have it fully digitised, every aspect of it, why would you entrust some anonymous algorithm call method to evaluate and effectively decide on the future of that relationship?

“No sane person would do that.”

Editor’s note: You can find out more about augmented intelligence at AIM, a virtual event hosted by Squirro on June 23-25.

 Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, , and Cyber Security & .

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A report from Greenpeace earlier this month exposed Google as being one of the top three developers of AI tools for the fossil fuel industry. Greenpeace found AI technologies boost production levels by as much as five percent.

In an interview with CUBE’s John Furrier, the leader of Google’s CTO office, Will Grannis, said that Google will “no longer develop artificial intelligence (AI) software and tools for oil and gas drilling operations.”

The pledge from Google Cloud is welcome, but it must be taken in a wider context.

In 2019, Google Cloud’s revenue from oil and gas was approximately $65 million. A hefty sum, but less than one percent of all Google Cloud revenues. Furthermore, Google Cloud’s revenue from oil and gas decreased by about 11 percent despite overall revenue growing by 53 percent.

While Google Cloud’s revenue from the oil and gas industry was declining, the public’s intolerance towards big polluters is increasing. The reputational damage caused to Google of continuing its relationship with polluters would likely have been more costly over the long-term.

This isn’t the first time Google has cut-off an AI-related relationship with a controversial industry to preserve its reputation.

Back in 2018, Google was forced into ending a contract with the Pentagon called Project Maven to build AI technologies for drones. Over 4,000 Google employees signed a petition demanding their management cease the project and never again “build warfare technology.”

Following the Project Maven backlash, Google CEO Sundar Pichai promised in a blog post the company will not develop technologies or weapons that cause harm, or anything which can be used for surveillance violating “internationally accepted norms” or “widely accepted principles of international law and human rights”.

Back in January, Pichai called for sensible AI regulation that does not limit the potential societal benefits.

PAX, a Dutch NGO, ranked Google among the safest companies developing AI while slamming rivals such as Amazon and Microsoft for being among the “highest risk” tech firms in the world.

(Photo by Zbynek Burival on Unsplash)

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The supercomputer will deliver eye-watering amounts of power from its 285,000 CPU cores and 10,000 GPUs (yes, it can probably even run Crysis.)

OpenAI is a non-profit that was founded by one Elon Musk to promote the ethical development of artificial intelligence technologies. Musk, however, departed OpenAI following disagreements over the company’s direction.

Back in February, Musk responded to an MIT Technology Review profile of OpenAI saying that it “should be more open,” and that all organisations “developing advanced AI should be regulated, including Tesla.”

Microsoft invested $1 billion in OpenAI last year and it seems we’re just beginning to see the fruits of that relationship. While most AIs today focus on doing single tasks well, the next wave of research is focusing on performing multiple at once.

“The exciting thing about these models is the breadth of things they’re going to enable,” said Microsoft Chief Technical Officer Kevin Scott.

“This is about being able to do a hundred exciting things in natural language processing at once and a hundred exciting things in computer vision, and when you start to see combinations of these perceptual domains, you’re going to have new applications that are hard to even imagine right now.”

So-called Artificial General Intelligence (AGI) is the ultimate goal for AI research; the point when a machine can understand or learn any task just like the human brain.

“The creation of AGI will be the most important technological development in human history, with the potential to shape the trajectory of humanity,” said Sam Altman, CEO, OpenAI. “Our mission is to ensure that AGI technology benefits all of humanity, and we’re working with Microsoft to build the supercomputing foundation on which we’ll build AGI.”

“We believe it’s crucial that AGI is deployed safely and securely and that its economic benefits are widely distributed. We are excited about how deeply Microsoft shares this vision.”

AGI will, of course, require tremendous amounts of processing power.

Microsoft and OpenAI claim their new supercomputer would rank in the top five but do not give any specific power measurements. To rank in the top five, a supercomputer would currently require more than 23,000 teraflops of performance. The current leader, the IBM Summit, reaches over 148,000 teraflops.

“As we’ve learned more and more about what we need and the different limits of all the components that make up a supercomputer, we were really able to say, ‘If we could design our dream system, what would it look like?’” said Altman. “And then Microsoft was able to build it.”

Unfortunately, for now at least, the supercomputer is built exclusively for OpenAI.

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As originally reported by the Los Angeles Times, Bill Frischling noticed in 2017 that his AI bot, Margaret, was struggling to transcribe part of the President’s speech from May 4 that year commemorating the 75th anniversary of the Battle of the Coral Sea. In particular, Margaret crashed after this 127-word section, featuring a multitude of sub-clauses and tense shifts:

“I know there are many active duty service personnel from both nations with us in the audience, and I want to express our gratitude to each and every one of you. We are privileged to be joined by many amazing veterans from our two countries, as well – and for really from so many different conflicts, there are so many conflicts that we fought on and worked on together – and by the way in all cases succeeded on – it’s nice to win.

“It’s nice to win, and we’ve won a lot, haven’t we Mr. Prime Minister? We’ve won a lot. We’re going to keep it going, by the way. You’ve given your love and loyalty to your nations, and tonight a room of grateful patriots says thank you.”

Frischling, in the words of the Times, ‘hired a computer expert with a PhD in machine punctuation to unteach Margaret normal grammar and syntax – and teach it to decipher Trump-speak instead.’ “It was still trying to punctuate it like it was English, versus trying to punctuate it like it was Trump,” he said.

Able to transcribe the President’s speeches unhindered after that, Margaret’s job is not just to keep a database of these remarks, but analyse behavioural patterns. According to Frischling, some of the behaviours Margaret has spotted include being ‘more comfortable’ telling falsehoods by talking quickly, as well as identifying when Trump is genuinely angry, as opposed to putting it on for show.

One example came at the White House coronavirus briefing on April 23, where Trump – against all medical advice – suggested patients should be injected with disinfectant to kill the virus. When a Washington Post reporter questioned this edict, Trump’s response, according to Margaret, was borne out of genuine anger. Yet Frischling added that for many of the President’s more pre-meditated attacks on ‘fake news’ – of which the Washington Post has been a frequent case – there is little palpable anger on show.

As this publication has previously reported, the lines between real and fake news continue to be blurred – with the President himself an obvious target. In January last year, a ‘deepfake’ video of a Trump speech was broadcast on a Fox-owned Seattle TV network, with an employee later sacked for the error. In February, the President outlined an executive order, titled ‘Maintaining American Leadership in Artificial Intelligence’ exploring five key principles.

President Trump is by no means the only world leader whose sentence construction could be considered off-beat. As reported in the Times (h/t @arusbridger) last week, UK Prime Minister Boris Johnson answered a question on coronavirus testing from Keir Starmer, the leader of the opposition, thus:

“As I think is readily apparent, Mr Speaker, to everybody who has studied the, er, the situation, and I think the scientists would, er, confirm, the difficulty in mid-March was that, er, the, er, tracing capacity that we had – it had been useful… in the containment phase of the epidemic er, that capacity was no longer useful or relevant since the, er, transmission from individuals within the UK um meant that it exceeded our capacity.

“As we get the new cases down, er, we will have a team that will genuinely be able to track and, er, trace hundreds of thousands of people across the country, and thereby to drive down the epidemic. And so, er, I mean, to put it in a nutshell, it is easier, er, to do now – now that we have built up the team on the, on the way out – than it was as er, the epidemic took off.”

One can only imagine what Margaret would have made of that transcription job.

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The Perelman School of Medicine at UPenn is working with Intel Labs to co-develop technology based on federated learning, a machine learning technique which trains an algorithm across various devices without exchanging data samples.

The goal is therefore to preserve privacy. Penn Medicine and Intel Labs have claimed they were first to publish a paper on federated learning in medical imaging, offering accuracy with a trained model to more than 99% of a model trained in a non-private method. Work which will build on this, according to the two companies, will ‘leverage Intel software and hardware to implement federated learning in a manner that provides additional privacy protection to both the model and the data.’

The two companies will be joined by 29 healthcare and research institutions from seven countries.

“AI shows great promise for the early detection of brain tumours, but it will require more data than any single medical centre holds to reach its full potential,” said Jason Martin, principal engineer at Intel Labs in a statement.

Artificial intelligence initiatives in healthcare continue apace. Microsoft recently announced details of a $40 million ‘AI for Health’ project, while last month startup Babylon Health stated its belief that it can appropriately triage patients in 85% of cases.

Read the full Intel announcement here.

Photo by jesse orrico on Unsplash

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The new video courses assume the developer already has a basic standard of Python skills. If you don’t, I’m afraid you’ll need to brush up on those first. Fortunately, Microsoft released a 44-part “Python for Beginners” series last autumn (or “fall” for our American friends.)

For those with the Python skills, or if you’ve just consumed all 44-parts of Microsoft’s course in record time, the new courses are around three hours each. 

The first course, More Python for Beginners, features 20 videos and covers areas such as lambdas, inheritance, and asynchronous operations.

The second course, Even More Python for Beginners: Data Tools, consists of 31 videos and really dives into using Python for machine learning and data science. Students are taught how to use popular Python libraries for the aforementioned topics; along with using the Jupyter Notebooks browser-based development environment.

Each of the courses are still led by Christopher Harrison, senior program manager at Microsoft, and Susan Ibach, business development manager from Microsoft AI Gaming. 

“While we’re not going to get into conversations about choosing algorithms or building models, we are going to introduce what you’ll use when you begin the journey. We’ll highlight Jupyter Notebooks, the favorite tool of data scientists,” Harrison and Ibach wrote in a blog. 

As of writing, the first part in Microsoft’s Python course series has been viewed over 1.75 million times.

The course’s popularity is of little surprise given the huge interest in Python as AI talent becomes more in-demand; with six-figure salaries not unheard of. In last year’s GitHub Octoverse report, Python overtook Java to become the second most popular language on the world’s largest repository host.

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AI will assist us mere humans in coming up with new innovations in the years to come. However, the USPTO will not let them take the credit.

The USPTO has rejected two early filings of inventions credited to an AI system called DABUS which was created by Stephen Thaler.

DABUS invented two devices; a shape-shifting food container, and a new type of emergency flashlight.

The filings were submitted by the Artificial Inventor Project (AIP) last year. AIP’s lawyers argued that Thaler is an expert in building AI systems like DABUS but has no experience in consumer goods and would not have created them himself.

The USPTO concluded that “only natural persons may be named as an inventor in a patent application,” under the current law.

Similar applications by the AIP in the UK and EU were rejected along the same lines by their respective patent authorities.

“If I teach my Ph.D. student and they go on to make a final complex idea, that doesn’t make me an inventor on their patent, so it shouldn’t with a machine,” editor Abbott, a professor at the University of Surrey who led a group of legal experts in the AI patent project, told the Wall Street Journal last year.

The case over whether only humans should hold such rights has similarities to the infamous monkey selfie saga where PETA argued that a monkey could own the copyright to a selfie.

The US Copyright Office also ruled in that instance that only photographs taken by humans can be copyrighted and PETA’s case was subsequently dismissed.

(Photo by Jesse Chan on Unsplash)

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