Daily Archives: May 28, 2022

In-brief Governments around the world should pass intellectual property laws that grant rights to AI systems, two academics at the University of New South Wales in Australia argued.

Alexandra George, and Toby Walsh, professors of law and AI, respectively, believe failing to recognize machines as inventors could have long-lasting impacts on economies and societies.

"If courts and governments decide that AI-made inventions cannot be patented, the implications could be huge," they wrote in a comment article published in Nature. "Funders and businesses would be less incentivized to pursue useful research using AI inventors when a return on their investment could be limited. Society could miss out on the development of worthwhile and life-saving inventions."

Today's laws pretty much only recognize humans as inventors with IP rights protecting them from patent infringement. Attempts to overturn the human-centric laws have failed. Stephen Thaler, a developer who insists AI invented his company's products, has sued trademark offices in multiple countries, including the US and UK to no avail.

George and Walsh are siding with Thaler's position. "Creating bespoke law and an international treaty will not be easy, but not creating them will be worse. AI is changing the way that science is done and inventions are made. We need fit-for-purpose IP law to ensure it serves the public good," they wrote.

A video clip with the face of a 13-year-old boy, who was shot dead outside a metro station in the Netherlands, swapped onto a body using AI technology was released by police.

Sedar Soares died in 2003. Officers have not managed to solve the case, and with Soares' family's permission, they have generated a deepfake of his image on a kid playing football in a field presumably to help jog anyone's memory. The cops have reportedly received dozens of potential leads since, according to The Guardian.

It's the first time AI-generated images have been used to try and solve a criminal case, it seems. "We haven't yet checked if these leads are usable," said Lillian van Duijvenbode, a Rotterdam police spokesperson.

You can watch the video here.

America's National Artificial Intelligence Research Resource (NAIRR) urged Congress to launch a "shared research cyberinfrastructure" to better provide academics with hardware and data resources for developing machine-learning tech.

The playing field of AI research is unequal. State-of-the-art models are often packed with billions of parameters; developers need access to lots of computer chips to train them. It's why research at private companies seems to dominate, while academics at universities lag behind.

"We must ensure that everyone throughout the Nation has the ability to pursue cutting-edge AI research," the NAIRR wrote in a report. "This growing resource divide has the potential to adversely skew our AI research ecosystem, and, in the process, threaten our nation's ability to cultivate an AI research community and workforce that reflect America's rich diversity and harness AI in a manner that serves all Americans."

If AI progress is driven by private companies, it could mean other types of research areas are left out and underdeveloped. "Growing and diversifying approaches to and applications of AI and opening up opportunities for progress across all scientific fields and disciplines, including in critical areas such as AI auditing, testing and evaluation, trustworthy AI, bias mitigation, and AI safety," the task force argued.

You can read the full report here [PDF].

Researchers at Meta AI released Myosuite, a set of musculoskeletal models and tasks to simulate biomechanical movement of limbs for a whole range of applications.

"The more intelligent an organism is, the more complex the motor behavior it can exhibit," they said in a blog post."So an important question to consider, then, is what enables such complex decision-making and the motor control to execute those decisions? To explore this question, we've developed MyoSuite."

Myosuite was built in collaboration with researchers at the University of Twente in the Netherlands, and aims to arm developers studying prosthetics and could help rehabilitate patients. There's another potential useful application for Meta, however: building more realistic avatars that can move more naturally in the metaverse.

The models only simulate the movements of arms and hands so far. Tasks include using machine learning to simulate the manipulation of die or rotation of two balls. The application of Myosuite in Meta's metaverse is a little ironic given that there's no touching allowed there along with restrictions on hands to deter harassment.

Excerpt from:

Experts: AI should be recognized as inventors in patent law - The Register

The pace of digitalization in healthcare is accelerating. AI-based reading solutions that support radiologists in managing their growing workload and delivering confident diagnoses will become particularly more important over the next years.

Usage of AI is on the rise

In healthcare, the demand for diagnostic services is steadily growing while the number of available experts is decreasing. Additionally, diagnostics and treatment are becoming increasingly complex. As a result, software solutions using artificial intelligence (AI) are on the rise. In fact, AI in the healthcare market is projected to grow from USD 6.9 billion in 2021 to USD 67.4 billion by 2027 translating into a compound annual growth rate (CAGR) of 46.2% from 2021 to 2027.1 Especially in diagnostics, where radiologists must examine ever larger amounts of data, AI has demonstrated its value by supporting radiologists in image reading pushing healthcare more to the cloud and cloud computing.

Cloud computing becomes essential

The dramatic increase in AI software calls for the implementation of cloud computing. It is essential for tapping the full potential of AI algorithms and maintaining flexibility. Some countries or even individual clinical institutions, however, have strict regulations that prohibit sending data to the cloud because patient data are particularly sensitive. This remains the case even though cloud computing has proved to be safe. So, does this mean that some radiologists will be left out in the cold, unable to use AI-based reading software?

Making full use of AI algorithms with a hybrid solution an example

Offering automatic postprocessing of imaging data sets through AI-powered algorithms, AI-Rad Companion from Siemens Healthineers is an analytical tool that uses patient data to support radiologists in fast reading and confident diagnostic decision-making. To support customers in making full use of AI algorithms without having to go to the cloud, Siemens Healthineers offers the Edge functionality of its teamplay digital health platform. With this hybrid computing solution, patient data stays on the local server of the clinical institution, while only the AI algorithms of the reading software are managed from the cloud, so they can be reliably updated and maintained.

Deeper insights into how the Edge functionality works

This hybrid computing solution combines essential capabilities of the cloud with the need for local data storage. By activating the Edge functionality, a closed environment is downloaded that is controlled by Siemens Healthineers via the cloud. This allows Siemens Healthineers to fully manage its applications like AI-Rad Companion locally, update AI algorithms within the defined regulatory framework and share data that are relevant for updating the AI based on the preferences of the clinical institution.

The draw of the hybrid solution is that the Edge functionality is a one-way data street. It allows the cloud to send data into the closed environment only to interact with the AI algorithms and allows the cloud to retrieve only the data needed for servicing the AI, leaving the privacy settings up to the customer and keeping patient data on premises.

Taking a first step toward cloud computing in healthcare

Clinical institutions that are restricted in using cloud computing may have to look for other solutions that allow them to stay current with innovations. With new hybrid computing solutions like the Edge functionality, radiologists have the chance to fully benefit from AI-based reading solutions while complying with strict data protection regulations.

Reference

Visit our site teamplay digital health platform

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AI-based reading solutions: Pointing the way to the cloud - Healthcare IT News

In their discussion section, the authors comment that engagement with stakeholders remains important if AI tools are to be used to positive effect in pediatric healthcare.

Further, although AI-assisted care is viewed favorably by parents for their children in the acute care setting, the selection of stakeholder groups for its development and implementation requires a diverse representation, Ramgopal and co-authors write. This is particularly important with respect to age and race/ethnicity, the two demographics associated with discomfort in our multivariable model.

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Our findings offer promise in the use of AI in acute care settings and indicate how parent perspectives on AI use may differ in ways that can inform clinicians discussions of AI-based decision-making with parents.

In a news item published by the hospitals communications team, Ramgopal says AI will sooner or later become standard in routine pediatric practice.

In the ED, we already use computer-based decision supports systems, which are precursors to AI, he adds. [T]hese systems dont dictate a particular course of action, but rather inform a physicians approach to care in situations where a human might easily miss an important pattern in how illness presents itself.

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Some 78% of parents OK with AI reading their child's chest X-rays - Radiology Business

Liberty Mutual is one of the most experienced and advanced cloud adopters in the nation. And that is in no small part thanks to the vision of James McGlennon, who in his role as CIO of Liberty Mutual for past 17 years has led the charge to the cloud, analytics, and AI with a budget north of $2 billion.

Eight years ago, McGlennon hosted an off-site think tank with his staff and came up with a technology manifesto document that defined in those early days the importance of exploiting cloud-based services, becoming more agile, and instituting cultural changes to drive the companys digital transformation.

Today, Liberty Mutual, which has 45,000 employees across 29 countries, has a robust hybrid cloud infrastructure built primarily on Amazon Web Services but with specific uses of Microsoft Azure and, lesser so, Google Cloud Platform. Liberty Mutuals cloud infrastructure runs an array of business applications and analytics dashboards that yield real-time insights and predictions, as well as machine learning models that streamline claims processing.

As the Boston-based insurance companys journey to the cloud has unfolded, it has also maintained a select set of datacenters from which to run legacy applications more economically than they would on the cloud, as well as software from vendors that make licensing on the cloud less attractive.

And while McGlennon believes that will change over time, he is far more focused on technologies that will define the next generation of applications.

Were really trying to understand the metaverse and what it might mean for us, says McGlennon, whose mild Irish brogue bares his Galway, Ireland, upbringing. Were focused on augmented reality and virtual reality. Were doing a lot on AI and machine learning and robotics. Weve already built up blockchain and well continue with all those.

And that ability to push the envelope, especially around machine learning and AI, finds its foundation in Liberty Mutuals rich cloud capabilities.

Despite his laser focus on embracing emerging technologies, McGlennon remains highly enthusiastic about Liberty Mutuals use of and expertise in the cloud. Sixty percent of the insurers global workloads run in the cloud, delivering significant savings in hardware and software purchasing, but the big benefit comes in the form of business insights from analytics on the cloud that are immeasurable, he says.

The cloud has been a huge positive impact on us economically and surely you hear this story all the time, but it didnt necessarily start out that way, he says. It tended to be additive to our legacy platforms when we started building out our cloud initially, but more recently, weve become far more mature in our use of the cloud and in our ability to optimize it to make sure that every single cycle of a CPU that we use out in the cloud is adding value.

Here, McGlennon says governing controls, instrumentation, and observability metrics are key. The CIO would not specify how much the multinational company has saved by deploying workloads to the cloud but estimated it has saved about 5% over the past two and a half years. Its a big number, he says.

Implementing cloud-native architectures for autoscaling and instrumenting Liberty Mutuals applications to control how theyre performing have been crucial to realizing those savings, McGlennon says.

Like many other early cloud adopters, Liberty Mutual deploys off-the-shelf tools such as Apptio to monitor costs and automate scaling depending on workloads, he says.

Weve worked with our cloud partners to better instrument our applications and better understand how theyre performing, says McGlennon, who was a finalist for the MIT Sloan CIO Leadership Award for 2022. That gives us greater insight into where we are potentially wasting resources and where we can optimize such as moving workloads to smaller cloud platforms.

McGlennon is proud of his teams use of Apptio, for example, to best exploit its consumption-oriented model for not just its data on the cloud but for its internal services, software, and SaaS offerings, which, when linked to Liberty Mutuals business portfolio, essentially provides the insurers partners with a bill of materials for all of the resources used.

Over the past eight years, the Liberty Mutual IT team, which consists of 5,000 internal IT employees and about 5,000 outside contractors, has used a variety of development platforms and analytical tools as part of its cloud journey, spanning from IBM Rational and .NET in the early days to Java and tools such as New Relic, Datadog, and Splunk.

Liberty Mutuals data scientists employ Tableau and Python extensively to deploy models into production. To expedite this, the insurers technical team built an API pipeline, called Runway, that packages models and deploys them as Python, as opposed to requiring the companys data scientists to go back and rebuild them in Java or another language, McGlennon says.

Its really critical that we can deploy models quickly without having to rebuild them in another platform or language, he adds. And to be able to track the effectiveness of those machine learning models such that we can retrain them should the data sets change as they often do.

The insurer also uses Amazon Sage Maker to build machine learning models, but the core models are based on Python.

Liberty Mutuals IT team has also created a set of components called Cortex to enable its data scientists to instantiate the workstations they need to build a new model so the data scientist doesnt have to worry about how to build out the infrastructure to start the modeling process, McGlennon says.

With Cortex, Liberty Mutuals data scientists can simply set their technical and data-set requirements, and a modeling workstation will be created on AWS with the right data and tools in an appropriately sized GPU environment, McGlennon explains.

The insurer also deploys software bots in its claims model to enable customers to initiate a claim, e-mail a digitized photograph of their damaged vehicle, answer a few questions, and arrange a car rental quickly. On the back end, a machine learning model analyzes the photograph of the damaged vehicle to detect whether its airbag has been deployed, for instance, and to determine immediately whether a vehicle is totaled or the damage is limited to a fender bender.

The insurers computer vision models may also tap into IoT devices and sensors deployed outside to generate more data for the claim.

Liberty Mutual has come a long way from its technology manifesto to its advanced use of the cloud and AI, and embracing next-generation technologies such as augmented reality and blockchain will yield further advances, McGlennon notes.

But this CIO is happy enough with the cloud and AI platform of today.

Weve already seen significant economic payback from being able to use machine learning models to fine-tune quotes and pricing, in fraud detection, and our coding process to make it easier for customers to do business with us, McGlennon says, pointing to advanced cloud applications benefits in its core business of processing claims. We use it all over the place.

Although his is a property and casualty company, McGlennon believes CIOs must drive innovation and take risks to create a culture where people feel there is the latitude to try something.

Risk is our business, McGlennon said during a panel at the MIT Sloan CIO Symposium this week, adding that CIOs need to show that when things go wrong, and sometimes they will, no one is going to be made to feel that the risk wasnt worth it.

You have to incubate something, nurture it, give it support, he said.

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AI in the cloud pays dividends for Liberty Mutual - CIO

CHICAGO To some, artificial intelligence (AI) may sound futuristic, but AI is here today and the technology can help convenience store retailers with their marketing campaigns.

"Using AI saves you time, effort and money," said Ryan DiLello, content specialist for Paytronix Systems Inc., during a recentConvenience Store Newswebinar."It ensures you do not have one-size-fits-all campaigns. It allows you to learn more about your customers and meet their needs."

Specifically, AI can help c-store retailers learn more about their customers' behavior and value; segment customers in more exacting ways; make more compelling, personalized offers; maximize channel efficiency; and, find ideal customers in the marketplace using AI-constructed profiles.

AI-driven marketing operates through four stages, stated DiLello. They are:

One thing AI can do is provide data regarding how likely customers are to visit a c-store, as well as open emails with provided targeted offers. The data can, for example, show which days a customer is visiting a store. If a customer visits exclusively on weekdays, AI can generate targeted offers to try to encourage consumers to visit on the weekend.

AI can also help launch "Missed Visit Campaigns," which recognizes individual lapses in guest behavior and identifies guests "out of their rhythm." According to DiLello, results from the first seven days of a Missed Visit Campaign revealed guest visits increased by 42 percent and in-store spending rose by 19 percent.

Geofencing, when AI notices when a customer is near a store and provides targeted offers and promotions, is another way to draw in-store traffic.

"We have seen great results using geofencing," he said.

C-store retailers have also used K-Means Clustering. Through this method, AI looks at popular pairing items and makes recommendations based on trends in the data. Further, K-Means Clustering helps develop unique and personalized guest experiences intended to keep customers returning to the store.

"AI recommends coupon offers," DiLello said. "It is a low-risk way to win back customers."

AI can help c-store retailers beyond just marketing, making their implementation well worth the investment, DiLello stressed. The technology takes it one step further by helping to determine a key metric: customer lifetime value (CLV). AI calculates how long a person has been in a loyalty program, what the average visit cadence looks like, when the most recent visit was, how much customers spend per visit and how long the consumer is likely to stay active. These data sets are important to identify top customers, segment more effectively, optimize acquisition and realize lift or a customer's lifetime journey DiLello pointed out.

The objectives of CLVs are to identify and reward most valuable customers, and find lower-value customers and boost their CLV.

"People often ask what a good CLV to customer acquisition cost (CAC) ratio is. We often say a three-to-one ratio is good," DiLello explained.

Retailers can lower CAC by retaining customers longer, reducing media and advertising expenses, and reducing third-party marketplace fees, he added.

In the future, DiLello expects AI to provide c-store retailers with practical uses in operations. For example, robotic servers, kiosks with facial recognition, food waste reduction management, inventory management and smart routing for delivery are ways AI can benefit c-store operators down the road.

"Inventory management is big for c-stores,"he said. "AI will allow retailers to cruise through an unsteady supply chain to order items months in advance."

An on-demand replay of this webinar, "Get Smart: How You Can Personalize the Customer Experience With AI,"is availablehere.

See more here:

C-store Retailers Can Personalize the Customer Experience With AI - CSNews Online

Two years ago this weekend, GPT-3 was introduced to the world.

You may not have heard of GPT-3, but there's a good chance you've read its work, used a website that runs its code, or even conversed with it through a chatbot or a character in a game.

GPT-3 is an AI model a type of artificial intelligence and its applications have quietly trickled into our everyday lives over the past couple of years.

In recent months, that trickle has picked up force: more and more applications are using AIlike GPT-3, and these AI programsare producing greater amounts of data, from words, to images, to code.

A lot of the time, this happens in the background; we don't see what the AI has done, or we can't tell if it's any good.

But there are some things that are easy for us to judge: writing is one of those.

From student essaysto content marketing, AI writing toolsare doing what only a few years ago seemed impossible.

In doing so, the technology ischanging how we think about what has been considered a uniquelyhuman activity.

And we have no idea how the AI models aredoing it.

Danny Mahoney's workmatenever leaves, sleeps, or takes a break.

Day after day,the AI writing assistant churns outblog posts, reviews, company descriptionsand the like for clients of Andro Media, Mr Mahoney'sdigital marketing company in Melbourne.

"Writers are expensive. And there's a limit to how much quality content a human can produce," Mr Mahoney says.

"You can get the same quality of content using AI tools. You just get it faster."

How much faster? About three times, he estimates.

He still has to check and edit the AI-generated text, but it's less work and he's cut his rates by half.

"Every SEO [Search Engine Optimisation] agency that I've spoken with uses AI to some extent."

In Perth, Sebastian Marks no longer bothers with content agencies at all.

About a year ago, he saw an ad for an AI writing assistant and signed up.

The AI tool nowwrites pretty much everything for his company, Moto Dynamics, whichsells motorcycles and organises racing events.

Its output includes employee bios, marketing copy, social media posts, and business proposals.

"Once we'd started feeding data into it and teaching it how to work for us, it became more and more user-friendly," he says.

"Now weuse it essentially as an admin."

The particular AI writing tool Mr Mahoney uses is calledContentBot, which like many of its competitors was launched early last year.

"It was very exciting," says Nick Duncan, the co-founder of ContentBot, speaking from Johannesburg.

"There was a lot of word of word of mouth with this technology. It just sort of exploded."

The trigger for this explosion was OpenAI's November 2021 decision to make its GPT-3 AIuniversally available for developers.

It meant anyone could payto access the AI tool, which had been introduced in May 2020 for a limited number of clients.

Dozens of AI writing tools launched in early 2021.

LongShot AIis only a year old, but claims to have 12,000 users around the world, including in Australia.

"And there are other products that would have ten-fold the number of clients we have,"says its co-founder,Ankur Pandey, speaking from Mumbai.

"Revolutionary changes in AI happened in the fall of 2020.This whole field has completely skyrocketed."

Companies likeContentBot andLongshot payOpenAI for access to GPT-3:the rate of the most popular model (Davinci) is about $US0.06 per 750 words.

In March 2021, GPT-3 was generating an average of 4.5 billion words per day.

We don't know the current figure, but it would be much higher given the AI is being more widely used.

"It's been a game changer," Mr Duncan says.

There are dozens of AI writing tools that advertise to students.

Among them isArticle Forge,a GPT-3 powered toolthat claims itsessayscan pass the plagiarism checkers used by schools and universities.

Demand for the product has increased five-foldin two years, chief executive officer Alex Cardinell says.

"It's the demand for cheaper content with shorter turnaround times that requires less overall effort to produce.

"People do not want AI, they want what AI can do for their business."

Lucinda McKnight, a curriculum expert at Deakin University, confirms that students are early adopters of AI writing tools.

"I can tell you without doubt that kids are very widely using these things, especially spinners on the internet."

Spinners are automated tools thatrephrase and rewrite content so it won't be flagged for plagiarism.

"It can produce in a matter of seconds multiple different copies of the same thing, but worded differently."

These developments are shifting ideas around student authorship.If it becomes impossible to distinguish AI writing from human, what's the point in trying to detect plagiarism?

"We should be getting studentsto acknowledge how they've used AI as another kind of source for their writing," Dr McKnight says.

"That is the way to move forwards, rather than to punish students for using them."

When GPT-3 launched two years ago, word spread of its writing proficiency, but access was limited.

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Recently, OpenAI has thrown open the doors to anyone with a guest login, which takes a few minutes to acquire.

Given the prompt "Write a news story about AI", the AI toolburped out three paragraphs. Here's the first:

"The world is on the brink of a new era of intelligence. For the first time in history, artificial intelligence (AI) is about to surpass human intelligence. This momentous event is sure to change the course of history, and it is all thanks to the tireless work of AI researchers."

In general, GPT-3is remarkably good at stringing sentences together, though plays fast and loose with the facts.

Asked to write about the 2022 Australian election, it claimed the vote would beheld on July 2.

But it stillmanaged to sound like it knew what it was talking about:

"Whoever wins the election, it is sure to be a close and hard-fought contest. With the country facing challenges on many fronts, the next government will have its work cut out for it."

Mr Duncan says you "can't just let the AI write whatever it wants to write".

"It's terrible atfact-checking. It actually makes up facts."

He uses the tool as a creative prompt: the slog of writing from scratch is replaced byediting and fact-checking.

"It helps you overcome the blank-page problem."

Mr Mahoney agrees.

"If you produce content purely by an AI, it's very obvious that it's written by one.

"It's either too wordy or just genuinely doesn't make sense."

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But with proper guidance, GPT-3 (andother AI writing tools) can be good enough for standard professional writing tasks like work emails or content marketing, where speed is more important than style.

"People who create content for marketing tend to use it every day,"Longshot'sAnkur Pandey says.

"Most of the focus of this industry is content writers,content marketers and copywriters, because this is mission critical for them."

Then there's coding: In November 2021, a third of the code on GitHuba hosting platform for code was being written with Copilot, a GPT-3 powered coding tool that had been launched five months earlier.

US technological research and consulting firm Gartner predicts that by 2025, generative AI (like GPT-3) will account for 10 per cent of all data produced, up from less than 1 per cent today.

That data includes everything from website code and chatbot platforms to image generation and marketing copy.

"At the moment, content creation is mostly using generative AI to assist as part of the pipeline," says Anthony Mullen, research director for AI at Gartner.

"I think that will persist for a while, but it does shift the emphasis more towards ideas, rather than craft.

"Whether it is producing fully completed work or automating tasks in the creative process,generative AI will continue to reshape the creative industries.

"This technology is a massive disruptor."

Until recently, decent text generationAI seemed a long way away.

Progress in natural language processing (NLP),or the ability of a computer program to understand human language, appeared to be getting bogged down in the complexity of the task.

Then, in 2017, a series of rapid advancements culminated in a new kind of AI model.

In traditional machine learning, a programmer teaches a computer to, for instance, recognise if an image does or does not containa dog.

In deep learning, the computer is provided with a set of training data eg. images tagged dog or not dog that it uses to create a feature set for dogs.

With this set, it creates amodel thatcan then predictwhether untagged images do or do not contain a dog.

These deep learning models are the technology behind, for instance, the computer vision that's used in driverless cars.

While working on ways to improve Google Translate, researchers at the companystumbled upon a deep learning model that proved to begood at predicting what word should come next in a sentence.

Called Transformer, it'slike a supercharged version of text messaging auto-complete.

"Transformer isa very, very good statistical guesser," says Alan Thompson, an independent AI researcher and consultant.

"It wants to know what is coming next in your sentence or phrase or piece of language, or in some cases, piece of music or image or whatever else you've fed to the Transformer."

At the same time, in parallel to Google, an Australian tech entrepreneurand data scientist, Jeremy Howard, was finding new ways to train deep learning models on large datasets.

Professor Howard, who would go on to become an honorary professor at the University of Queensland,had moved to San Francisco six years earlier, from Melbourne.

He proposed feeding Transformer a big chunk of text data and seeing what happened.

"So in 2018, the OpenAI team actually tookProfessor Jeremy Howard's advice and fed the original GPTwith a whole bunch of book data into this Transformer model,"Dr Thompson says.

"And they watched as it was able to complete sentences seemingly out of nowhere."

Transformer is the basis forGPT(which stands for Generative Pre-trained Transformer), as well as other current language models.

ProfessorHoward's contribution is widely recognised inSilicon Valley, but not so much in Australia, to which he recently returned.

"In Australia, people will ask what do you do and I'll be like, 'I'm aprofessor in AI'. And they say, 'Oh well, how about the footy?'" he says.

"It's very, very different."

The short answer is that, beyond a certain point, we don't know.

AI like GPT-3 are known as "black boxes", meaning it's impossible to know the internal process of computation.

The AI has trained itself to do a task, but how it actually performsthat task is largely a mystery.

"We've given it this training data and we've let it kind of macerate that data for months, which is the equivalent of many human years, or decades even," Dr Thompson says.

"And it can do things that it shouldn't be able to do. It taught itselfcoding and programming. It can write new programmes that haven't existed."

As you might guess, this inability to understand exactly how the technology works is a problem fordriverless cars, which rely on AI to make life-and-death decisions.

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Danny's workmate is called GPT-3. You've probably read its work without realising it's an AI - ABC News

[Alexander] created codex_py2cpp as a way of experimenting with Codex, an AI intended to translate natural language into code. [Alexander] had slightly different ideas, however, and created codex_py2cpp as a way to play with the idea of automagically converting Python into C++. Its not really intended to create robust code conversions, but as far as experiments go, its pretty neat.

The program works by reading a Python script as an input file, setting up a few parameters, then making a request to OpenAIs Codex API for the conversion. It then attempts to compile the result. If compilation is successful, then hopefully the resulting executable actually works the same way the input file did. If not? Well, learning is fun, too. If you give it a shot, maybe start simple and dont throw it too many curveballs.

Codex is an interesting idea, and this isnt the first experiment weve seen that plays with the concept of using machine learning in this way. Weve seen a project that generates Linux commands based on a verbal description, and our own [Maya Posch] took a close look at GitHub Copilot, a project high on promise and concept, but at least at the time considerably less so when it came to actual practicality or usefulness.

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AI Attempts Converting Python Code To C++ - Hackaday

Ireland has appointed Dr Patricia Scanlon as its first AI Ambassador to facilitate the Governments AI adoption strategy launched last year. Patricia is the founder and former executive chairperson of the speech recognition tech firm called SoapBox Labs. Irelands AI Here For Good strategy focuses on how technology can be utilised in human-centric and ethical ways to improve the lives of its citizens.

Dr Scanlon, a member of the Enterprise Digital Advisory Forum (EDAF), will work closely with the Department of Enterprise, Trade and Employment.

She will work on demystifying AI and promoting the positive impacts it can have in areas such as transport, agriculture, health and education.

The Department of Trade, Enterprise and Employment announced the national AI strategy, AI Here for Good, on 8th July 2021.

The strategy outlined Irelands plan to become a global leader in artificial intelligence to benefit its economy and society, with a people-centred, ethical approach to AI development, adoption and use. Further, Ireland will join the Global Partnership on AI and continue to take part in EU discussions and define a framework for trustworthy AI.

The government also plans to identify areas AI researchers from Ireland could collaborate with other countries. As part of a broader strategy, higher education institutions are encouraged to design AI-related courses and employers are urged to facilitate workplace-focused AI upskilling and reskilling.

Additionally, National Youth Assembly on Artificial Intelligence is set to take place in September 2022 to address concerns of youth around AI and to promote STEM careers.

The AI Ambassador appointment came weeks after the United States Department of Defense appointed Dr Craig Martell as the Chief Digital and Artificial Intelligence Officer (CDAO)a newly created position. The role was created to monitor data and AI initiatives under one official at the highest levels of the Pentagon. The CDAO reports directly to the deputy secretary of defence.

Countries worldwide are engaged in an AI arms race, sometimes literally. For example, in the ongoing Russia-Ukraine war, the former had used AI-based drones to unleash terror on Ukrainian cities. Ukraine, on its part, has also taken help from the US firm Clearview AI to uncover the Russian assailants and combat misinformation.

In Xinjiang and Tibet, China uses AI-powered technology to combine multiple streams of informationincluding individual DNA samples, online chat history, social media posts, medical records, and bank account informationto track citizens.

India is behind the US, China, the UK, France, Japan and Germany in the top AI adopters list. Canada, South Korea and Italy round out the top 10.

In October 2016, the Obama administration released a report titled Preparing for the Future of Artificial Intelligence, addressing concerns around AI like its application for the public good, economic impact, regulation, fairness and global security. In addition, the US government also released a companion document called the National Artificial Intelligence Research and Development Strategic Plan 3, which formed the benchmark for Federally-funded research and development in AI. These documents were the first of a series of policy documents released by the US regarding the role of AI.

The United Kingdom announced its national development strategy in 2020 and issued a report to accelerate the application of AI by government agencies. In 2018, the Department for Business, Energy, and Industrial Strategy released the Policy Paper AI Sector Deal. The Japanese government released its paper on Artificial Intelligence Technology Strategy in 2017. The European Union launched SPARC, the worlds largest civilian robotics R&D program, in 2014. Developing countries such as Mexico and Malaysia are in the process of creating their national AI strategies.

In recent years, the Indian government has launched several initiatives at state and national levels.

In 2018, the Indian government published two AI roadmaps the Report of Task Force on Artificial Intelligence by the AI Task Force constituted by the Ministry of Commerce and Industry and the National Strategy for Artificial Intelligence by Niti Aayog.

The National Roadmap for Artificial Intelligence by NITI Aayog proposed creating a National AI marketplace. In particular, the data marketplace would be based on blockchain technology and offer features like traceability, access controls, compliance with local and international regulations, and a robust price discovery mechanism for data.In 2022, the government increased the budget expenditure from INR 6,388 crores to INR 10,676.18 crores for the Digital India programme to boost AI, machine learning, IoT, big data, cybersecurity and robotics. Indias flagship digital initiative plans to make the internet more accessible, promoting e-governance, e-banking, e-education and e-health.

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Ireland gets its first AI ambassador. Will other countries follow suit? - Analytics India Magazine

As vendor events go, Microsoft Build is one of the more interesting because it focuses on the people who create things.

While Build is mostly about software, theres usually a considerable amount of information on hardware that can be, at times, revolutionary. Major breakthroughs for both software and hardware dont typically happen at the same show, but this year we had new ARM-based, four-processor PCs and AI applications that address what is the most pervasive problem in computing that has been largely unaddressed since its creation: Enabling users to interact easily and naturally with PCs.

Also read: Top Artificial Intelligence (AI) Software 2022

The hardware announcement was Project Volterra, which boasts four processors, two more than the typical CPU and GPU weve known for years. The third processor is called a Neural Processing Unit focused on AI loads and handles them faster while using far less energy than CPUs or GPUs, according to Microsoft.

The fourth processor Im calling an ACU, or Azure Compute Unit, and it is in the Azure Cloud. This is arguably the first hybrid PC sharing load between the cloud and the device, which is stackable if more localized performance is needed. Volterra may look like a well provisioned small-form factor PC. However, while its targeted at creating native Windows ARM code, it is predictive of the ARM PCs well see on the market once this code is available.

As fantastic as this new hardware is, Microsoft is a software company with a deep history in development tools that goes all the way back to its roots. A huge problem computing has had since its inception is that people have to learn how to interact with the machines, which makes no sense in an ideal world.

Why would you build a tool that people have to work with and then create programming languages that require massive amounts of training? Why not put in the extra work and do it so we can communicate with them like we communicate with each other? Why not create a system to which we can explain what we want and have the computer create it?

Granted a lot of us have trouble explaining what we want, but at least getting training in doing that better would have broad positive implications for our ability to communicate overall, not just communicate with computers. In short, having computers respond to natural language requests would force us to train people how to generally communicate better, leading to fewer conflicts, fewer mistakes, and far deeper and more understanding relationships, not just with computers, but with each other. Something I think you can agree we need now.

Also read: Microsoft Embraces the Significance of Developers

The featured offering is a release coming from GitHub called Co-Pilot, which collaboratively builds code using an AI. It will anticipate what needs to be done and suggest it, and it will provide written code that corresponds to the coders request. Not sure how to write a command? Just ask how one would be done and Co-Pilot will provide the answer.

Microsoft provided examples of several targeted AI-driven Codex prototypes as well. One seemed to go farther by creating more complete code, while another, used for web research, didnt just identify the source but would pull out the relevant information and summarize it. I expect this capability will find its way underneath digital assistants, making them far more capable of providing complete answers in the future.

A demonstration that really caught my attention was on OpenAIs DALL-E (pronounced Dolly). This is a prototype program that will create an image based on your description. One use: Young schoolchildren who use their imaginations to describe a picture of an invention they had thought up, which led to shoes made of recycled trash, a robotic space trash collector, and even a house kind of like the Jetsons apartment that could be raised or lowered according to the weather.

Right now, due to current events, Im a bit more focused on children this week, but I think a tool like this could be an amazing way to visualize ideas and convey ideas better. They say a picture is worth a thousand words; this AI could create that picture with just a few words. While cartoonish initially (this can be addressed with several upscaling tools from companies like AMD and NVIDIA), they nevertheless excited and enthralled the kids. It was also, I admit, magical for me.

Microsoft Build showed me the best future of AI. Applied not for weapons or to convince me to buy something I dont want (extended car insurance anyone?), but to remove the drudgery from coding, enabling more people with less training to create high-quality code, translate imagination into images and make digital assistants much more useful.

Ive also seen the near-term future of PCs, with quad processors, access to the near unlimited processing power of the web (including Microsoft Azure Supercomputers when needed), and an embedded AI that could use the technology above to help that computer learn, for once, how to communicate with us and not the other way around.

This years Microsoft Build was, in a word, extraordinary. The things they talked about will have a significant, and largely positive, impact on our future.

Read next: Using Responsible AI to Push Digital Transformation

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Microsoft Build Showcases 4-Processor PCs and Useful AI Apps - IT Business Edge

While the use of AI technologies is proving effective as a tool to help stop cyber criminals, the Federal government continues to faces an uphill road in deploying the technology, a U.S. Secret Service official said this week.

Roy Dotson Jr., Acting Special Agent in Charge, USSS National Pandemic Fraud Recovery Coordinator, at the U.S. Secret Service, said at a May 26 ATARC event entitled Impact of AI and Machine Learning on Financial Crime Investigation that the Federal government still lacks some of the professional resources it needs to further implement AI tech to deter financial crimes.

Were extremely limited, its very difficult for us to hire experienced data scientists, forensic accountants those [people] in the fields that would be very beneficial to us, he said.

Dotson also talked about strategic options for AI deployments that would better help deter cyber criminals.

Im a big proponent of being proactive instead of reactive, he said. Thats what I would love to see, so that we can be on the same playing field as the more complex cybercriminal, that would give us a leg up, Dotson said. There is also different AI that Id love to see be used as well, he added.

While the Federal government is still facing AI implementation issues, Dotson explained how the technology has already been helping to stop cyber crimes.

It gives us a better chance of working cases faster, identifying suspects quicker, and that helps us to possibly apprehend people that we might not have a chance because of the time delay that other traditional means that take longer going through data, he said.

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Feds Facing Uphill Road to Deploy AI Tech in Cyber Fight - MeriTalk