For years, physicists have been making major advances and breakthroughs in the field using their minds as their primary tools. But what if artificial intelligence could help with these discoveries?

Last month, researchers at Duke University demonstrated that incorporating known physics into machine learning algorithms could result in new levels of discoveries into material properties, according to a press release by the institution. They undertook a first-of-its-kind project where theyconstructed a machine-learning algorithm to deduce the properties of a class of engineered materials known as metamaterials and to determine how they interact with electromagnetic fields.

The results proved extraordinary. The new algorithm accurately predicted the metamaterials properties more efficiently than previous methods while also providing new insights.

By incorporating known physics directly into the machine learning, the algorithm can find solutions with less training data and in less time, said Willie Padilla, professor of electrical and computer engineering at Duke. While this study was mainly a demonstration showing that the approach could recreate known solutions, it also revealed some insights into the inner workings of non-metallic metamaterials that nobody knew before.

In their new work, the researchers focused on making discoveries that were accurate and made sense.

Neural networks try to find patterns in the data, but sometimes the patterns they find dont obey the laws of physics, making the model it creates unreliable, said Jordan Malof, assistant research professor of electrical and computer engineering at Duke. By forcing the neural network to obey the laws of physics, we prevented it from finding relationships that may fit the data but arent actually true.

They did that by imposing upon the neural network a physics called a Lorentz model. This is a set of equations that describe how the intrinsic properties of a material resonate with an electromagnetic field. This, however, was no easy feat to achieve.

When you make a neural network more interpretable, which is in some sense what weve done here, it can be more challenging to fine tune, said Omar Khatib, a postdoctoral researcher working in Padillas laboratory. We definitely had a difficult time optimizing the training to learn the patterns.

The researchers were pleasantly surprised to find that this model workedmore efficiently than previous neural networks the group had created for the same tasks by dramatically reducing the number of parameters needed for the model to determine the metamaterial properties. The new model could evenmake discoveries all on its own.

Now, the researchers are getting ready to use their approach on unchartered territory.

Now that weve demonstrated that this can be done, we want to apply this approach to systems where the physics is unknown, Padilla said.

Lots of people are using neural networks to predict material properties, but getting enough training data from simulations is a giant pain, Malof added. This work also shows a path toward creating models that dont need as much data, which is useful across the board.

The study is published in the journal Advanced Optical Materials.

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Artificial intelligence has reached a threshold. And physics can help it break new ground - Interesting Engineering

NEW ORLEANS (WGNO) Locals will have a chance to try the first craft beer created by an artificial intelligence platform in June.

The AI Blonde Ale will be released at a Launch Party at Nola Brewery on June 20to coincide with CVPR, the worlds premier computer vision event.

Derek Lintern, a brewer at NOLA Brewing said he is excited to have a helping hand when it comes to crafting beer.

Its state-of-the-art technology with the traditional brewing methods, its pretty unique and its a recipe I would have never done normally but I really like how it tastes its very refreshing and very easy drinking Im really happy with it, said Lintern.

The beer was an experiment between The Australian Institute for Machine Learning (AIML) and Barossa Valley Brewing (BVB), founded by DSilva.

DSilva said the idea all started with a beer.

Yeah thats how it started, it started with a beer, Im sure a lot of ideas for companies have started over a beer, this started over a beer and ended up creating a beer and a company which is great, said DSilva.

With the technology, it makes it easier for brewers to produce their products.

About 10 million people review beers every day, there are all these sites and they put it into the world basically to show people what they think of the beer. You do exactly the same thing, there are 5 questions, you scan a QR code answer 5 questions you rate the beer and instead of it going into a website maybe somebody reads maybe not. What happens is artificial intelligence picks that up and goes directly to the producer the AI then takes all that data and manipulates a recipe and then gives it to the producer here this is what the markets thinking, said DSilva.

Derek Lintern said the new technology is not meant to replace brewers, but to help with the process.

The technology helps create the recipe, but the beer is still brewed manually.

The AI beer will only be available in New Orleans for a limited time.

DSilva said he is excited to bring something new to an amazing city. I am so excited I cant think of a better place to launch a beer, said DSilva.

He added, I am really keen for people to get down here and taste the future.

Anyone interested in attending the launch of the new beer can visit NOLA Brewing from 4 p.m. to 10 p.m. on Monday, June 20.

Deep Liquid is also offering 100 customers a free AI beer with their booking with Nola Pedal Barge and Nola Bike Bar.

They are offering $100 discount tickets to any of its private tours.

That includes any of the boat tours in Bayou Bienvenue as well as our pedal bike tour in the Bywater neighborhood.

For more information call (504) 264-1056) for NolaPedalBarge and (504) 308-1041 for NolaBikeBar.

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Taste of the future first artificial intelligence-created craft beer to be released at NOLA Brewing - WGNO New Orleans

Have you ever wanted to see a polar bear riding a skateboard? What about a hot dog wearing a tracksuit?

Well, if youll settle for AI-generated images of those things or anything else you can dream up then youll appreciate DALLE mini, the free website currently taking over the internet.

It may sound like sci-fi, but the premise is simple: On your phone or computer, go to huggingface.co/spaces/dalle-mini/dalle-mini. Type out any prompt in the text box for example, Dak Prescott holding a banana. Hit the button that says Run (you may need to hit it multiple times before traffic subsides and your request goes through).

Eventually, nine images generated completely by artificial intelligence will appear, bringing your concept to life with varying levels of accuracy and hilarity. In the case of Dak Prescott holding a banana, the results were good for a laugh, but stopped short of realism see below.

The ripe-for-memes program was created by Boris Dayma, a machine learning engineer based in Houston. He made the website available for public use last year, but only in the past two weeks has it taken off in social media popularity, with users sharing images of everything from Darth Vader ice fishing to Karl Marx making an appearance in Seinfeld. A Twitter account that shares some of the weirdest creations has racked up over 600,000 followers.

Dayma was inspired to build the program after reading a research paper about DALLE, a sophisticated text-to-image artificial intelligence program created by OpenAI, an artificial intelligence company co-founded by Elon Musk. Last summer, as part of a program organized by the AI company Hugging Face, Dayma and a team developed DALLE mini, a scaled-down version that, unlike the original program, is open to the public. (There is currently a waitlist to access the original DALLE).

Being able to create an image that looks like what you wanted, on the technical level, to me, it was very interesting, said Dayma. I want to be able to try it out myself and I want to be able to let other people use it.

The way the DALLE mini program works, Dayma said, is by processing images and captions from across the internet. Slowly, the program begins to discern patterns, such as a visual patch of blue when the caption indicates sky. When a user types in a text prompt, the program, using these associations, will try to put it together to make something that makes sense, Dayma said.

It learns very tiny concepts like that, and over time, it becomes better and better, he said.

Demand for the app, Dayma confirmed, has soared as of late. Many users now complain of getting a pop-up saying, Too much traffic, please try again, when they try to generate images.

We obviously didnt plan for such crazy traffic, so weve been working on improving the code, improving the model, said Dayma. People seem to like it, so they need to be able to use it.

Despite the wait times, Dayma said the public nature of the program is an asset to the technology. Beyond the futuristic entertainment it provides the masses, the program is open source, meaning the code is publicly available, so some people are able to play with the model itself and program and tweak it, he said. Since he is still training the model to produce better images, input from other users proves valuable.

People can learn about the limitations of the model, the biases, what its good at, what it can be used for, he said. Everybody can benefit from having a public model like this.

After improvements are made on the traffic capacity and the model itself, Dayma said, the sky is the limit. You can generate videos, you can generate music, he said. Its a new area thats opening up.

Its an area, however, thats fraught with controversy. Experts have raised concerns that artificial intelligence technology will perpetuate biases or promote disinformation. But with DALLE mini, Dayma said, the quality is just not there for most people to be fooled by the images at least for now. By bringing AI out of the ivory towers of Silicon Valley and into the hands of anybody with a smartphone, Dayma said, he is hoping not only to amuse, but also to sound the alarm.

At least people can learn that that type of thing is coming, and now you need to be aware with the content that you see online, he said. I hope it helps people develop their critical thinking.

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DALL-E mini is the viral artificial intelligence artist taking over Twitter - The Dallas Morning News

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Register today!

Artificial intelligence (AI) technologies are considered essential for internet of things (IoT) hardware for digital operations, such as cameras and automation equipment, according to a survey from Samsara released today.

Samsara, which makes IoT hardware and software, surveyed more than 1,500 operations leaders for its 2022 State of Connected Operations survey, in industries including transportation, manufacturing, construction, field services and food and beverage. The survey was conducted by the independent research firm Lawless Research.

Organizations with physical operations represent more than 40% of global gross domestic product, yet theyve been historically underserved by technology, said Stephen Franchetti, Samsaras CIO.

The IoT market is booming: A March 2020 Insider Intelligence report, for example, predicted that the IoT market size would reach more than $2 trillion by 2027.

The pandemics supply chain interruptions have only underpinned the need for increased investment in IoT. For instance, in late 2021, when the effects of the pandemic were already being felt, the market research firm Gartner discovered industrial enterprises were speeding investments in industrial IoT (IIoT) platforms to improve business and industrial processes.

The IoT and IIoT acronyms are widely used interchangeably, though the IoT is generally applicable to consumer and home devices, such as thermostats and lights, while the IIoT connects physical industrial systems. It also analyzes data returned from those systems for operational improvement.

In industry, the IIoT monitors conditions on, for example, a manufacturing line and predicts which machines will soon need maintenance, among other uses. It unlocks data that was previously housed in data silos, Gartner says.

And its vital to Industry 4.0 adoption, according to McKinsey. The technology holds the key to unlocking drastic reductions in downtimes, new business models, and a better customer experience, the consulting company reports.

Ninety percent of respondents to the Samsara survey said they implemented or plan to implement AI automation technologies connected via the IoT.

AI and automation will play a significant role in the safety and efficiency of physical operations and were already seeing this with our customers today, Franchetti said.

In fact, 95% of those surveyed said AI and automation efforts led to increased employee retention, he said.

Our research found that 31% of respondents benefited from less time spent on repetitive tasks and 40% higher employee engagement as a result of AI and automation, he explained.

Franchetti pointed to Chalk Mountain Services, a transportation and logistics provider in the oilfield services industry. The company rolled out Samsaras AI Dash Cams across its fleet last year to study how drivers safely handled real-world conditions. With that information, the company changed how it rewarded, coached and protected drivers.

The changes translated to a 15% improvement in driver retention and an 86% decrease in preventable accident costs, Franchetti said.

Whats significant about our research is we found that early adopters of digital technologies are proving to be more agile and resilient, he said. While pen-and-paper management is still a stark reality for many companies, they can now clearly see the benefits of digitization from their industry peers.

The combination of AI tools and IoT hardware, particularly when it comes to connecting digital operations, shows no signs of slowing down over the next few years, so organizations should be prepared. These technologies will be widespread soon, and operations leaders should see them as a critical tool in defining their future of work, Franchetti said.

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Big business benefits from artificial intelligence in IoT & IIoT hardware - VentureBeat

Artificial intelligence (AI) is everywhere, and it has an impact on all our lives.

However, years of bold proclamations have resulted in AI becoming overhyped, with reality often falling short of the world-altering promises.

The coming years will be more about practical uses of AI, as businesses ensure return on investment by using AI to address specific cases.

Power Technologys artificial intelligence in power dashboard covers all you need to know about this emerging technology and its impact on the sector.

The power sector, especially in Europe is expected to be impacted due to gas availability and price issues. Utilities will have to look for alternate sources of gas or shift to other sources of generation. AI in power industry usage is likely to be impacted alongside many other corporate tools.

The recent ban on Russian oil and gas supplies will have a varied impact on both the buying and selling nations. Russia is an important source of energy supply for the US and European countries.

Pressure on the Western Bloc to impose more sanctions on Russian energy imports due to civilian killings in Ukraine by Russian Army. Fuel prices (such as for oil) have increased due to talks of a boycott of Russian oil.

Energy companies continue to exit or halt operations in Russia due to increasing pressure to cut ties amid civilian killings in Ukraine.

The electric vehicle and energy storage market will be impacted due to a shortage of nickel and an increase in commodity prices.

The International Energy Agency (IEA) recently published A 10-Point Plan to Reduce the European Unions Reliance on Russian Natural Gas, providing short-term measures and claiming that the EU could cut Russian gas imports by more than 33%.

It also advocates for gas-to-coal switching that could account for the majority of the potential reduction in gas demand.

GlobalData estimates that the global AI platform market will be worth $52bn in 2024, up from $28bn in 2019.

Total spending on AI technology is certainly higher, but it is difficult to estimate. There are two main reasons for this.

Firstly, AI is an intrinsic part of many applications and functions, making it almost impossible to identify revenue explicitly generated by AI.

Secondly, the range of sub-sets and technologies that make up AI can be challenging to locate and track. In general, valuations of the overall AI market range from a few billion dollars to several trillion, depending on the source.

Rather than attempting to size the market, some companies have tried to forecast its economic impact. A PwC report in 2017 estimated that AI would add $15.7 trillion to the global economy by 2030 and boost global GDP by up to 14%.

Global AI platform revenue will reach $52bn by 2024, up from $28bn in 2019.

The competitive landscape for AI is highly fragmented. Companies are investing considerable sums, and there is a swath of innovative AI start-ups that possess innovative expertise. When it comes to the use of artificial intelligence in power industry terms, the competition is constant.

Yet, there is no denying that companies with access to large repositories of data to power AI models are leading the development of AI.

Big Tech excels in this regard, and several tech giants set the overall tone in AI. GAFAM (Google, Apple, Facebook, Amazon, and Microsoft), BAT (Baidu, Alibaba, and Tencent), early-mover IBM, and the two hardware giants Intel and Nvidia are key players within the field.

All industries are feeling the impact of AI, with established incumbents coming up against game-changing disruption from AI platforms developed either by technology giants such as Amazon, Google, and Microsoft; or AI-focused start-ups, such as Lemonade, Trax, and Butterfly Network.

It is not only companies that are making AI investment a priority, but countries as well.

China is the most obvious example, having pledged to become the world leader in AI by 2030, but governments in several nations are backing large spending projects to make sure they do not miss out on AIs positive effects.

The US remains the dominant player in the development of AI technologies, accounting for almost one-third of AI platform revenues in 2019, according to GlobalData estimates.

In a 2019 report from the Center for Data Innovation that compared China, the European Union (EU), and the US in terms of their relative standing in the AI economy, the US came out on top in four out of the six categories of metrics that were examined, including talent, research, development, and hardware.

China led in data and adoption, but its advantage in AI adoption was due to a strong position in a limited number of AI technologies such as facial recognition and smart surveillance.

These are related to the governments extensive use of surveillance and are unlikely to create benefits across the economy.

The US and Europe have a sizable lead in terms of access to high-quality talent and research, and the US has the most AI start-ups and a more developed private equity and venture capital ecosystem.

Therefore, while China is making considerable investments, the USs structural advantages may even enable it to extend its lead.

Discussions about the race for AI dominance tend to focus on the US and China, but other countries are also in the race. Japan has long been at the forefront of AI when it comes to robotics.

The Japanese government released its AI strategy in 2017, and the country boasts a major AI investor in the form of Softbank, which, in 2019, created a $108bn fund to invest in AI companies and opened the Beyond AI institute in Tokyo, a $184m initiative to accelerate AI research in Japan.

In the UK, AI companies secured a record 1.3bn ($1.7bn) of investments in 2019, according to a study by Crunchbase and Tech Nation.

The UK has the second-highest number of AI companies globally, after the US, but most of those companies are small, making them a popular target for acquisition by the tech giants.

Germany is a powerhouse when it comes to the uses of AI in the manufacturing, automotive, and industrial sectors. In 2018, France announced that it would invest 1.5bn ($1.8bn) in AI research until the end of 2022.

Other countries that consider AI an important strategic initiative include South Korea, Russia, Canada, Israel, India, Sweden, Australia, and Singapore.

To best track the emergence and use of artificial intelligence in power, GlobalData tracks patent filings and grants, as well as companies that hold most patents in the field of artificial intelligence.

Power Technology monitors live power company job postings mentioning artificial intelligence or those requiring similar skills.

Jobs postings by power companies mentioning artificial intelligence in recent months. AI jobs tracker in the power sector looks at jobs posted, closed and active in the sector.

As illustrated by the value chain, big data extremely large, diverse data sets that, when analysed in aggregate, reveal patterns, trends, and associations, especially relating to human behaviour and interactions plays a significant role in the development of AI technology.

Big data is produced by all forms of digital activity: phone calls, emails, sensors, payments, social media posts, and much more.

It is also produced by machines, both hardware and software, in the form of machine-to-machine exchanges of data.

These exchanges are particularly important in the IoT (Internet of Things) era, where devices talk to each other without any form of human prompting.

Once collected, big data is typically managed in data centres, either in the public cloud, in corporate data centres, or on end devices. Big data is covered in more detail in our Big Data report.

Originally posted here:
Artificial intelligence companies leading the way in the power industry - Power Technology

Computer vision systems can identify dental deterioration using various object identification and semantic segmentation techniques

FREMONT, CA: AI-powered dental imaging software can assist in swiftly and efficiently making sense of the data. Machine learning algorithms also outperformed dentists in diagnosing tooth decay and predicting whether a tooth should be removed, kept, or restored. Before you worry that a robot will replace the kind human who looks after your teeth, know that ML and computer vision systems are being used to assist your dentist in providing the best possible treatment.

Detection of dental deterioration

Enlisting the assistance of other (computer vision) eyes can increase dentists' capacity to diagnose and treat difficulties in the same. And sometimes, that extra assistance is more valuable than you might think. Computer vision systems can identify dental deterioration using various object identification and semantic segmentation techniques. One method is to train CNNs on large sets of photos, including labeled carious lesions.

Oral cancer screening

While losing a tooth is upsetting, it pales in comparison to the consequences of oral cancer. Furthermore, diagnosing the early signs of oral cancer is not difficult. Visible oral lesions known as "oral potentially malignant disorders" (OPMDs) are a significant indicator of cancer and can be found during routine oral exams by a general dentist. The issue is that this type of inspection is not performed frequently enough during dental visits. If only there were simple, low-cost methods for automating the detection of cancerous or potentially malignant tumors.

Dental caries detection and diagnosis

Early identification of dental caries, like oral cancer, is crucial to preventing irreversible injury. Cavities that are addressed early minimize treatment costs, restoration time, and the chance of tooth loss dramatically. However, computer-aided detection and diagnosis systems (CAD) are gradually becoming a common feature of dental clinics. These technologies can detect oral pathology by reading dental X-rays and cone-beam computed tomography (CBCT) pictures. Furthermore, computer vision-powered systems can assess lesion depth and use this information to detect and classify lesions.

Endodontics

Endodontics is something you've probably heard of if you've ever had a root canal. Fortunately, artificial intelligence (AI) offers applications that can assist dentists in detecting and treating these feared illnesses even more effectively. Endodontists often examine, measure, and evaluate the status of the tooth beneath the gums using radiographic imaging. Deep learning algorithms can then detect, locate, and classify various elements of tooth root anatomy and potential diseases. It is beneficial for locating specific tooth features and identifying particular types of fissures and lesions in or around the tooth.

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Major Applications of Artificial Intelligence in Dentistry - Healthcare Tech Outlook

The use of artificial intelligence (AI) for war has been a promise of science fiction and politicians for years, but new research from the Georgia Institute of Technology claims to show the value that AI can automate only a limited subset of human judgment. "All of the hard problems in AI really are judgment and data problems, and the interesting thing about that is when you start thinking about war, the hard problems are strategy and uncertainty, or what is well known as the fog of war," said Jon Lindsay, an associate professor in the School of Cybersecurity & Privacy and the Sam Nunn School of International Affairs. "You need human sense-making and to make moral, ethical, and intellectual decisions in an incredibly confusing, fraught, scary situation." AI decision-making is based on four key components: data about a situation, interpretation of those data (or prediction), determining the best way to act in line with goals and values (or judgment), and action. Machine learning advancements have made predictions easier, which makes data and judgment even more valuable. Although AI can automate everything from commerce to transit, judgment is where humans must intervene, Lindsay and University of Toronto Professor Avi Goldfarb wrote in the paper, "Prediction and Judgment: Why Artificial Intelligence Increases the Importance of Humans in War," published in International Security.

Many policymakers assume human soldiers could be replaced with automated systems, ideally making militaries less dependent on human labor and more effective on the battlefield. This is called the substitution theory of AI, but Lindsay and Goldfarb state that AI should not be seen as a substitute, but rather as a complement to existing human strategy.

"Machines are good at prediction, but they depend on data and judgment, and the most difficult problems in war are information and strategy," he said. "The conditions that make AI work in commerce are the conditions that are hardest to meet in a military environment because of its unpredictability."

An example Lindsay and Goldfarb highlight is the Rio Tinto mining company, which uses self-driving trucks to transport materials, reducing costs and risks to human drivers. There are abundant, predictable, and unbiased data traffic patterns and maps that require little human intervention unless there are road closures or obstacles.

War, however, usually lacks abundant unbiased data, and judgments about objectives and values are inherently controversial, but that doesn't mean it's impossible. The researchers argue AI would be best employed in bureaucratically stabilized environments on a task-by-task basis.

"All the excitement and the fear are about killer robots and lethal vehicles, but the worst case for military AI in practice is going to be the classically militaristic problems where you're really dependent on creativity and interpretation. But what we should be looking at is personnel systems, administration, logistics, and repairs," Lindsay said.

There are also consequences to using AI for both the military and its adversaries, according to the researchers. If humans are the central element to deciding when to use AI in warfare, then military leadership structure and hierarchies could change based on the person in charge of designing and cleaning data systems and making policy decisions. This also means adversaries will aim to compromise both data and judgment since they would largely affect the trajectory of the war. Competing against AI may push adversaries to manipulate or disrupt data to make sound judgment even harder. In effect, human intervention will be even more necessary.

Yet this is just the start of the argument and innovations.

"If AI is automating prediction, that's making judgment and data really important," Lindsay said. "We've already automated a lot of military action with mechanized forces and precision weapons, then we automated data collection with intelligence satellites and sensors, and now we're automating prediction with AI. So, when are we going to automate judgment, or are there components of judgment cannot be automated?"

Until then, though, tactical and strategic decision-making by humans continues to be the most important aspect of warfare. (ANI)

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An artificial intelligence-based strategy or judgement cannot be trusted by the military, according to researc - Times Now

Guest post from Jingchen Zhao, professor of law at Nottingham Law School

Zhao: AI brings many positive changes

Artificial intelligence (AI) is taking the business world by storm with its capability to collect, filter and react to data rapidly and in many different ways.

Applying AI technology to law firms involves the use of computers, algorithms and big data to assist, support, collaborate or even duplicate lawyers behaviours and decisions so that law firms can function competently, successfully and with foresight in their business environment.

The interconnected world enhanced by technologies such as AI brings many positive changes for the ways in which law firms communicate with their customers, clients and business partners, offering the advantage of providing more an efficient and effective service without compromising quality.

Although AI has not yet been developed to a level where AI-empowered legal advice could fully replace human legal practitioners, the adoption of AI has the potential to reduce transaction costs and improve the accessibility of legal advice through the use of automated assistants, digital hubs or software to offer AI-powered legal services for vulnerable clients.

In collaboration with the Hungarian digital law firm SimpLEGAL, InvestCEE LegalTech Consultancy issued AI in Legal Services A Practical Guide in December 2021, suggesting that AI offers new opportunities for digitalising legal services.

One of the most common ways of using AI in legal practice is to delegate certain tasks, especially where decisions need to be reached on the basis of a large quantity of data and legal practitioners are not capable of providing a swift response.

This kind of delegation can ease the tension between plausible hypotheses and the formal analysis of professional judgements by lawyers, allow the systematic study of issues in order to help legal practitioners make better decisions, and mitigate human limitations in terms of understanding complex data and making well-informed choices between the options available.

In addition to assistance with processing large quantities of data, efficient algorithms have empowered AI to make decisions at a near-instantaneous speed.

AI technologies are able to categorise solutions based on different criteria and priorities, assess the merits of each solution, and recommend a set of selected options for legal practitioners, who are then able to evaluate these solutions more efficiently and in a focused and informed manner.

This evaluation process can be made even more effective as algorithms can be configured to calculate and inform the confidence level of the selected options and assess the merits and disadvantages of each one.

In-house legal departments require more guidance in relation to the basic terminology used in the legal AI domain. When applying AI in a firm, it is also important to understand how this might change the firms risk profile, since AI can also be a disruptive technology, and accountable AI practice needs to be reinforced by regulatory insight to enable its sustainable development.

However, as yet no consensus has been reached on the most appropriate regulatory framework to achieve these goals.

The European Commission is taking a lead in terms of regulating AI globally, proposing a risk-based regulatory framework that involves determining the scale or scope of risks related to a concrete situation and a recognised threat.

This framework is also likely to be useful in unpacking the potential role and challenges of AI in promoting more accountable law firms and legal professionals, considering the benefits that accountable and sustainable AI could bring to law firms to protect their clients, particularly vulnerable ones.

By facilitating the use of AI services, the commissions regulatory framework should help law firms to identify and meet the needs of clients who may have difficulty using legal services, or who may be at risk of acting against their own best interests.

An appropriate regulatory framework to promote sustainable AI by monitoring and mitigating the associated risks in legal practice is a pre-condition for using AI more comprehensively in the legal domain.

Instead of being a free-standing regulatory intervention, I believe that an ideal approach will be to construct a regulatory agenda and a control strategy to be combined with other control strategies across different social, economic and cultural contexts and tasks.

The design of this framework should encourage the participation of stakeholders with different expertise such as computer scientists, representatives from industrial organisations, active shareholders, specialist committees and counsel, and consultants or partners with expert technological skill sets, as well as international agencies.

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Using artificial intelligence in the legal profession - Legal Futures

Throughout history, technology has never advanced as quickly as it is right now. It's becoming harder than ever for investors to track the sheer number of innovative tech companies in the public markets, each offering its own unique vision for the future.

But perhaps no technology is more transformative than artificial intelligence (AI), which is already being deployed to complete highly complex tasks in a fraction of the time that humans can. According to one estimate, up to 70% of companies worldwide will have integrated some form of AI into their businesses by 2030, adding $13 trillion in additional output to the global economy.

There will be no shortage of opportunities in the sector over the next decade, but these two stocks might be a great place to start given they're trading at hefty discounts to their all-time highs amid the broader tech sell-off.

C3ai (AI 5.18%) is a first-of-its-kind enterprise AI company. Its stock is volatile because the company isn't profitable yet, and its revenue growth has underperformed expectations since it was listed on the public markets in December 2020. But that's often part and parcel of breaking ground in a brand-new industry.

C3.ai is a good place to start for investors who want exposure to the artificial intelligence sector because it builds both ready-made and customizable AI applications for 11 different industries. For most of its customers, C3.ai is the one-stop source for their AI needs, and it's possible they wouldn't otherwise have access to the technology.

The oil and gas industry is C3.ai's largest contributor to revenue, making up 54% of its $252 million in fiscal 2022, which ended on April 30. The company's technology helps oil behemoths like Shelltrack thousands of pieces of equipment to predict potentially catastrophic failures, saving time, money, and negative environmental impacts. C3.ai has an entire suite of applications just for the fossil fuel sector, which also helps those companies manage carbon emissions to run cleaner businesses.

But C3.ai has also drawn recognition from the largest tech companies in the world. It has partnerships with both Microsoftand Alphabet'sGoogle to collaboratively develop AI applications to better serve their customers using cloud computing technology.

C3.ai's stock price is down 88% from its all-time high, so it carries inherent risks. The company lost $192 million in fiscal 2022 (which ended April 30), but importantly, it has $959 million in cash, equivalents, and short-term investments on its balance sheet -- which means it can run at that loss rate for the next five years before it needs more money. C3.ai has a high gross profit margin of 81%, so once it achieves scale, it can cut back its operating costs to generate positive earnings. But the key question is how long it will take to get there, if at all. With new businesses in new industries, it's always an unknown.

But C3.ai estimates its AI software opportunity could be worth $596 billion by 2025. Since the company's market value is only $2 billion now, it might be worth a small bet for investors with some risk appetite.

Upstart Holdings (UPST 9.54%) offers a great example of how artificial intelligence is being used to improve decades-old processes. Its AI-powered algorithm is designed to replace Fair Isaac's FICO credit scoring system, which is the traditional means of assessing a potential borrower's creditworthiness. Upstart can analyze as many as 1,600 data points about an applicant to deliver a loan decision almost instantly 74% of the time, a feat that might take human assessors days or even weeks to determine.

Fifty-seven banks and credit unions have signed on to use Upstart's algorithm, and one bank, in particular, has abandoned FICO scores altogether in its favor. This is key because Upstart isn't a lender; it originates loans for its bank partners in exchange for a fee. But the company was forced to deviate from this strategy slightly in the recent first quarter of 2022 amid turbulent credit market conditions. Upstart absorbed $345 million worth of new loans using its own balance sheet, which spooked investors.

This added to the $252 million worth of loans it already held mostly for research and development purposes. Management says the increase is only temporary, and it's important to note the $345 million jump represented just 7% of the total $4.5 billion in originations during the quarter.

It's partly a symptom of Upstart's rapid growth, which is bolstered by its entry into the automotive loan origination space. Since launching its car sales and origination software in 2021 called Upstart Auto Retail, 35 car makers have adopted the platform across 525 dealerships. That's up 224% from 162 dealers in Q1 last year.

Upstart generated $849 million in revenue during 2021, a whopping 264% year-over-year jump. It thinks revenue could top $1.25 billion this year, and while that's a slowdown in growth, consumers are contending with higher interest rates and tougher economic conditions, which could dampen demand for credit.

But the company continues to expand into what it estimates is a $6 trillion addressable opportunity. With its stock price down 90% from its all-time high, it might be a great chance to make a long-term bet on what could be the future of credit assessments.

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2 Artificial Intelligence Growth Stocks to Buy on the Dip - The Motley Fool

To many they are arts next big thing digital images of jellyfish pulsing and blurring in a dark pink sea, or dozens of butterflies fusing together into a single organism.

The Argentine artist Sofia Crespo, who created the works with the help of artificial intelligence, is part of the generative art movement, where humans create rules for computers which then use algorithms to generate new forms, ideas and patterns.

The field has begun to attract huge interest among art collectors and even bigger price tags at auction.

US artist and programmer Robbie Barrat a prodigy still only 22 years old sold a work called Nude Portrait#7Frame#64 at Sothebys in March for 630,000 ($821,000).

That came almost four years after French collective Obvious sold a work at Christies titled Edmond de Belamy largely based on Barrats code for $432,500.

Collector Jason Bailey told AFP that generative art was like a ballet between humans and machines.But the nascent scene could already be on the verge of a major shake-up, as tech companies begin to release AI tools that can whip up photo-realistic images in seconds.

Artists in Germany and the United States blazed a trail in computer-generated art during the 1960s.

The V&A museum in London keeps a collection going back more than half a century, one of the key works being a 1968 piece by German artist Georg Nees called Plastik 1.

Nees used a random number generator to create a geometric design for his sculpture.

Nowadays, digital artists work with supercomputers and systems known as Generative Adversarial Networks (GANs) to create images far more complex than anything Nees could have dreamed of.

GANs are sets of competing AIs - one generates an image from the instructions it is given, the other acts as a gatekeeper, judging whether the output is accurate.

If it finds fault, it sends the image back for tweaks and the first AI gets back to work for a second try to beat the gamekeeper.But artists like Crespo and Barrat insist that the artist is still central to the process, even if their working methods are not traditional.

When Im working this way, Im not creating an image. Im creating a system that can create images, Barrat told AFP.

Crespo said she thought her AI machine would be a true collaborator, but in reality it is incredibly tough to get even a single line of code to generate satisfactory results.

She said it was more like babysitting the machine. Tech companies are now hoping to bring a slice of this rarefied action to regular consumers.

Google and Open AI are both touting the merits of new tools they say bring photorealism and creativity without the need for coding skills.

They have replaced GANs with more user-friendly AI models called transformers that are adept at converting everyday speech into images.

Google Imagens webpage is filled with absurdist images generated by instructions such as: A small cactus wearing a straw hat and neon sunglasses in the Sahara desert.

Open AI boasts that its Dalle-2 tool can offer any scenario in any artistic style from the Flemish masters to Andy Warhol.

Although the arrival of AI has led to fears of humans being replaced by machines in fields from customer care to journalism, artists see the developments more as an opportunity than a threat.

Crespo has tried out Dalle-2 and said it was a new level in terms of image generation in general though she prefers her GANs. I very often dont need a model that is very accurate to generate my work, as I like very much when things look indeterminate and not easily recognizable, she said.

Camille Lenglois of Pariss Pompidou Centre Europes largest collection of contemporary art also played down any idea that artists were about to be replaced by machines.

She told AFP that machines did not yet have the critical and innovative capacity, adding: The ability to generate realistic images does not make one an artist.

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Is Artificial Intelligence the future of art? : - The Tico Times