Algorithms play a huge role in the content people watch. Netflix, for example, has said that approximately 80% of subscribers trust the platforms recommendations. But as artificial intelligence technology advances, it may play an increasingly important role in what films and TV shows are made.

Bloomberg columnist Trung Phan recently wrote about AIs potential in evaluating film and television projects commercial viability. The following is an edited transcript of Phans conversation with Marketplace host Kai Ryssdal about what he learned by having his own script analyzed.

Kai Ryssdal: Tell me about this screenplay you wrote called The Lose.

Trung Phan: OK. So, I write now publicly on the internet quite a bit, including a column at Bloomberg. But 10 years ago, I was living in Ho Chi Minh City [Vietnam], and I had dreams of being a screenwriter. And I managed to put a comedy script together and sold it to Fox. The log line for that film was The Fugitive meets Harold and Kumar, set in Southeast Asia. (Laughter) It was just ahead of its time they werent ready to make it. TL;DR it didnt get made, and now were about a decade later.

Ryssdal: All right. So how did it come to pass that you wound up writing a column about it?

Phan: So, separately, I wrote about the story of writing and selling the script ages ago. And the CEO of an artificial intelligence company called Corto AI happened to read my newsletter, and hes like, Hey, Trung, I read this article that you wrote about this old script of yours. And it just so happens that my company has technology that uses artificial intelligence to scan screenplays. And the way they describe it is they look for the narrative DNA of a screenplay and they can basically tell you why a film could or cannot succeed. I already knew that my film couldnt, but I wanted to find out.

Ryssdal: Yeah, being a glutton for punishment. So they run your script through the algorithm. How do they know what they are looking for?

Phan: So the CEO told me that they have a database of about 700,000 scripts. And I guess in the AI industry, in machine learning, a lot of what you do is you kind of tag certain items. So, as an example, in script writing you can usually tell when a screenplay transitions from the first to the second to the third act. So basically, they have this giant catalog, and they wanted to run my script against their giant catalog.

Ryssdal: So they come back with some report, and they tell you what?

Phan: So the report comes back. Im not going to bury the lede here. They said, Your film is not commercially viable. So they come back with something I already knew. Having said that, though, they correctly identified kind of the genre of the film. One of the top comparisons that they pulled up was The Hangover [Part] II, which was set in Bangkok. But they said two things specifically about my script that made it not supermarketable. They have these two scores that they calculate. One is called interestingness, and another is called uniqueness. So what interestingness does is it looks at the range of characters in a script. Obviously, most films will have the protagonist and the villain. But then other films really good films will have a lot of good secondary and tertiary characters. Apparently, my script didnt have those. Whereas, if you take a movie like The Godfather, you probably have half a dozen or 10 characters that you see how they progress. But to my credit, thats a three-hour movie, and Im not Francis Ford Coppola.

Ryssdal: And its worth pointing out here, actually and this is my favorite part of it that they also recommended that if this film did get made, if the studio put Chris Pratt in it, it might work.

Phan: Yeah, it basically chose Chris Pratt as the silver bullet for this film. Like, we looked through the entire 700,000 film and TV [show] database and, based on the interestingness and the lack of uniqueness, the other score, your only chance now is you know, you gotta get Chris Pratt in the drivers seat or this things done.

Ryssdal: If Chris Pratt is a Marketplace listener, maybe we can hook you up. But wait a minute. You mentioned The Godfather, right? And so heres what I want to go bigger picture on Hollywood and AI: The Godfather almost didnt get made like 10 different times, right? Oh, and so now were trying to put AI into this unbelievably subjective what makes a good movie. And I just wonder what you think of that. Set aside your own bad experiences with your film, but come on, man, how do they know?

Phan: I am 100% on the same page with you on this. The one thing I will say is that AI is advancing just so incredibly fast, Im hedging a little bit, but will I be completely out of a job as a writer in 10 years? Maybe, so theres that.

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Get Chris Pratt in the drivers seat or this things done - Marketplace

Rounding off the event was an insightful Q&A session where the audience was invited to interrogate our presenters. The below is a summary of the discussions.

How does AI apply in highly regulated areas?If the use case exists to use data, you should use it even if the platform/system is highly regulated.

There is much hype about the applications for AI, and rightly a lot of excitement for what the future holds. However, just because we want AI to be a success, doesn't mean we should be afraid to say "No". At this point in time, we need to take each AI use case proposal and approach it with the right mindset what is the outcome you're looking to achieve?

Are we in an arms race with China/Russia?Technology doesnt always give you the advantage. War is very complex and different countries approach it with a different mindset. For example, in the West the primary unit is focused on the individual. Whereas in the East, the primary unit is the State which is why they will sacrifice vast numbers of people to protect the mother country.

In many ways it comes down to ethics. In the UK we have whats referred to as The Daily Mail Test essentially if something goes wrong will it be splashed across tomorrows headlines? Its the media and the general public that set the ethical bar, and then the Governments aversity to collateral damage that will determine how AI is accepted in defence. And ultimately, AI will always be judged to a higher standard than humans are its part of the challenge our sector faces.

How far is fiction in the development of science?Todays AI isn't advanced enough to allow us to delegate too much responsibility to the technology. The applications are too narrow, which makes the technologies prone to adversarial attacks where bad actors attempt to 'trick' the AI, for example painting a tank to look like a tree so it's not detected. Additionally, poisoning attacks, where data is injected with vulnerabilities, can affect the accuracy of the outcome.

Follow a design-led process, and you build resilience into the development so you reach a point where you know the output can be trusted, because you have implemented specific controls along the way.

What education do we need around AI?One of the most dangerous ideas we need to get under control is the perception that AI technology is like magic and can solve any problem. We even hear politicians make statements like blockchain can solve Brexits border issues. And it comes back to the problem highlighted at the beginning of the lecture:

What is AI?Thankfully the industry does have influence on what the politicians do with AI through groups, like the BCS AI community interest group and AI expert groups in Brussels. If we can help the lawmakers to get the fundamentals right, well get everyone speaking the same language and more progress can be made.

Who has the capacity and skill to deliver AI in defence? And do we have the right people?BCS has run its AI community interest group for 40+ years, but we cant ignore the war on talent. New graduates emerging from university are in such high demand they can name their price, which is typically far more than the defence sector can afford.

Therefore, we need to be creative in how we approach future talent. If you can find people with more generic IT skills like testing, infrastructure, systems architecture, and design-led principles you can train them to understand how the military works, and train them in how to understand AI so they can speak the language with confidence. Rather than look to hire in specific skills, which are then difficult to retain, we need to invest in creating the right environment to support and upskill people. Find out more about BCS AI Certifications.

BCS offers a range of certifications in AI to help your teams level up. As well as aligning to SFIAplus, a globally recognised skills framework, you join a global community of 60,000 members who are committed to advancing our industry. As a member of BCS you can also join specialist groups and branches, gain access to mentoring, and we provide everything you need to facilitate continued professional development.Become a member of BCS

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Developing artificial intelligence to support robotic autonomous systems in the battlespace | BCS - BCS

Capitol Music Records has severed ties with an artificial-intelligence-powered rapper days after the release of his first single amid intense backlash accusing the artist of perpetuating racist stereotypes.

Artist FN Meka became the worlds first augmented-reality music artist to be signed to a major record label earlier this month, releasing his first single Florida Water on August 12. The single featured Fortnite gamer Clix and Atlanta rapper Gunna.

Meka already has over 500,000 monthly listeners on Spotify and over 10 million followers on TikTok, where his posts allow fans a peek into his virtual world, which includes huge Bugatti jets, Maybach helicopters, and a machine that turns ice into iced-out watches.

However, backlash quickly rose up on social media with users pointing out their discomfort with how Meka is portrayed, claiming the creation was equivalent to digital blackface and that his content on Instagram and TikTok trivialized incarceration and police brutality.

One Instagram post showed the rapper being beaten by a police officer in a jail cell because he wont snitch.

On Tuesday, activist nonprofit Industry Blackout wrote an open letter to Capitol summarizing the issues brought to light.

It is a direct insult to the Black community and our culture. An amalgamation of gross stereotypes, appropriative mannerisms that derive from Black artists, complete with slurs infused in lyrics, the statement said. We find fault in the lack of awareness in how offensive this caricature is.

Industry Blackout called for Capitol to cut ties with the artist and donate any associated funds to charity or other black artists under the label.

Capitol quickly responded in a statement shared online by New York Times journalist Joe Costarelli, confirming that it had dropped the rapper with immediate effect.

Mekas debut single Florida Water has also been removed from all streaming platforms.

We offer our deepest apologies to the Black community for our insensitivity in signing this project without asking enough questions about equity and the creative process behind it. We thank those who have reached out to us with constructive feedback in the past couple of daysyour input was invaluable as we came to the decision to end our association with the project, the statement read.

Meka is partially backed by A.I. and was cocreated by Anthony Martini and Brandon Le of the company Factory New. While the voice is based on a real human, the rest is all down to artificial intelligence.

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Capitol Records forced to drop its artificial-intelligence-created rapper after just one week following gross stereotypes backlash - Fortune

NEW YORK--(BUSINESS WIRE)--Qloo, the leading artificial intelligence platform for culture and taste preferences, announced today that it has raised $15M in Series B funding from Eldridge and AXA Venture Partners. This latest round of funding brings Qloos total capital raised to $30M, and will enable the privacy-centric AI leader to expand its team of world-class data scientists, enrich its technology, and build on its sales channels in order to continue to offer premier insights into global consumer taste for Fortune 500 companies across the globe.

Founded in 2012, Qloo pioneered the predictive algorithm as a service model, using AI technology to help brands securely analyze anonymized and encrypted consumer taste data to provide recommendations based on a consumers preferences. Demand for Qloo has been accelerating as companies look for privacy centric solutions - in fact, API request volumes across endpoints grew more than 273% year-over-year in Q2.

Before Qloo, consumer taste was really only examined within the silo of a certain app or service - which made it impossible to model a fuller picture of peoples preferences, said Alex Elias, Founder and CEO of Qloo. Qloo is the first AI platform that takes into account all the cross-sections of our preferences - like how our music tastes correlate to our favorite restaurants, or how our favorite clothing brands may lend themselves to a great movie recommendation.

Qloos flagship API works across multiple layers to process and correlate over 575 million primary entities (such as a movie, book, restaurant, song, etc.) across entertainment, culture, and consumer products, giving the most accurate and expansive predictions of consumer taste based on demographics, preferences, cultural entities, metadata, and geolocational factors. Qloos API can be plugged directly into leading data platforms such as Snowflake and Tableau, with results populated in only a matter of seconds making it easy for companies to improve product development, media buying, and consumer experiences in real time.

Qloo currently delivers cultural AI that powers inferences for clients serving over 550 million customers globally in 2022, including industry leaders across media and publishing, entertainment, technology, e-commerce, consumer brands, travel, hospitality, automakers, fashion, financial services, and more.

About Qloo:

Qloo is the leading artificial intelligence platform on culture and taste preferences, providing completely anonymized and encrypted consumer taste data and recommendations for leading companies in the tech, entertainment, publishing, retail, travel, hospitality and CPG sectors. Qloos proprietary API can predict consumers' preferences and connect how their tastes correlate across over a dozen major categories, including music, film, television, podcasts, dining, nightlife, fashion, consumer products, books and travel. Launched in 2012, Qloo combines the latest in machine learning, theoretical research in Neuroaesthetics and one of the largest pipelines of detailed taste data to better inform its customers - and makes all of this intelligence available through an API. By allowing companies to speak more effectively with their target consumers, Qloo helps its customers solve real-world problems such as driving sales, saving money on media buys, choosing locations and building brands. Qloo is the parent company of TasteDive, a cultural recommendation engine and social community that allows users to discover what to watch, read, listen to, and play based on their existing unique preferences.

Learn more at qloo.com and http://www.tastedive.com.

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Qloo, the Leading Artificial Intelligence Platform for Culture and Taste Preferences, Raises $15M in Series B - Business Wire

Kyle Wiggers is a senior reporter at TechCrunch with a special interest in artificial intelligence. His writing has appeared in VentureBeat and Digital Trends, as well as a range of gadget blogs including Android Police, Android Authority, Droid-Life, and XDA-Developers. He lives in Brooklyn with his partner, a piano educator, and dabbles in piano himself. occasionally -- if mostly unsuccessfully.

Research in the field of machine learning and AI, now a key technology in practically every industry and company, is far too voluminous for anyone to read it all. This column,Perceptron, aims to collect some of the most relevant recent discoveries and papers particularly in, but not limited to, artificial intelligence and explain why they matter.

An earable that uses sonar to read facial expressions was among the projects that caught our eyes over these past few weeks. So did ProcTHOR, a framework from the Allen Institute for AI (AI2) that procedurally generates environments that can be used to train real-world robots. Among the other highlights, Meta created an AI system that can predict a proteins structure given a single amino acid sequence. And researchers at MIT developed new hardware that they claim offers faster computation for AI with less energy.

The earable, which was developed by a team at Cornell, looks something like a pair of bulky headphones. Speakers send acoustic signals to the side of a wearers face, while a microphone picks up the barely-detectable echoes created by the nose, lips, eyes, and other facial features. These echo profiles enable the earable to capture movements like eyebrows raising and eyes darting, which an AI algorithm translates into complete facial expressions.

Image Credits: Cornell

The earable has a few limitations. It only lasts three hours on battery and has to offload processing to a smartphone, and the echo-translating AI algorithm must train on 32 minutes of facial data before it can begin recognizing expressions. But the researchers make the case that its a much sleeker experience than the recorders traditionally used in animations for movies, TV, and video games. For example, for the mystery game L.A. Noire, Rockstar Games built a rig with 32 cameras trained on each actors face.

Perhaps someday, Cornells earable will be used to create animations for humanoid robots. But those robots will have to learn how to navigate a room first. Fortunately, AI2s ProcTHOR takes a step (no pun intended) in this direction, creating thousands of custom scenes including classrooms, libraries, and offices in which simulated robots must complete tasks, like picking up objects and moving around furniture.

The idea behind the scenes, which have simulated lighting and contain a subset of a massive array of surface materials (e.g., wood, tile, etc.) and household objects, is to expose the simulated robots to as much variety as possible. Its a well-established theory in AI that performance in simulated environments can improve the performance of real-world systems; autonomous car companies like Alphabets Waymo simulate entire neighborhoods to fine-tune how their real-world cars behave.

Image Credits: Allen Institute for Artificial Intelligence

As for ProcTHOR, AI2 claims in a paper that scaling the number of training environments consistently improves performance. That bodes well for robots bound for homes, workplaces, and elsewhere.

Of course, training these types of systems requires a lot of compute power. But that might not be the case forever. Researchers at MIT say theyve created an analog processor that can be used to create superfast networks of neurons and synapses, which in turn can be used to perform tasks like recognizing images, translating languages, and more.

The researchers processor uses protonic programmable resistors arranged in an array to learn skills. Increasing and decreasing the electrical conductance of the resistors mimics the strengthening and weakening of synapses between neurons in the brain, a part of the learning process.

The conductance is controlled by an electrolyte that governs the movement of protons. When more protons are pushed into a channel in the resistor, the conductance increases. When protons are removed, the conductance decreases.

Processor on a computer circuit board

An inorganic material, phosphosilicate glass, makes the MIT teams processor extremely fast because it contains nanometer-sized pores whose surfaces provide the perfect paths for protein diffusion. As an added benefit, the glass can run at room temperature, and it isnt damaged by the proteins as they move along the pores.

Once you have an analog processor, you will no longer be training networks everyone else is working on, lead author and MIT postdoc Murat Onen was quoted as saying in a press release. You will be training networks with unprecedented complexities that no one else can afford to, and therefore vastly outperform them all. In other words, this is not a faster car, this is a spacecraft.

Speaking of acceleration, machine learning is now being put to use managing particle accelerators, at least in experimental form. At Lawrence Berkeley National Lab two teams have shown that ML-based simulation of the full machine and beam gives them a highly precise prediction as much as 10 times better than ordinary statistical analysis.

Image Credits: Thor Swift/Berkeley Lab

If you can predict the beam properties with an accuracy that surpasses their fluctuations, you can then use the prediction to increase the performance of the accelerator, said the labs Daniele Filippetto. Its no small feat to simulate all the physics and equipment involved, but surprisingly the various teams early efforts to do so yielded promising results.

And over at Oak Ridge National Lab an AI-powered platform is letting them do Hyperspectral Computed Tomography using neutron scattering, finding optimal maybe we should just let them explain.

In the medical world, theres a new application of machine learning-based image analysis in the field of neurology, where researchers at University College London have trained a model to detect early signs of epilepsy-causing brain lesions.

MRIs of brains used to train the UCL algorithm.

One frequent cause of drug-resistant epilepsy is what is known as a focal cortical dysplasia, a region of the brain that has developed abnormally but for whatever reason doesnt appear obviously abnormal in MRI. Detecting it early can be extremely helpful, so the UCL team trained an MRI inspection model called Multicentre Epilepsy Lesion Detection on thousands of examples of healthy and FCD-affected brain regions.

The model was able to detect two thirds of the FCDs it was shown, which is actually quite good as the signs are very subtle. In fact, it found 178 cases where doctors were unable to locate an FCD but it could. Naturally the final say goes to the specialists, but a computer hinting that something might be wrong can sometimes be all it takes to look closer and get a confident diagnosis.

We put an emphasis on creating an AI algorithm that was interpretable and could help doctors make decisions. Showing doctors how the MELD algorithm made its predictions was an essential part of that process, said UCLs Mathilde Ripart.

Original post:
Perceptron: Face-tracking earables, analog AI chips, and accelerating particle accelerators - TechCrunch

Companies come in promising the world and often dont deliver, said Bob Wachter, head of the department of medicine at the University of California, San Francisco. When I look for examples of true AI and machine learning thats really making a difference, theyre pretty few and far between. Its pretty underwhelming.

Administrators say algorithms the software that processes data from outside companies dont always work as advertised because each health system has its own technological framework. So hospitals are building out engineering teams and developing artificial intelligence and other technology tailored to their own needs.

But its slow going. Research based on job postings shows health care behind every industry except construction in adopting AI.

The Food and Drug Administration has taken steps to develop a model for evaluating AI, but it is still in its early days. There are questions about how regulators can monitor algorithms as they evolve and rein in the technologys detrimental aspects, such as bias that threaten to exacerbate health care inequities.

Sometimes theres an assumption that AI is working, and its just a matter of adopting it, which is not necessarily true, said Florenta Teodoridis, a professor at the University of Southern Californias business school whose research focuses on AI. She added that being unable to understand why an algorithm came to a certain result is fine for things like predicting the weather. But in health care, its impact is potentially life-changing.

Despite the obstacles, the tech industry is still enthusiastic about AIs potential to transform health care.

The transition is slightly slower than I hoped but well on track for AI to be better than most radiologists at interpreting many different types of medical images by 2026, Hinton told POLITICO via email. He said he never suggested that we should get rid of radiologists, but that we should let AI read scans for them.

If hes right, artificial intelligence will start taking on more of the rote tasks in medicine, giving doctors more time to spend with patients to reach the right diagnosis or develop a comprehensive treatment plan.

I see us moving as a medical community to a better understanding of what it can and cannot do, said Lara Jehi, chief research information officer for the Cleveland Clinic. It is not going to replace radiologists, and it shouldnt replace radiologists.

Radiology is one of the most promising use cases for AI. The Mayo Clinic has a clinical trial evaluating an algorithm that aims to reduce the hours-long process oncologists and physicists undertake to map out a surgical plan for removing complicated head and neck tumors.

An algorithm can do the job in an hour, said John D. Halamka, president of Mayo Clinic Platform: Weve taken 80 percent of the human effort out of it. The technology gives doctors a blueprint they can review and tweak without having to do the basic physics themselves, he said.

NYU Langone Health has also experimented with using AI in radiology. The health system has collaborated with Facebooks Artificial Intelligence Research group to reduce the time it takes to get an MRI from one hour to 15 minutes. Daniel Sodickson, a radiological imaging expert at NYU Langone who worked on the research, sees opportunity in AIs ability to downsize the amount of data doctors need to review.

When I look for examples of true AI and machine learning thats really making a difference, theyre pretty few and far between. Its pretty underwhelming.

Bob Wachter, head of the department of medicine at the University of California, San Francisco

Covid has accelerated AIs development. Throughout the pandemic, health providers and researchers shared data on the disease and anonymized patient data to crowdsource treatments.

Microsoft and Adaptive Biotechnologies, which partner on machine learning to better understand the immune system, put their technology to work on patient data to see how the virus affected the immune system.

The amount of knowledge thats been obtained and the amount of progress has just been really exciting, said Peter Lee, corporate vice president of research and incubations at Microsoft.

There are other success stories. For example, Ochsner Health in Louisiana built an AI model for detecting early signs of sepsis, a life-threatening response to infection. To convince nurses to adopt it, the health system created a response team to monitor the technology for alerts and take action when needed.

Im calling it our care traffic control, said Denise Basow, chief digital officer at Ochsner Health. Since implementation, she said, death from sepsis is declining.

The biggest barrier to the use of artificial intelligence in health care has to do with infrastructure.

Health systems need to enable algorithms to access patient data. Over the last several years, large, well-funded systems have invested in moving their data into the cloud, creating vast data lakes ready to be consumed by artificial intelligence. But thats not as easy for smaller players.

Another problem is that every health system is unique in its technology and the way it treats patients. That means an algorithm may not work as well everywhere.

Over the last year, an independent study on a widely used sepsis detection algorithm from EHR giant Epic showed poor results in real-world settings, suggesting where and how hospitals used the AI mattered.

This quandary has led top health systems to build out their own engineering teams and develop AI in-house.

That could create complications down the road. Unless health systems sell their technology, its unlikely to undergo the type of vetting that commercial software would. That could allow flaws to go unfixed for longer than they might otherwise. Its not just that the health systems are implementing AI while no ones looking. Its also that the stakeholders in artificial intelligence, in health care, technology and government, havent agreed upon standards.

A lack of quality data which gives algorithms material to work with is another significant barrier in rolling out the technology in health care settings.

Over the last several years, large, well-funded systems have invested in moving their data into the cloud, creating vast data lakes ready to be consumed by artificial intelligence.|Elaine Thompson/AP Photo

Much data comes from electronic health records but is often siloed among health care systems, making it more difficult to gather sizable data sets. For example, a hospital may have complete data on one visit, but the rest of a patients medical history is kept elsewhere, making it harder to draw inferences about how to proceed in caring for the patient.

We have pieces and parts, but not the whole, said Aneesh Chopra, who served as the governments chief technology officer under former President Barack Obama and is now president of data company CareJourney.

While some health systems have invested in pulling data from a variety of sources into a single repository, not all hospitals have the resources to do that.

Health care also has strong privacy protections that limit the amount and type of data tech companies can collect, leaving the sector behind others in terms of algorithmic horsepower.

Importantly, not enough strong data on health outcomes is available, making it more difficult for providers to use AI to improve how they treat patients.

That may be changing. A recent series of studies on a sepsis algorithm included copious details on how to use the technology in practice and documented physician adoption rates. Experts have hailed the studies as a good template for how future AI studies should be conducted.

But working with health care data is also more difficult than in other sectors because it is highly individualized.

We found that even internally across our different locations and sites, these models dont have a uniform performance, said Jehi of the Cleveland Clinic.

And the stakes are high if things go wrong. The number of paths that patients can take are very different than the number of paths that I can take when Im on Amazon trying to order a product, Wachter said.

Health experts also worry that algorithms could amplify bias and health care disparities.

For example, a 2019 study found that a hospital algorithm more often pushed white patients toward programs aiming to provide better care than Black patients, even while controlling for the level of sickness.

Last year, the FDA published a set of guidelines for using AI as a medical device, calling for the establishment of good machine learning practices, oversight of how algorithms behave in real-world scenarios and development of research methods for rooting out bias.

The agency subsequently published more specific guidelines on machine learning in radiological devices, requiring companies to outline how the technology is supposed to perform and provide evidence that it works as intended. The FDA has cleared more than 300 AI-enabled devices, largely in radiology, since 1997.

Regulating algorithms is a challenge, particularly given how quickly the technology advances. The FDA is attempting to head that off by requiring companies to institute real-time monitoring and submit plans on future changes.

But in-house AI isnt subject to FDA oversight. Bakul Patel, former head of the FDAs Center for Devices and Radiological Health and now Googles senior director for global digital health strategy and regulatory affairs, said that the FDA is thinking about how it might regulate noncommercial artificial intelligence inside of health systems, but he adds, theres no easy answer.

FDA has to thread the needle between taking enough action to mitigate flaws in algorithms while also not stifling AIs potential, he said.

Some argue that public-private standards for AI would help advance the technology. Groups, including the Coalition for Health AI, whose members include major health systems and universities as well as Google and Microsoft, are working on this approach.

But the standards they envision would be voluntary, which could blunt their impact if not widely adopted.

Link:
Artificial intelligence was supposed to transform health care. It hasn't. - POLITICO

Today, AI learning requires a connection to a remote server to perform heavy computing calculations. These researchers say changing that could transform health care.

ORLANDO, Fla. Our computers, devices, smart watches, video monitoring systems, etc...- we rely on connectivity to the internet and dont think twice about it. Now, scientists are developing technology for artificial intelligence that will allow it to work even in remote areas.

Self-driving cars, drone helicopters and medical monitoring equipment; its all cutting-edge technology that requires connection to the cloud. Now, researchers at the University of Central Florida are developing devices that wont rely on an internet connection.

What we are trying to do is make small devices, which will mimic the neurons and synapses of the brain, researcher at the University of Central Florida, Tania Roy, PhD, explains.

Right now, artificial intelligence learning requires a connection to a remote server to perform heavy computing calculations. Scientists are making the AI circuits microscopically small.

Roy emphasizes, Each device that we have is the size of 1/100th of a human hair.

The AI can fit on a small microchip less than an inch wide eliminating the need for an internet connection, meaning life-saving devices could work in remote areas. For example, helping emergency responders find missing hikers.

We would send a drone which has a camera eye, and it can just go and locate those people and rescue them, Roy says.

The scientists say with no need for an internet connection, the AI would also work in space, where no AI technology has gone before.

The same UCF team is expanding on their work with artificial brain devices, and they are developing artificial intelligence that mimics the retina in the human eye, meaning someday, AI could instantly recognize the images in front of it. The researchers say this technology is about five years away from commercial use.

If this story has impacted your life or prompted you or someone you know to seek or change treatments, please let us know by contacting Shelby Kluver at shelby.kluver@wqad.com or Marjorie Bekaert Thomas at mthomas@ivanhoe.com.

Watch more 'Your Health' segments on News 8's YouTube channel

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Is the future of artificial intelligence internet-free? These researchers hope so - WQAD Moline

QuantumBlack, AI by McKinsey recently sat down with Selim Turki, head of data and AI at Uber-owned mobility company Careem, to discuss the latest trends in advanced analytics and artificial intelligence. Far from a dry discussion of theory, the conversation coalesced around several fascinating use cases in which Careem is using AI to make a difference in peoples lives. We discussed how AI is being leveraged to improve customer and driver security through targeted facial-recognition checks to ensure drivers (captains) are who they say they are. We also discussed how AI is being used to provide customers with the most accurate and up-to-date estimated times of arrival (ETAs) by factoring in a host of conditions, including local weather conditions, prayer times, and even iftar times during Ramadan. Along the way, we discussed what it means to be an AI first company and the outlook for AI techand talentin the region.

QuantumBlack: Was AI always an important part of Careems growth journey? How has AIs role evolved since Careems inception?

Selim Turki: We started our journey as a ride-hailing company booking journeys for corporate clients. We were initially booking cars manually, without a data server, before introducing more advanced systems to deliver more efficient, personalized experiences. Since day one, our mission has been to simplify and improve the lives of peopleparticularly our customers and captains. We quickly understood that maintaining high reliability for our dynamic marketplace 24/7 was a complex process that needed to be driven by instant decision making through continuous automation at scale.

We began processing real-time data, using algorithms and machine learning [ML] models to solve some of the core issues for our ride-hailing marketplace, including matching customers and captains efficiently, shaping our demand and supply via surge pricing, calculating accurate ETAs for our captains, and improving our maps and location search functionality.

Today, we are scaling the Middle East, North Africa, and Pakistan [MENAP] regions first super app. AI is in our DNA as we invest more in platform capabilities and team skill sets. Our hiring strategy is focused on growing a diverse team of data and machine learning scientists to build out our in-house experimentation and machine learning platforms.

QuantumBlack: Has the adoption of these new AI techniques changed the way Careem works to serve its customers? How has this affected business teams within the organization?

Selim Turki: We use several AI techniques depending on the type of service we offer in our super app. All of these techniques are directed at three particular needs:

We use AI to factor in prayer times, iftar time during Ramadan, and weather conditions to better predict the ETA accuracy of when the food will be delivered to our customers.

QuantumBlack: How many AI practitioners work with Careem today?

Selim Turki: We have dozens of AI and machine learning experts who are driving forward our strategy of being an AI-first company. Part of our plan is to educate the entire organization on the topic, inviting our engineers and business counterparts to use AI to solve some of their challenges. We have also designed a program dedicated to new college graduates to ensure future talent is up to date with the latest AI techniques and to encourage them to further develop their skills.

QuantumBlack: How do you integrate AI into your decision making now? How do you stay ahead of competition in the market?

Selim Turki: AI is part of Careems decision-making framework. We set quarterly goals to measure and assess the usage and impact of our ML models on the different business streams.

We use rigorous statistical methodologies, taking confounding effects into account, to accurately estimate the models impact on different areas of the business.

To help our data and AI teams stay on top of the changes happening in the industry, we have started collaborating with regional academic institutions to solve some of the most significant super-app challenges and to identify exciting new opportunities for AI innovation.

We publish our progress on the Careem engineering blog and invite third parties to collaborate with us on specific areas related to AI.

We also contribute to open-source data communities and offer our work to other AI and ML professionals.

QuantumBlack: Can you share a recent instance of how AI fundamentally changed the way Careem does business with its customers or captains?

Selim Turki: With any digital platform, fraudsters will look for loopholes to exploit, whether through creating fake-identity accounts or exploring ways to hijack open accounts. Our team uses advanced AI techniques focusing on the identity of users to detect and prevent losses stemming from fraud. One system we use, called Crazy Wall, uses a relational graph convolutional network to map different data points of a customers identity. It also identifies characteristics shared across different identities to detect and mass-block fraudulent patterns across customer or captain activities.

QuantumBlack: AI talent has been a key challenge for companies in the region. How have you dealt with the regions structural talent issues?

Selim Turki: The regions tech talent is growing rapidly, and its exciting to see more specialists choosing to come to the region to make an impact in some of the fastest-growing countries in the world. Its also exciting to see a growing number of local university graduates specializing in AI. Were fortunate to have attracted a strong community of AI talent both locally and from surrounding markets to Careem. Our teams are building tech across various areas, including e-commerce, technology-enabled logistics, maps, identity, and fintech. They can solve complex and meaningful challenges at scale thanks to Careems deep tech expertise, strong regulatory relationships, local presence, and increasingly specialized global teams that are structured to operate as autonomous start-ups. Our team of more than 400 engineers and developers are empowered to develop cutting-edge technology every day. Being a remote-first company allows us to attract talent from across the world who want to have an impact on the MENAP region. This means that the opportunities to gain new perspectives and solve complex, real-world challenges alongside talented peers are endless.

QuantumBlack: Do you think the talent-supply challenges are here to stay? What is your ambition for attracting cutting-edge AI practitioners to Careem in the next three to five years?

Selim Turki: As AI becomes more widely used across industries, the demand for specialists will continue to rise. We need to inspire the next generation of data and AI specialists to be curious and gain exposure to the workplace at an earlier age.

At Careem, we are focused on building an AI culture where opportunities to learn and thrive are fostered by adapting, mentoring, and sharing within our AI communities and beyond. We are also hoping to make AI more accessible to stakeholders across Careem with initiatives like no-code AI, where AI is accessible without existing coding skill sets, as well as partnerships with AI labs to democratize AI usage across the company.

QuantumBlack: How will AI specifically change the mobility space in MENAP? Are there any white spaces where MENAP companies could be global first movers?

Selim Turki: The global mobility space is at a very nascent phase, with considerable opportunities to solve using AI techniques. At Careem, we have the vision of creating an internet-like network to transport packages of atoms, like how the internet transports packets of bits, called the AtomNet.

The AtomNet provides an open-network platform that connects, manages, and routes multimode autonomous vehicles [AVs] to make transport ubiquitous. Similar to how packets can travel across multiple modalities of transport (Wi-Fi, DSL, cable, and fiber), packages on the AtomNet can travel in autonomous motorcycles, cars, vans, trucks, ships, drones, and airplanes. We foresee an AtomNet industry ecosystem with open package headers and protocols to allow package switching and efficient package mobility. With open protocols, coordination costs will drop significantly, and local, national, and international transport gaps will narrow over the years.

AtomNet will support Careems quick commerce, fulfillment centers, restaurants, groceries, dark stores, transportation, and cross-border commerce. We see the epicenter of AtomNet starting in the UAE due to its progressive regulation and culture of innovation.

QuantumBlack: AI is still in its nascency in the broader context of this region. How do you think this will change in the next five to ten years?

Selim Turki: A long and exciting journey is ahead of us in the wider Middle East. With the growing pace of technology, more and more regional corporations will use AI to enhance their products and offer a better experience to customers.

At Careem, our primary focus will continue to be building the internet platform of the Middle East to provide access to our servicesusing data and AI as a core to simplify and improve customers lives. The meta goal is to delight all our users and personalize their experience through data and AI in every service offered through our super app.

The current trend of making trade-offs by improving AI prediction will be strengthened at the cost of short-term factors such as ingestion costs, customer experience, and operational excellence. We will continue investing in our data streams to help our models learn, build, and manage algorithms at scale. Moreover, real-time feedback loops will continue to decipher customer behavior and how it evolves by using our services through leveraging more intelligent software and hardware. Some of the emerging machine learning models will be tailored more to our region, considering language, customer behavior, and product relevance.

Our goal is to provide the simplest and best possible customer experience. To make things simple, you have to make them intuitive. To make things intuitively simple, we need to:

Read this article:
Artificial intelligence and the super app - McKinsey

DUBLIN, Aug. 18, 2022 /PRNewswire/ -- The "Global Artificial Intelligence for Drug Discovery and Development Market (2022-2027) by Offering, Application, End User, Technology, Geography, Competitive Analysis and the Impact of Covid-19 with Ansoff Analysis" report has been added to ResearchAndMarkets.com's offering.

The Global Artificial Intelligence for Drug Discovery and Development Market is estimated to be USD 1.22 Bn in 2022 and is expected to reach USD 4.8 Bn by 2027, growing at a CAGR of 31.54%.

Market dynamics are forces that impact the prices and behaviors of the stakeholders. These forces create pricing signals which result from the changes in the supply and demand curves for a given product or service. Forces of Market Dynamics may be related to macro-economic and micro-economic factors.

There are dynamic market forces other than price, demand, and supply. Human emotions can also drive decisions, influence the market, and create price signals. As the market dynamics impact the supply and demand curves, decision-makers aim to determine the best way to use various financial tools to stem various strategies for speeding the growth and reducing the risks.

Company Profiles

The report provides a detailed analysis of the competitors in the market. It covers the financial performance analysis for the publicly listed companies in the market. The report also offers detailed information on the companies' recent development and competitive scenario. Some of the companies covered in this report are Aria Pharmaceuticals Inc, Atomwise Inc., BenevolentAI, BioSymetrics, Cloud Pharmaceuticals, etc.

Countries Studied

Competitive Quadrant

The report includes Competitive Quadrant, a proprietary tool to analyze and evaluate the position of companies based on their Industry Position score and Market Performance score. The tool uses various factors for categorizing the players into four categories. Some of these factors considered for analysis are financial performance over the last 3 years, growth strategies, innovation score, new product launches, investments, growth in market share, etc.

Ansoff Analysis

The report presents a detailed Ansoff matrix analysis for the Global Artificial Intelligence for Drug Discovery and Development Market. Ansoff Matrix, also known as Product/Market Expansion Grid, is a strategic tool used to design strategies for the growth of the company. The matrix can be used to evaluate approaches in four strategies viz. Market Development, Market Penetration, Product Development and Diversification.

The matrix is also used for risk analysis to understand the risk involved with each approach. The analyst analyses the Global Artificial Intelligence for Drug Discovery and Development Market using the Ansoff Matrix to provide the best approaches a company can take to improve its market position. Based on the SWOT analysis conducted on the industry and industry players, the analyst has devised suitable strategies for market growth.

Why buy this report?

Key Topics Covered:

1 Report Description

2 Research Methodology

3 Executive Summary3.1 Introduction3.2 Market Size, Segmentations and Outlook

4 Market Dynamics4.1 Drivers4.1.1 Need for Control Drug Discovery Process and Cost Reduction 4.1.2 Increasing Need to Manage the Large Data Generated During Preclinical Studies4.1.3 Increasing Adoption Across Biopharmaceutical Companies4.2 Restraints4.2.1 Unavailability of Skilled Professionals4.3 Opportunities4.3.1 AI Cloud to Create a Streamlined and Automated Approach in Drug Discovery4.3.2 Increasingly Growing R&D Investments4.4 Challenges4.4.1 Limited Availability of Data Sets

5 Market Analysis5.1 Regulatory Scenario5.2 Porter's Five Forces Analysis5.3 Impact of COVID-195.4 Ansoff Matrix Analysis

6 Global Artificial Intelligence for Drug Discovery and Development Market, By Offering6.1 Introduction6.2 Services6.3 Software

7 Global Artificial Intelligence for Drug Discovery and Development Market, By Application7.1 Introduction7.2 Cardiovascular Disease7.3 Immuno-Oncology7.4 Metabolic Diseases7.5 Neurodegenerative Diseases

8 Global Artificial Intelligence for Drug Discovery and Development Market, By End User8.1 Introduction8.2 Contract Research Organizations8.3 Pharmaceutical & Biotechnology Companies8.4 Research Centers and Academic & Government Institutes

9 Global Artificial Intelligence for Drug Discovery and Development Market, By Technology9.1 Introduction9.2 Machine Learning9.2.1 Deep Learning9.2.2 Supervised Learning9.2.3 Reinforcement Learning9.2.4 Unsupervised Learning9.2.5 Other Machine Learning Technologies9.3 Other Technologies

10 Americas' Artificial Intelligence for Drug Discovery and Development Market10.1 Introduction10.2 Argentina10.3 Brazil10.4 Canada10.5 Chile10.6 Colombia10.7 Mexico10.8 Peru10.9 United States10.10 Rest of Americas

11 Europe's Artificial Intelligence for Drug Discovery and Development Market11.1 Introduction11.2 Austria11.3 Belgium11.4 Denmark11.5 Finland11.6 France11.7 Germany11.8 Italy11.9 Netherlands11.10 Norway11.11 Poland11.12 Russia11.13 Spain11.14 Sweden11.15 Switzerland11.16 United Kingdom11.17 Rest of Europe

12 Middle East and Africa's Artificial Intelligence for Drug Discovery and Development Market12.1 Introduction12.2 Egypt12.3 Israel12.4 Qatar12.5 Saudi Arabia12.6 South Africa12.7 United Arab Emirates12.8 Rest of MEA

13 APAC's Artificial Intelligence for Drug Discovery and Development Market13.1 Introduction13.2 Australia13.3 Bangladesh13.4 China13.5 India13.6 Indonesia13.7 Japan13.8 Malaysia13.9 Philippines13.10 Singapore13.11 South Korea13.12 Sri Lanka13.13 Thailand13.14 Taiwan13.15 Rest of Asia-Pacific

14 Competitive Landscape14.1 Competitive Quadrant14.2 Market Share Analysis14.3 Strategic Initiatives14.3.1 M&A and Investments14.3.2 Partnerships and Collaborations14.3.3 Product Developments and Improvements

15 Company Profiles15.1 Aria Pharmaceuticals Inc15.2 Atomwise Inc15.3 BenevolentAI15.4 BioSymetrics15.5 Cloud Pharmaceuticals15.6 Cyclica15.7 Deep Genomics15.8 Envisagenics15.9 Exscientia15.10 IBM15.11 Insitro15.12 Novartis AG15.13 Nvidia15.14 Owkin Inc15.15 Verge Genomics15.16 XtalPi

16 Appendix

For more information about this report visit https://www.researchandmarkets.com/r/awqyve

Media Contact:

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Insights on the Artificial Intelligence for Drug Discovery and Development Global Market to 2027 - AI Cloud to Create a Streamlined and Automated...

Across the country, artificial intelligence is powering machines and computers to help us solve problems and work more efficiently. Its assisting scientists to develop vaccines and treat patients more effectively, securing our nations networks and critical infrastructure against cyberattacks, alerting customers of bank fraud and expanding financial opportunities for underserved communities through access to credit, and much more. AI is rapidly changing how businesses operateand is foundational to a thriving 21st-century economy. By 2030, 70% of businessesglobally expect to use AI. Around the world, AI is estimated to boost global GDP by 14% over the same period, accounting for nearly $16 trillion of economic output.

From basic needs, such as food security and supply chain resiliency, to ensuring our nations competitive advantage through research and development and the intellectual property rights that underpin it, AI will shape the new economic era. Its no wonder that, according to a poll conducted by the U.S. Chamber Technology Engagement Center (C_TEC) 80% of Americans feel its vital for the U.S. to lead the world in AI. The reality before us is as simple as it is stark: whoever leads in the advancement of AI will lead the global economy.

To that end, were seeing allies and strategic competitors pursue AI leadership. Earlier this year, Russia and China announced they would work cooperatively to develop AI. Of course, China is already investing heavily in this space in parallel to engaging in IP theft and cyber espionage to steal American innovation. At the same time, our friends and partners in Europe are looking to write regulations around data and AI, some of which could disadvantage U.S. businesses if not carefully constructed. Nations worldwide are racing ahead and we must not fall behind.

We must get the policy environment right to enable American innovators to lead the AI revolution. With government and industry working together, we will ensure that becomes a reality. We will compete against nations in research and development, create an environment where AI is used responsibly, respect personal liberties, and ensure our workforce is prepared for an AI-driven future. The work of this Commission is a critical next step in the U.S. Chambers leadership on this issue, building on the AI principles we released in 2019.

Recently, the U.S. Chamber Artificial Intelligence Commission on Competition, Inclusion, and Innovation wrapped its final field hearing. The U.S. Chamber formed this Commission in January to better understand how our nation can lead the world in adopting AI technologies and enact sound regulations to harness its potential.

Co-chaired by former Congressman John Delaney and former Congressman Mike Ferguson, the Commission has held public hearings in Austin, Cleveland, Palo Alto, London, and Washington, DC, bringing together thought leaders, researchers, and experts in industry, academia, and civil society. Here is what the Commission found during those public hearings:

As AI grows increasingly ubiquitous in our everyday lives and crucial to our nations economic growth, these issues are inextricably linked. This Fall, we look forward to the Commissions final recommendations to help guide policymakers toward durable, bipartisan AI policy solutions. The U.S.Chamber is committed to ensuring our recommendations produce actions, and those actions produce results.

Executive Vice President, Center for Capital Markets Competitiveness (CCMC), U.S. Chamber of Commerce, Executive Vice President, Center for Technology Engagement (C_TEC), U.S. Chamber of Commerce, Executive Vice President, Global Innovation Policy Center (GIPC), U.S. Chamber of Commerce, Senior Advisor to the President and CEO, U.S. Chamber of Commerce

Tom Quaadman develops and executes strategic policies to implement a global corporate financial reporting system, address ongoing attempts of minority shareholder abuse of the proxy system, communicate the benefits of efficient American capital markets, and promote an innovation economy and the long-term interests of all investors.

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In the Global Race to Lead on Artificial Intelligence, America Must Win - uschamber.com