Monthly Archives: July 2022

A dolphin handler makes the signal for together with her hands, followed by create. The two trained dolphins disappear underwater, exchange sounds and then emerge, flip on to their backs and lift their tails. They have devised a new trick of their own and performed it in tandem, just as requested. It doesnt prove that theres language, says Aza Raskin. But it certainly makes a lot of sense that, if they had access to a rich, symbolic way of communicating, that would make this task much easier.

Raskin is the co-founder and president of Earth Species Project (ESP), a California non-profit group with a bold ambition: to decode non-human communication using a form of artificial intelligence (AI) called machine learning, and make all the knowhow publicly available, thereby deepening our connection with other living species and helping to protect them. A 1970 album of whale song galvanised the movement that led to commercial whaling being banned. What could a Google Translate for the animal kingdom spawn?

The organisation, founded in 2017 with the help of major donors such as LinkedIn co-founder Reid Hoffman, published its first scientific paper last December. The goal is to unlock communication within our lifetimes. The end we are working towards is, can we decode animal communication, discover non-human language, says Raskin. Along the way and equally important is that we are developing technology that supports biologists and conservation now.

Understanding animal vocalisations has long been the subject of human fascination and study. Various primates give alarm calls that differ according to predator; dolphins address one another with signature whistles; and some songbirds can take elements of their calls and rearrange them to communicate different messages. But most experts stop short of calling it a language, as no animal communication meets all the criteria.

Until recently, decoding has mostly relied on painstaking observation. But interest has burgeoned in applying machine learning to deal with the huge amounts of data that can now be collected by modern animal-borne sensors. People are starting to use it, says Elodie Briefer, an associate professor at the University of Copenhagen who studies vocal communication in mammals and birds. But we dont really understand yet how much we can do.

Briefer co-developed an algorithm that analyses pig grunts to tell whether the animal is experiencing a positive or negative emotion. Another, called DeepSqueak, judges whether rodents are in a stressed state based on their ultrasonic calls. A further initiative Project CETI (which stands for the Cetacean Translation Initiative) plans to use machine learning to translate the communication of sperm whales.

Yet ESP says its approach is different, because it is not focused on decoding the communication of one species, but all of them. While Raskin acknowledges there will be a higher likelihood of rich, symbolic communication among social animals for example primates, whales and dolphins the goal is to develop tools that could be applied to the entire animal kingdom. Were species agnostic, says Raskin. The tools we develop can work across all of biology, from worms to whales.

The motivating intuition for ESP, says Raskin, is work that has shown that machine learning can be used to translate between different, sometimes distant human languages without the need for any prior knowledge.

This process starts with the development of an algorithm to represent words in a physical space. In this many-dimensional geometric representation, the distance and direction between points (words) describes how they meaningfully relate to each other (their semantic relationship). For example, king has a relationship to man with the same distance and direction that woman has to queen. (The mapping is not done by knowing what the words mean but by looking, for example, at how often they occur near each other.)

It was later noticed that these shapes are similar for different languages. And then, in 2017, two groups of researchers working independently found a technique that made it possible to achieve translation by aligning the shapes. To get from English to Urdu, align their shapes and find the point in Urdu closest to the words point in English. You can translate most words decently well, says Raskin.

ESPs aspiration is to create these kinds of representations of animal communication working on both individual species and many species at once and then explore questions such as whether there is overlap with the universal human shape. We dont know how animals experience the world, says Raskin, but there are emotions, for example grief and joy, it seems some share with us and may well communicate about with others in their species. I dont know which will be the more incredible the parts where the shapes overlap and we can directly communicate or translate, or the parts where we cant.

He adds that animals dont only communicate vocally. Bees, for example, let others know of a flowers location via a waggle dance. There will be a need to translate across different modes of communication too.

The goal is like going to the moon, acknowledges Raskin, but the idea also isnt to get there all at once. Rather, ESPs roadmap involves solving a series of smaller problems necessary for the bigger picture to be realised. This should see the development of general tools that can help researchers trying to apply AI to unlock the secrets of species under study.

For example, ESP recently published a paper (and shared its code) on the so called cocktail party problem in animal communication, in which it is difficult to discern which individual in a group of the same animals is vocalising in a noisy social environment.

To our knowledge, no one has done this end-to-end detangling [of animal sound] before, says Raskin. The AI-based model developed by ESP, which was tried on dolphin signature whistles, macaque coo calls and bat vocalisations, worked best when the calls came from individuals that the model had been trained on; but with larger datasets it was able to disentangle mixtures of calls from animals not in the training cohort.

Another project involves using AI to generate novel animal calls, with humpback whales as a test species. The novel calls made by splitting vocalisations into micro-phonemes (distinct units of sound lasting a hundredth of a second) and using a language model to speak something whale-like can then be played back to the animals to see how they respond. If the AI can identify what makes a random change versus a semantically meaningful one, it brings us closer to meaningful communication, explains Raskin. It is having the AI speak the language, even though we dont know what it means yet.

A further project aims to develop an algorithm that ascertains how many call types a species has at its command by applying self-supervised machine learning, which does not require any labelling of data by human experts to learn patterns. In an early test case, it will mine audio recordings made by a team led by Christian Rutz, a professor of biology at the University of St Andrews, to produce an inventory of the vocal repertoire of the Hawaiian crow a species that, Rutz discovered, has the ability to make and use tools for foraging and is believed to have a significantly more complex set of vocalisations than other crow species.

Rutz is particularly excited about the projects conservation value. The Hawaiian crow is critically endangered and only exists in captivity, where it is being bred for reintroduction to the wild. It is hoped that, by taking recordings made at different times, it will be possible to track whether the speciess call repertoire is being eroded in captivity specific alarm calls may have been lost, for example which could have consequences for its reintroduction; that loss might be addressed with intervention. It could produce a step change in our ability to help these birds come back from the brink, says Rutz, adding that detecting and classifying the calls manually would be labour intensive and error prone.

Meanwhile, another project seeks to understand automatically the functional meanings of vocalisations. It is being pursued with the laboratory of Ari Friedlaender, a professor of ocean sciences at the University of California, Santa Cruz. The lab studies how wild marine mammals, which are difficult to observe directly, behave underwater and runs one of the worlds largest tagging programmes. Small electronic biologging devices attached to the animals capture their location, type of motion and even what they see (the devices can incorporate video cameras). The lab also has data from strategically placed sound recorders in the ocean.

ESP aims to first apply self-supervised machine learning to the tag data to automatically gauge what an animal is doing (for example whether it is feeding, resting, travelling or socialising) and then add the audio data to see whether functional meaning can be given to calls tied to that behaviour. (Playback experiments could then be used to validate any findings, along with calls that have been decoded previously.) This technique will be applied to humpback whale data initially the lab has tagged several animals in the same group so it is possible to see how signals are given and received. Friedlaender says he was hitting the ceiling in terms of what currently available tools could tease out of the data. Our hope is that the work ESP can do will provide new insights, he says.

But not everyone is as gung ho about the power of AI to achieve such grand aims. Robert Seyfarth is a professor emeritus of psychology at University of Pennsylvania who has studied social behaviour and vocal communication in primates in their natural habitat for more than 40 years. While he believes machine learning can be useful for some problems, such as identifying an animals vocal repertoire, there are other areas, including the discovery of the meaning and function of vocalisations, where he is sceptical it will add much.

The problem, he explains, is that while many animals can have sophisticated, complex societies, they have a much smaller repertoire of sounds than humans. The result is that the exact same sound can be used to mean different things in different contexts and it is only by studying the context who the individual calling is, how are they related to others, where they fall in the hierarchy, who they have interacted with that meaning can hope to be established. I just think these AI methods are insufficient, says Seyfarth. Youve got to go out there and watch the animals.

There is also doubt about the concept that the shape of animal communication will overlap in a meaningful way with human communication. Applying computer-based analyses to human language, with which we are so intimately familiar, is one thing, says Seyfarth. But it can be quite different doing it to other species. It is an exciting idea, but it is a big stretch, says Kevin Coffey, a neuroscientist at the University of Washington who co-created the DeepSqueak algorithm.

Raskin acknowledges that AI alone may not be enough to unlock communication with other species. But he refers to research that has shown many species communicate in ways more complex than humans have ever imagined. The stumbling blocks have been our ability to gather sufficient data and analyse it at scale, and our own limited perception. These are the tools that let us take off the human glasses and understand entire communication systems, he says.

More here:

Can artificial intelligence really help us talk to the animals? - The Guardian

With headlines emerging about artificial intelligence (AI) reaching sentience, its clear that the power of AI remains both revered and feared. For any AI offering to reach its full potential, though, its executive sponsors must first be certain that the AI is a solution to a real business problem.

And as more enterprises and startups alike develop their AI capabilities, were seeing a common roadblock emerge known as AIs last mile problem. Generally, when machine learning engineers and data scientists refer to the last mile, theyre referencing the steps required to take an AI solution and make it available for generalized, widespread use.

The last mile describes the short geographical segment of delivery of communication and media services or the delivery of products to customers located in dense areas. Last mile logistics tend to be complex and costly to providers of goods and services who deliver to these areas.(Source: Investopedia).

Democratizing AI involves both the logistics of deploying the code or model as well as using the appropriate approach to track the models performance. The latter becomes especially challenging, however, since many models function as black boxes in terms of the answers that they provide. Therefore, determining how to track a models performance is a critical part of surmounting the last-mile hurdle. With less than half of AI projects ever reaching a production win, its evident that optimizing the processes that comprise the last mile will unlock significant innovation.

The biggest difficulty developers face comes after they build an AI solution. Tracking its performance can be incredibly challenging as its both context-dependent and varies based on the type of AI model. For instance, while we must compare the results of predictive models to a benchmark, we can examine outputs from less deterministic models such as personalization models with respect to their statistical characteristics. This also requires a deep understanding of what a good result actually entails. For example, during my time working on Google News, we created a rigorous process to evaluate AI algorithms. This involved running experiments in production and determining how to measure their success. The latter required looking at a series of metrics (long vs. short clicks, source diversity, authoritativeness, etc.) to determine if in fact the algorithm was a win. Another metric that we tracked on Google News is new source diversity in personalized feeds. In local development and experiments, the results might appear good, but at scale and as models evolve, the results may skew.

The solution, therefore, is two-fold:

Machine learning operations (MLOps) is becoming a new category of products necessary to adopt AI. MLOps are needed to establish good patterns and the tools required to increase confidence in AI solutions. Once AI needs are established, decision-makers must weigh the fact that while developing in-house may look attractive, it can be a costly affair given the approach is still nascent.

Looking ahead, cloud providers will start offering AI platforms as a commodity. In addition, innovators will consolidate more robust tooling, and the same rigors that we see with traditional software development will standardize and operationalize within the AI industry. Nonetheless, tooling is only a piece of the puzzle. There is significant work required to improve how we take an AI solution from idea to test to reality and ultimately measure success. Well get there more quickly when AIs business value and use case is determined from the outset.

Read More on Built Ins Expert Contributors NetworkRage Against the Machine Learning: My War With Recommendation Engines

Read the rest here:

Artificial Intelligence Has a 'Last Mile' Problem, and Machine Learning Operations Can Solve It - Built In

A study on the physics discovery titled "Automated discovery of fundamental variables hidden in experimental data" has been published in the journal Nature Computational Science.

Researchers from Columbia Engineering have developed a new artificial intelligence (AI) program that could derive the fundamental variables of physics from video footage of physical phenomena. The program analyzed videos of systems like the swinging double pendulum, which researchers already know four "state variables" exist for; the angle and angular velocity of each arm. Within a few hours, the AI determined there were 4.7 variables at play.

"We thought this answer was close enough. Especially since all the AI had access to was raw video footage, without any knowledge of physics or geometry. But we wanted to know what the variables actually were, not just their number," said Hod Lipson, director of the Creative Machines Lab in the Department of Mechanical Engineering.

Two of the variables it identified correlated with the angles of each arm. However, the other two were unclear, as the program interprets and visualizes the variables differently from how humans intuitively understand them. Nevertheless, as the AI was making accurate predictions about the system, it is clear it managed to identify four valid variables. The researchers then tested the AI on systems we don't fully understand, like a lava lamp, and a fireplace, identifying 8 and 24 variables, respectively.

"I always wondered, if we ever met an intelligent alien race, would they have discovered the same physics laws as we have, or might they describe the universe in a different way? Perhaps some phenomena seem enigmatically complex because we are trying to understand them using the wrong set of variables. In the experiments, the number of variables was the same each time the AI restarted, but the specific variables were different each time. So yes, there are alternative ways to describe the universe and it is quite possible that our choices aren't perfect," said Lipson.

You can read more from the study here.

Read the original here:

'Alternative physics' discovered by artificial intelligence - TweakTown

When the richest man in the world is being sued by one of the most popular social media companies, its news. But while most of the conversation about Elon Musks attempt to cancel his $44 billion contract to buy Twitter is focusing on the legal, social, and business components, we need to keep an eye on how the discussion relates to one of tech industrys most buzzy products: artificial intelligence.

The lawsuit shines a light on one of the most essential issues for the industry to tackle: What can and cant AI do, and what should and shouldnt AI do? The Twitter v Musk contretemps reveals a lot about the thinking about AI in tech and startup land and raises issues about how we understand the deployment of the technology in areas ranging from credit checks to policing.

At the core of Musks claim for why he should be allowed out of his contract with Twitter is an allegation that the platform has done a poor job of identifying and removing spam accounts. Twitter has consistently claimed in quarterly filings that less than 5% of its active accounts are spam; Musk thinks its much higher than that. From a legal standpoint, it probably doesnt really matter if Twitters spam estimate is off by a few percent, and Twitters been clear that its estimate is subjective and that others could come to different estimates with the same data. Thats presumably why Musks legal team lost in a hearing on July 19when they asked for more time to perform detailed discovery on Twitters spam-fighting efforts, suggesting that likely isnt the question on which the trial will turn.

Regardless of the legal merits, its important to scrutinise the statistical and technical thinking from Musk and his allies. Musks position is best summarised in his filing from July 15, which states: In a May 6 meeting with Twitter executives, Musk was flabbergasted to learn just how meager Twitters process was. Namely: Human reviewers randomly sampled 100 accounts per day (less than 0.00005% of daily users) and applied unidentified standards to somehow conclude every quarter for nearly three years that fewer than 5% of Twitter users were false or spam. The filing goes on to express the flabbergastedness of Musk by adding, Thats it. No automation, no AI, no machine learning.

Perhaps the most prominent endorsement of Musks argument here came from venture capitalist David Sacks,who quoted it while declaring, Twitter is toast. But theres an irony in Musks complaint here: If Twitter were using machine learning for the audit as he seems to think they should, and only labeling spam that was similar to old spam, it would actually produce a lower, less-accurate estimate than it has now.

There are three components to Musks assertion that deserve examination: his basic statistical claim about what a representative sample looks like, his claim that the spam-level auditing process should automated or use AI or machine learning, and an implicit claim about what AI can actually do.

On the statistical question, this is something any professional anywhere near the machine learning space should be able to answer (so can many high school students). Twitter uses a daily sampling of accounts to scrutinise a total of 9,000 accounts per quarter (averaging about 100 per calendar day) to arrive at its under-5% spam estimate. Though that sample of 9,000 users per quarter is, as Musk notes, a very small portion of the 229 million active users the company reported in early 2022, a statistics professor (or student) would tell you that thats very much not the point. Statistical significance isnt determined by what percentage of the population is sampled but simply by the actual size of the sample in question. As Facebook whistleblower Sophie Zhang put it, you can make the comparison to soup: It doesnt matter if you have a small or giant pot of soup, if its evenly mixed you just need a spoonful to taste-test.

The whole point of statistical sampling is that you can learn most of what you need to know about the variety of a larger population by studying a much-smaller but decently sized portion of it. Whether the person drawing the sample is a scientist studying bacteria, or a factory quality inspector checking canned vegetables, or a pollster asking about political preferences, the question isnt what percentage of the overall whole am I checking, but rather how much should I expect my sample to look like the overall population for the characteristics Im studying? If you had to crack open a large percentage of your cans of tomatoes to check for their quality, youd have a hard time making a profit, so you want to check the fewest possible to get within a reasonable range of confidence in your findings.

Also read: Why Understanding This 60S Sci-Fi Novel Is Key to Understanding Elon Musk

While this thinking does go against the grain of certain impulses (theres a reason why many people make this mistake), there is also a way to make this approach to sampling more intuitive. Think of the goal in setting sample size as getting a reasonable answer to the question, If I draw another sample of the same size, how different would I expect it to be? A classic approach to explaining this problem is to imagine youve bought a great mass of marbles, that are supposed to come in a specific ratio: 95% purple marbles and 5% yellow marbles. You want to do a quality inspection to ensure the delivery is good, so you load them into one of those bingo game hoppers, turn the crank, and start counting the marbles you draw, in each color. Lets say your first sample of 20 marbles has 19 purple and one yellow; should you be confident that you got the right mix from your vendor? You can probably intuitively understand that the next 20 random marbles you draw could end up being very different, with zero yellows or seven. But what if you draw 1,000 marbles, around the same as the typical political poll? What if you draw 9,000 marbles? The more marbles you draw, the more youd expect the next drawing to look similar, because its harder to hide random fluctuations in larger samples.

There are onlinecalculators that can let you run the numbers yourself. If you only draw 20 marbles and get one yellow, you can have 95% confidence that the yellows would be between 0.13% and 24.9% of the total not very exact. If you draw 1,000 marbles and get 50 yellows, you can have 95% confidence that yellows would be between 3.7% and 6.5% of the total; closer, but perhaps not something youd sign your name to in a quarterly filing. At 9,000 marbles with 450 yellow, you can have 95% confidence the yellows are between 4.56% and 5.47%; youre now accurate to within a range of less than half a percent, and at that point Twitters lawyers presumably told them theyd done enough for their public disclosure.

Printed Twitter logos are seen in this picture illustration taken April 28, 2022. Photo: Reuters/Dado Ruvic/Illustration/File Photo

This reality that statistical sampling works to tell us about large populations based on much-smaller samples underpins every area where statistics is used, from checking the quality of the concrete used to make the building youre currently sitting in, to ensuring the reliable flow of internet traffic to the screen youre reading this on.

Its also what drives all current approaches to artificial intelligence today. Specialists in the field almost never use the term artificial intelligence to describe their work, preferring to use machine learning. But another common way to describe the entire field as it currently stands is applied statistics. Machine learning today isnt really computers thinking in anything like what we assume humans do (to the degree we even understand how humans think, which isnt a great degree); its mostly pattern-matching and -identification, based on statistical optimisation. If you feed a convolutional neural network thousands of images of dogs and cats and then ask the resulting model to determine if the next image is of a dog or a cat, itll probably do a good job, but you cant ask it to explain what makes a cat different from a dog on any broader level; its just recognising the patterns in pictures, using a layering of statistical formulas.

Stack up statistical formulas in specific ways, and you can build a machine learning algorithm that, fed enough pictures, will gradually build up a statistical representation of edges, shapes, and larger forms until it recognises a cat, based on the similarity to thousands of other images of cats it was fed. Theres also a way in which statistical sampling plays a role: You dont need pictures of all the dogs and cats, just enough to get a representative sample, and then your algorithm can infer what it needs to about all the other pictures of dogs and cats in the world. And the same goes for every other machine learning effort, whether its an attempt to predict someones salary using everything else you know about them, with a boosted random forests algorithm, or to break down a list of customers into distinct groups, in a clustering algorithm like a support vector machine.

You dont absolutely have to understand statistics as well as a student whos recently taken a class in order to understand machine learning, but it helps. Which is why the statistical illiteracy paraded by Musk and his acolytes here is at least somewhat surprising.

But more important, in order to have any basis for overseeing the creation of a machine-learning product, or to have a rationale for investing in a machine-learning company, its hard to see how one could be successful without a decent grounding in the rudiments of machine learning, and where and how it is best applied to solve a problem. And yet, team Musk here is suggesting they do lack that knowledge.

Once you understand that all machine learning today is essentially pattern-matching, it becomes clear why you wouldnt rely on it to conduct an audit such as the one Twitter performs to check for the proportion of spam accounts. Theyre hand-validating so that they ensure its high-quality data, explained security professional Leigh Honeywell, whos been a leader at firms like Slack and Heroku, in an interview. She added, any data you pull from your machine learning efforts will by necessity be not as validated as those efforts. If you only rely on patterns of spam youve already identified in the past and already engineered into your spam-detection tools, in order to find out how much spam there is on your platform, youll only recognise old spam patterns, and fail to uncover new ones.

Also read: India Versus Twitter Versus Elon Musk Versus Society

Where Twitter should be using automation and machine learning to identify and remove spam is outside of this audit function, which the company seems to do. It wouldnt otherwise be possible tosuspend half a million accountsevery day and lock millions of accounts each week, as CEO Parag Agrawal claims. In conversations Ive had with cybersecurity workers in the field, its quite clear that large amounts of automation is used at Twitter (though machine learning specifically is actually relatively rare in the field because the results often arent as good as other methods, marketing claims by allegedly AI-based security firms to the contrary).

At least in public claims related to this lawsuit, prominent Silicon Valley figures are suggesting they have a different understanding of what machine learning can do, and when it is and isnt useful. This disconnect between how many nontechnical leaders in that world talk about AI, and what it actually is, has significant implications for how we will ultimately come to understand and use the technology.

The general disconnect between the actual work of machine learning and how its touted by many company and industry leaders is something data scientists often chalk up to marketing. Its very common to hear data scientists in conversation among themselves declare that AI is just a marketing term. Its also quite common to have companies using no machine learning at all describe their work as AI to investors and customers, who rarely know the difference or even seem to care.

This is a basic reality in the world of tech. In my own experience talking with investors who make investments in AI technology, its often quite clear that they know almost nothing about these basic aspects of how machine learning works. Ive even spoken to CEOs of rather large companies that rely at their core on novel machine learning efforts to drive their product, who also clearly have no understanding of how the work actually gets done.

Not knowing or caring how machine learning works, what it can or cant do, and where its application can be problematic could lead society to significant peril. If we dont understand the way machine learning actually works most often by identifying a pattern in some dataset and applying that pattern to new data we can be led deep down a path in which machine learning wrongly claims, for example, to measure someones face for trustworthiness (when this is entirely based on surveys in which people reveal their own prejudices), or that crime can be predicted (when many hyperlocal crime numbers are highly correlated with more police officers being present in a given area, who then make more arrests there), based almost entirely on a set of biased data or wrong-headed claims.

If were going to properly manage the influence of machine learning on our society on our systems and organisations and our government we need to make sure these distinctions are clear. It starts with establishing a basic level of statistical literacy, and moves on to recognising that machine learning isnt magicand that it isnt, in any traditional sense of the word, intelligent that it works by pattern-matching to data, that the data has various biases, and that the overall project can produce many misleading and/or damaging outcomes.

Its an understanding one might have expected or at least hoped to find among some of those investing most of their life, effort, and money into machine-learning-related projects. If even people that deep arent making those efforts to sort fact from fiction, its a poor omen for the rest of us, and the regulators and other officials who might be charged with keeping them in check.

This article was originally published on Future Tense, a partnership between Slate magazine, Arizona State University, and New America.

Go here to read the rest:

Elon Musk and Silicon Valley's Overreliance on Artificial Intelligence - The Wire

Browse in-depth TOC on "Artificial Intelligence (AI) in Drug Discovery Market"177 Tables 33 Figures 198 Pages

Download PDF Brochure: https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=151193446

North America is expected to dominate the Artificial Intelligence in Drug Discovery Marketin 2022.

North America accounted for the largest share of the global AI in drug discovery market in 2021 and also expected to grow at the highest CAGR during the forecast period. North America, which comprises the US, Canada, and Mexico, forms the largest market for AI in drug discovery. These countries have been early adopters of AI technology in drug discovery and development. Presence of key established players, well-established pharmaceutical and biotechnology industry, and high focus on R&D & substantial investment are some of the key factors responsible for the large share and high growth rate of this market

Prominent players in Artificial Intelligence in Drug Discovery Market:

Players adopted organic as well as inorganic growth strategies such as product upgrades, collaborations, agreements, partnerships, and acquisitions to increase their offerings, cater to the unmet needs of customers, increase their profitability, and expand their presence in the global AI in Drug Discovery Industry.

Request Sample Pages: https://www.marketsandmarkets.com/requestsampleNew.asp?id=151193446

Ai In Drug Discovery Market Dynamics

What benefits AI show in drug discovery and development process?

Drug discovery is a very costly and lengthy process, owing to which there is a need for alternative tools for discovering new drugs. Drug discovery and development are commonly conducted through in vivo and in vitro methods, which are very costly and time-consuming. Furthermore, it takes ~10 years on average for a new drug to enter the market at a cost of ~USD 2.6 billion (Source: Biopharmaceutical Research and Development.org). Several players operating in this market are developing platforms that can help in the rapid discovery of drugs. For instance, Insilico Medicine (US) developed an AI-based drug discovery system, GENTRL, with which it could develop six experimental novel molecules within 21 days.

How and why AI workforce shortage is important retraining factor holding back the growth of the market?

AI is a complex system, and companies require a workforce with specific skill sets to design, manage, and implement AI systems. Personnel dealing with AI systems should be familiar and aware of technologies such as machine intelligence, deep learning, cognitive computing, image recognition and other AI technologies. Additionally, integrating AI technologies into existing systems is a challenging task that necessitates substantial data processing in order to replicate human brain behavior. Even slight errors might cause system failure and have a negative impact on the desired outcome. The absence of professional standards and certifications in AI/ML technologies is restraining the growth of AI

What are the emerging markets for Artificial Intelligence in Drug Discovery?

Emerging economies such as India, China, and countries in the Middle East are expected to offer potential growth opportunities for players operating in the AI in drug discovery market. In most of these countries, the demand for pharmaceuticals is expected to increase significantly, owing to the rising incidence of chronic and infectious diseases, increasing income levels, and improving healthcare infrastructure. As a result, these markets are very attractive for companies whose profit margins are affected by stagnation in mature markets, the patent expiration of drugs, and increasing regulatory hurdles.

Speak to Analyst: https://www.marketsandmarkets.com/speaktoanalystNew.asp?id=151193446

Scope of the Artificial Intelligence (AI) in Drug Discovery Market Report:

Report Metric

Details

Market size available for years

2020-2027

Base year considered

2021

Forecast period

20222027

Forecast units

Value (USD Billion)

Segments covered

Offering, Technology, Application, End User,And Region

Geographies covered

North America (US, and Canada), Europe (Germany, France, UK, Italy, and the RoE), Asia Pacific (Japan, China, India, and RoAPAC), and RoW

Companies covered

NVIDIA Corporation (US), Microsoft (US), Google (US), Exscientia (UK), Schrdinger (US), Atomwise, Inc. (US), BenevolentAI (UK), NuMedii (US), BERG LLC (US), Cloud Pharmaceuticals (US), Insilico Medicine (US), Cyclica (Canada), Deep Genomics (Canada), IBM (US), BIOAGE (US), Valo Health (US), Envisagenics (US), twoXAR (US), Owkin, Inc. (US), XtalPi (US), Verge Genomics (US), Biovista (US), Evaxion Biotech (Denmark), Iktos (France), Standigm (South Korea), and BenchSci (Canada)

Browse Adjacent Markets: Healthcare IT Market Research Reports & Consulting

Browse Related Reports:

Drug Discovery Services Marketby Process (Target Selection, Validation, Hit-to-lead), Type (Chemistry, Biology), Drug Type (Small molecules, biologics), Therapeutic Area (Oncology, Neurology) End User (Pharma, Biotech) - Global Forecast to 2026

Artificial Intelligence in Genomics Marketby Offering (Software, Services), Technology (Machine Learning, Computer Vision), Functionality (Genome Sequencing, Gene Editing), Application (Diagnostics), End User (Pharma, Research)-Global Forecasts to 2025

About MarketsandMarkets

MarketsandMarkets provides quantified B2B research on 30,000 high growth niche opportunities/threats which will impact 70% to 80% of worldwide companies' revenues. Currently servicing 7500 customers worldwide including 80% of global Fortune 1000 companies as clients. Almost 75,000 top officers across eight industries worldwide approach MarketsandMarkets for their painpoints around revenues decisions.

Our 850 fulltime analyst and SMEs at MarketsandMarkets are tracking global high growth markets following the "Growth Engagement Model GEM". The GEM aims at proactive collaboration with the clients to identify new opportunities, identify most important customers, write "Attack, avoid and defend" strategies, identify sources of incremental revenues for both the company and its competitors. MarketsandMarkets now coming up with 1,500 MicroQuadrants (Positioning top players across leaders, emerging companies, innovators, strategic players) annually in high growth emerging segments. MarketsandMarkets is determined to benefit more than 10,000 companies this year for their revenue planning and help them take their innovations/disruptions early to the market by providing them research ahead of the curve.

MarketsandMarkets's flagship competitive intelligence and market research platform, "Knowledge Store" connects over 200,000 markets and entire value chains for deeper understanding of the unmet insights along with market sizing and forecasts of niche markets.

Contact:Mr. Aashish MehraMarketsandMarkets INC.630 Dundee RoadSuite 430Northbrook, IL 60062USA: +1-888-600-6441Email: [emailprotected] Research Insight: https://www.marketsandmarkets.com/ResearchInsight/ai-in-drug-discovery-market.asp Visit Our Web Site: https://www.marketsandmarkets.com Content Source: https://www.marketsandmarkets.com/PressReleases/ai-in-drug-discovery.asp

Photo: https://mma.prnewswire.com/media/1868356/AI_DRUG_DISCOVERY.jpg Logo: https://mma.prnewswire.com/media/660509/MarketsandMarkets_Logo.jpg

SOURCE MarketsandMarkets

Read more from the original source:

Artificial Intelligence (AI) in Drug Discovery Market worth $4.0 billion by 2027 - Exclusive Report by MarketsandMarkets - PR Newswire

DENVER--(BUSINESS WIRE)--Palantir Technologies Inc. (NYSE: PLTR) today announced that it will expand its work with the U.S. Army Research Laboratory to implement data and artificial intelligence (AI)/machine learning (ML) capabilities for users across the combatant commands (COCOMs). The contract totals $99.9 million over two years.

Palantir first partnered with the Army Research Lab to provide those on the frontlines with state-of-the-art operational data and AI capabilities in 2018. Palantirs platform has supported the integration, management, and deployment of relevant data and AI model training to all of the Armed Services, COCOMs, and special operators. This extension grows Palantirs operational RDT&E work to more users globally.

Maintaining a leading edge through technology is foundational to our mission and partnership with the Army Research Laboratory, said Akash Jain, President of Palantir USG. Our nations armed forces require best-in-class software to fulfill their missions today while rapidly iterating on the capabilities they will need for tomorrows fight. We are honored to support this critical work by teaming up to deliver the most advanced operational AI capabilities available with dozens of commercial and public sector partners.

By working with the U.S. Army Research Lab, integrating with partner vendors, and iterating with users on the front lines, Palantirs software platforms will continue to quickly implement advanced AI capabilities against some of DODs most pressing problem sets. Were looking forward to fielding our newest ML, Edge, and Space technologies alongside our U.S. military partners, said Shannon Clark, Senior Vice President of Innovation, Federal. These technologies will enable operators in the field to leverage AI insights to make decisions across many fused domains. From outer space to the sea floor, and everything in between.

About Palantir Technologies Inc.

Foundational software of tomorrow. Delivered today. Additional information is available at https://www.palantir.com.

Forward-Looking Statements

This press release contains forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. These statements may relate to, but are not limited to, Palantirs expectations regarding the amount and the terms of the contract and the expected benefits of our software platforms. Forward-looking statements are inherently subject to risks and uncertainties, some of which cannot be predicted or quantified. Forward-looking statements are based on information available at the time those statements are made and were based on current expectations as well as the beliefs and assumptions of management as of that time with respect to future events. These statements are subject to risks and uncertainties, many of which involve factors or circumstances that are beyond our control. These risks and uncertainties include our ability to meet the unique needs of our customer; the failure of our platforms to satisfy our customer or perform as desired; the frequency or severity of any software and implementation errors; our platforms reliability; and our customers ability to modify or terminate the contract. Additional information regarding these and other risks and uncertainties is included in the filings we make with the Securities and Exchange Commission from time to time. Except as required by law, we do not undertake any obligation to publicly update or revise any forward-looking statement, whether as a result of new information, future developments, or otherwise.

More:

U.S. Army Research Lab Expands Artificial Intelligence and Machine Learning Contract with Palantir for $99.9M - Business Wire