Category Archives: Deep Mind
AI use cases are expanding and evolving in healthcare (Photo by STR / AFP) / China OUT (Photo credit should read STR/AFP/Getty Images)
Artificial intelligence (AI) is getting increasingly sophisticated at doing what humans do but more efficiently, more quickly, and at a lower cost.
The potential for AI in healthcare is vast, and PwC estimates the global market for AI healthcare applications will erupt from US$663.8 million in 2014 to US$6.7 billion in 2021. This increased demand correlates with a substantial rise in the complexity and abundance of data.
There are myriaduse cases for AI in the healthcare industry and it is often structured around typical processes that are used in the healthcare industry.
Lets take a look at how AI is helping key stakeholders like hospitals, diagnostic labs, and pharmaceutical companies in various ways.
In an era of technological ubiquity, data fuels innovation.
Data mining is being deployed to find insights and patterns from large databases.
The healthcare industry captures large volumes of patient records and with appropriate analysis of this data. Currently, the sector employs data mining to develop early detection systems by using clinical and diagnosis data.
Using machine learning tools, the healthcare sector can address a plethora of diseases prior to their occurrence.
Tech giants, such as Google and IBM are using AI to unearth patient data which are structured and unstructured. The data is extracted by mining medical records or by deciphering physician-patient interactions (voice and non-voice-based interactions).
According to Minds Field Globals report, AI has expanded substantially in the fields of medical imaging and diagnostics over the past couple of years, thereby enabling medical researchers and doctors to deliver flawless clinical practice.
Paving the way for quantification and standardization, deep learning is aiding in the prevention of errors in diagnostics and improving the test outcome, the report said.
Furthermore, AI is improving the assessment in medical imaging to detect cases such as malignancy and Diabetic Retinopathy (DR). It is also assisting with quantifying blood flow and providing visualization, it added.
The Da Vinci Surgery System was the first surgical robot that was approved by the FDA for general laparoscopic surgery 15 years ago.
Since then, many other surgical robots have been introduced. Including the current generation of robots that are integrating AI in surgery, the next generation will be powered by machine learning.
In the near future, we may witness AI platforms such as DeepMind, IBM Watson, and other advanced AI tools enabling physicians and hospitals to deliver promising surgical interventions.
Currently, IBM Watson has advanced medical cognitive and NLP capabilities to respond to queries by surgeons.
Furthermore, similar AI platforms aid in monitoring blood in real-time, detect physiological response to pain, and provide navigation support in arthroscopy and open surgery.
Inevitably, AI is revolutionizing the way pharmaceutical companies develop medicines. In fact, AI and ML have been playing a critical role in the industry and consumer healthcare business.
The McKinsey Global Institute estimates that AI and machine learning in the pharmaceutical industry could generate nearly US$100 billion annually across the US healthcare system.
From augmented intelligence applications such as disease identification and diagnosis, helping identify patients for clinical trials, drug manufacturing, and predictive forecasting, these technologies have proven critical to the sector.
Top pharmaceutical companies, including Roche, Pfizer, Merck, AstraZeneca, GSK, Sanofi, AbbVie, Bristol-Myers Squibb, and Johnson & Johnson have already collaborated with or acquired AI technologies.
Dashveenjit Kaur| @DashveenjitK
Dashveen writes for Tech Wire Asia and TechHQ, providing research-based commentary on the exciting world of technology in business. Previously, she reported on the ground of Malaysia's fast-paced political arena and stock market.
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AI use cases are expanding and evolving in healthcare - Tech Wire Asia
With the advent of cheap genetic sequencing, the world of biology has been flooded with 2D data. Now, artificial intelligence is pushing the field into three dimensions.
On Thursday, Alphabet-owned AI outfit DeepMind announced it has used its highly accurate deep learning model AlphaFold2 to predict the 3D structure of 350,000 proteins including nearly every protein expressed in the human body from their amino acid sequences. Those predictions, reported in Nature and released to the public in the AlphaFold Protein Structure Database, are a powerful tool to unravel the molecular mechanisms of the human body and deploy them in medical innovations.
This resource were making available, starting at about twice as many predictions as there are structures in the Protein Data Bank, is just the beginning, said John Jumper, lead AlphaFold researcher at DeepMind, in a press call. The company intends to continue adding predicted structures to the database.
When we reach the scale of 100 million predictions that cover all proteins, were really starting to talk about transformative uses, he said.
One of those transformations may come in the databases application to drug discovery. In an uncommon move, DeepMind has chosen to make the database released in partnership with the European Molecular Biology Laboratory completely open source for any use.
So we hope, actually, that drug discovery and pharma will use it, DeepMind CEO Demis Hassabis said during the call.
DeepMinds predictions could be of interest to AI-driven drug companies looking to hone their models, biotech startups hoping to expand their list of target proteins, and even companies engineering new designer enzymes.
Whenever theres a breakthrough, I think rising tides lift all boats. And this opens up a super exciting era in structure-driven drug design, said Abraham Heifets, CEO of AI-driven drug discovery company Atomwise, which uses its own library of computationally inferred protein structures to find molecules that selectively bind with proteins involved in disease. Having better information on the shape of a protein is how you design a molecule that fits into that protein really well, to shut down or arrest that disease process.
DeepMind had committed to opening up its work in November, after AlphaFold2 took home the top prize in the protein-folding prediction contest CASP, in what was hailed as a solution to the long-standing protein folding problem. But in the seven months since then, structural biologists got antsy waiting for the groundbreaking work to go public. As STAT reported last week, DeepMind raced to publish its open source code and methods in Nature, just as a group at the University of Washington published their own attempt at replicating AlphaFolds approach in Science.
With the database adding so many new structural predictions, researchers from drug developers and basic scientists will have a lot of new material to work with. Well look through it very quickly to see if there are proteins were interested in that are suddenly enabled by this new dataset, said Heifets.
Jumper thinks the new tool will remove a difficult choice that plagues some biologists: If a protein structure isnt available, they could spend lots of time and money on physical experiments to figure it out (which still might not pan out), or they could simply go without and focus on functional studies. Suddenly, the access to structures is going to increase dramatically, he told STAT. I think thats really going to change how scientists approach these biological questions.
Still, these arent plug-and-play structures: Theyre predictions, and they come with caveats that scientists will have to consider.
Me as a biochemist, Id like to understand is this a good model or not? What about this algorithm is confident or not? said Frank von Delft, who leads protein crystallography at the University of Oxfords Centre for Medicines Discovery. I think that will be the key. Can you tell me, Yeah, I kind of nailed it, and this one Im struggling to nail, but this one is easy to get right?
To answer that question, DeepMind built measures into its predictions to help researchers determine whether to rely on the structures for their work. Preparing the predictions has actually only been a small part of this work, DeepMinds Kathryn Tunyasuvunakool, lead author on the paper, said in the call. Perhaps even more effort has gone into providing both local and global confidence metrics.
Across the board, AlphaFold2 predicted 58% of amino acids in the human proteome all the proteins expressed by the human body with confidence, and 35.7% with a very high degree of confidence. At that level, the model could nail not just the backbone of the protein, but the orientation of its side chains. The degree of confidence required will depend on how scientists are using the prediction. If you were looking at, say, the active site of an enzyme, you would want the residues involved to be in that highest confidence bracket, said Tunyasuvunakool, but actually theres an awful lot of utility even in the next-highest confidence bracket.
It is kind of overwhelming what they can do, said Arne Elofsson, a bioinformatician at Stockholm University.
The AlphaFold database doesnt spell doom for experimental biologists, those who painstakingly determine the physical structure of proteins using methods like X-ray crystallography, cryo-electron microscopy, and nuclear magnetic resonance spectroscopy. For many applications, there will be a need to validate the structures proposed by these models, said Elofsson.
But as predicted structures become more accepted, the AlphaFold database could change the way structural biology prioritizes its work and even what it considers its gold standard.
Normally in CASP we assume the experiment is the gold standard, and if you disagree youre wrong, said John Moult, a computational biologist at the University of Maryland who founded the contest. And with DeepMind some of the time thats true, but quite a lot of the time not true. In other words, theres room for error in the physical experiments used to determine protein structure and with a highly accurate prediction model, a computer could in some cases do the job better. So I think that theres a lot to sort out there: When is a detail actually computationally better than the corresponding experimental result?
That will be a philosophical question for the field to confront over time, especially as AlphaFolds approach continues to develop. DeepMind made massive gains between its first entry in 2018s CASP competition, with AlphaFold1, and AlphaFold2 in 2020. This is sort of v2.1 in a way, and we expect there will be more improvements over time, said Hassabis, adding that DeepMind may update the database as more experimental protein structures are solved or as the computational model continues to be developed.
As the database expands, so too could the set of structures that could be applied to drug discovery. A thing that people dont really know or think about is that theres 20,000 human genes, but only 4% of those have ever had a drug approved by the FDA, said Heifets. So we have many more protein targets that we could go after than weve ever had medicine brought to bear against. DeepMind has established a partnership with the Drugs for Neglected Diseases Initiative to develop approaches for Chagas disease and leishmaniasis.
But there are also uses for the database that are as yet unseen. AlphaFold is a paradigm change in the level of accuracy which biologists can now expect, which will unlock other applications, Pushmeet Kohli, DeepMinds head of AI for science, told STAT. Which is why we wanted to make AlphaFold broadly accessible: So the community would not just leverage it for existing applications, like in drug discovery, but other applications they might not even have been thinking about until now.
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DeepMind releases massive database of 3D protein structures - STAT - STAT
By Matthew Sparkes
Detemining the delicate folds of proteins traditionally takes ages, but DeepMind AI speeds that up
It took decades of painstaking research to map the structure of just 17 per cent of the proteins used within the human body, but less than a year for UK-based AI company DeepMind to raise that figure to 98.5 per cent. The company is making all this data freely available, which could lead to rapid advances in the development of new drugs.
Determining the complex, crumpled shape of proteinsbased on the sequence of amino acids that make them has been a huge scientific hurdle. Some amino acids are attracted to others, some are repelled by water, and the chains form intricate shapes that are hard to calculate accurately.Understanding these structures enables new, highly targeted drugs to be designed that bind to specific parts of proteins.
Genetic research had long provided the ability to determine the sequence of a protein, but an efficient way of finding the shape crucial to understanding its properties has proven elusive.Although supercomputers and distributed computing projectshave been effective, they have failed to make significant progress.
DeepMind published research last year that proved that AI can solve the problem quickly. Its AlphaFold neural network was trained on sections of previously solved protein shapes and learned to deduce the structure of new sequences, which were then checked against experimental data.
Since then, the company has been applying and refining the technology to thousands of proteins, beginning with the human proteome, proteins relevant to covid-19 and others that will most benefit immediate research. It is now releasing the results in a database created in partnership with the European Molecular Biology Laboratory.
DeepMind has mapped the structure of 98.5 per cent of the 20,000 or so proteins in the human body. For 35.7 per cent of these, the algorithm gave a confidence of over 90 per cent accuracy in predicting its shape.
The company has released more than 350,000 protein structure predictions in total, including those for 20 additional model organisms that are important for biological research, from Escherichia coli to yeast. The team hopes that within months it can add almost every sequenced protein known to science more than 100 million structures.
John Moult at the University of Maryland says the rise of AI in the area of protein folding had been a profound surprise.
Its revolutionary in a sense thats hard to get your head around, he says. If youre working on some rare disease and you never had a structure, now youll be able to go and look at structural information which was basically very, very hard or impossible to get before.
Demis Hassabis, chief executive and founder of DeepMind, says that AlphaFold which is composed of around 32 separate algorithms and has been made open source is now solving protein shapes in minutes or, in some cases, seconds using hardware no more sophisticated than a standard graphics card.
It takes one [graphics processing unit] a few minutes to fold one protein, which of course would have taken years of experimental work, he says. Were just going to put this treasure trove of dataout there. Its a little bit mind blowing in a way because going from the breakthrough of creating a system that can do that to actually producing all the data has only been a matter of months. We hope its going to become a sort of standard tool that all biologists around the world use.
The team also added a confidence measure to all structure predictions, which Hassabis says he felt was vital given that the results will be the basis for research efforts.Hassabis believes that some portion of human proteins for which the predicted structure had lower confidence scores could be down to errors in the sequence or perhaps something intrinsic about the biology, such as proteins that are inherently disordered or unpredictable. The 1.5 per cent remaining of the human proteome which no structure has been published for were proteins with sequences longer than 2700 segments, which were excluded for the time being to minimise runtime.
Journal reference: Nature, DOI: https://www.nature.com/articles/s41586-021-03828-1
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Good morning, Portland! Reminder: There's only one week left in July, meaning there's only one week left to enjoy boozy $5 slushies. Please act accordingly!!!
Here are the headlines.
You'll want to read this story about powerful Portland businessmen trying to convince District Attorney Mike Schmidt to prosecute more protesters arrested on bullshit charges. It's frustrating as hell, but ultimately gratifying:
Here's another environmental phenomenon to worry about: Oregon is experiencing a "hypoxia season"when oxygen levels drop to low levels in the ocean off the Oregon coastthat's much earlier than usual. That could mean trouble for both crabs and bottom-dwelling fish off the coast.
Portland City Council unanimously approved a $600,000 settlement agreement Wednesday to the family of Terrell Johnson, a 24-year-old man killed by a Portland police officer in 2017. Johnson died on May 10, 2017, after being chased on foot by former PPB officer Samson Ajir from the SE Flavel MAX platform.
A Portland police officer shot and wounded a member of the public Tuesday evening at a convenience store in Northwest Portlandthe fourth shooting by PPB this year. New information is still coming out, but Alex Zielinski has more details on the shooting.
With limited fire-fighting resources, some Oregonians are forced to take matters into their own hands:
Disturbing headline of the day, courtesy of NBC News: "As GOP supporters die of Covid, the party remains split in its vaccination message."
NPR has a report out about a new trend with the United States Supreme Court: Last month, the Court twice ruled in favor of giving the President more power over federal regulatory agencies, such as the United States Patent and Trademark Office or the Federal Housing Finance Agency. This means that the agencies, which are meant to simply enforce the rules, could become more overtly political, depending on the whims of whoever happens to be President at the time.
Looking forward to a few months from now, when I can sit back and let an AI bot write this column:
Let's check in on the Tokyo Olympics, where everything is going great, the athletes are happy and healthy, and the world is coming together to enjoy some sports! Oh, what's that? The opening ceremony director was fired for making Holocaust jokes? Yeah, okay, sounds about right.
And finally, let's all sit in awe of this fast-acting teen for a moment:
DeepMind, the AI unit of Google that invented the chess champ neural network AlphaZero a few years back, shocked the world again in November with a program that had solved a decades-old problem of how proteins fold. The program handily beat all competitors, in what one researcher called a "watershed moment" that promises to revolutionize biology.
AlphaFold 2, as it's called, was described at the time only in brief terms, in a blog post by DeepMind and in a paper abstract provided by DeepMind for the competition in which they submitted the program, the Critical Assessment of Techniques for Protein Structure Prediction biannual competition.
Last week, DeepMind finally revealed just how it's done, offering up not only a blog post but also a16-page summary paperwritten by DeepMind's John Jumper and colleagues in Nature magazine, a 62-page collection of supplementary material, and a code library on GitHub. A story on the new details by Nature's Ewan Calloway characterizes the data dump as "protein structure coming to the masses."
So, what have we learned? A few things. As the name suggests, this neural net is the successor to the first AlphaFold, which had also trounced competitors in the prior competition in 2018. The most immediate revelation of AlphaFold 2 is that making progress in artificial intelligence can require what's called an architecture change.
The architecture of a software program is the particular set of operations used and the way they are combined. The first AlphaFold was made up of a convolutional neural network, or "CNN," a classic neural network that has been the workhorse of many AI breakthroughs in the past decade, such as containing triumphs in the ImageNet computer vision contest.
But convolutions are out, and graphs are in. Or, more specifically, the combination of graph networks with what's called attention.
A graph network is when some collection of things can be assessed in terms of their relatedness and how they're related via friendships -- such as people in a social network. In this case, AlphaFold uses information about proteins to construct a graph of how near to one another different amino acids are.
Also: Google DeepMind's effort on COVID-19 coronavirus rests on the shoulders of giants
These graphs are manipulated by the attention mechanism that has been gaining in popularity in many quarters of AI. Broadly speaking, attention is the practice of adding extra computing power to some pieces of input data. Programs that exploit attention have lead to breakthroughs in a variety of areas, but especially natural language processing, as in the case of Google's Transformer.
The part that used convolutions in the first AlphaFold has been dropped in Alpha Fold 2, replaced by a whole slew of attention mechanisms.
Use of attention runs throughout AlphaFold 2. The first part of AlphaFold is what's called EvoFormer, and it uses attention to focus processing on computing the graph of how each amino acid relates to another amino acid. Because of the geometric forms created in the graph, Jumper and colleagues refer to this operation of estimating the graph as "triangle self-attention."
Echoing natural language programs, the EvoFormer allows the triangle attention to send information backward to the groups of amino acid sequences, known as "multi-sequence alignments," or "MSAs," a common term in bioinformatics in which related amino acid sequences are compared piece by piece.
The authors consider the MSAs and the graphs to be in a kind of conversation thanks to attention -- what they refer to as a "joint embedding." Hence, attention is leading to communication between parts of the program.
The second part of AlphaFold 2, following the EvoFormer, is what's called a Structure Module, which is supposed to take the graphs that the EvoFormer has built and turn them into specifications of the 3-D structure of the protein, the output that wins the CASP competition.
Here, the authors have introduced an attention mechanism that calculates parts of a protein in isolation, called an "invariant point attention" mechanism. They describe it as "a geometry-aware attention operation."
The Structure Module initiates particles at a kind of origin point in space, which you can think of as a 3-D reference field, called a "residue gas," and then proceeds to rotate and shift the particles to produce the final 3-D configuration. Again, the important thing is that the particles are transformed independently of one another, using the attention mechanism.
Why is it important that graphs, and attention, have replaced convolutions? In the original abstract offered for the research last year, Jumper and colleagues pointed out a need to move beyond a fixation on what are called "local" structures.
Going back to AlphaFold 1, the convolutional neural network functioned by measuring the distance between amino acids, and then summarizing those measurements for all pairs of amino acids as a 2-D picture, known as a distance histogram, or "distogram." The CNN then operated by poring over that picture, the way CNNs do, to find local motifs that build into broader and broader motifs spanning the range of distances.
But that orderly progression from local motifs can ignore long-range dependencies, which are one of the important elements that attention supposedly captures. For example, the attention mechanism in the EvoFormer can connect what is learned in the triangle attention mechanism to what is learned in the search of the MSA -- not just one section of the MSA, but the entire universe of related amino acid sequences.
Hence, attention allows for making leaps that are more "global" in nature.
Another thing we see in AlphaFold is the end-to-end goal. In the original AlphaFold, the final assembly of the physical structure was simply driven by the convolutions, and what they came up with.
In AlphaFold 2, Jumper and colleagues have emphasized training the neural network from "end to end." As they say:
"Both within the Structure Module and throughout the whole network, we reinforce the notion of iterative refinement by repeatedly applying the final loss to outputs then feeding the outputs recursively to the same modules. The iterative refinement using the whole network (that we term 'recycling' and is related to approaches in computer vision) contributes significantly to accuracy with minor extra training time."
Hence, another big takeaway from AlphaFold 2 is the notion that a neural network really needs to be constantly revamping its predictions. That is true both for the recycling operation, but also in other respects. For example, the EvoFormer, the thing that makes the graphs of amino acids, revises those graphs at each of the multiple stages, what are called "blocks," of the EvoFormer. Jumper and team refer to this constant updates as "constant communication" throughout the network.
As the authors note, through constant revision, the Structure piece of the program seems to "smoothly" refine its models of the proteins. "AlphaFold makes constant incremental improvements to the structure until it can no longer improve," they write. Sometimes, that process is "greedy," meaning, the Structure Module hits on a good solution early in its layers of processing; sometimes, it takes longer.
Also: AI in sixty seconds
In any event, in this case the benefits of training a neural network -- or a combination of networks -- seem certain to be a point of emphasis for many researchers.
Alongside that big lesson, there is an important mystery that remains at the center of AlphaFold 2: Why?
Why is it that proteins fold in the ways they do? AlphaFold 2 has unlocked the prospect of every protein in the universe having its structure revealed, which is, again, an achievement decades in the making. But AlphaFold 2 doesn't explain why proteins assume the shape that they do.
Proteins are amino acids, and the forces that make them curl up into a given shape are fairly straightforward -- things like certain amino acids being attracted or repelled by positive or negative charges, and some amino acids being "hydrophobic," meaning, they stay farther away from water molecules.
What is still lacking is an explanation of why it should be that certain amino acids take on shapes that are so hard to predict.
AlphaFold 2 is a stunning achievement in terms of building a machine to transform sequence data into protein models, but we may have to wait for further study of the program itself to know what it is telling us about the big picture of protein behavior.
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DeepMind's AlphaFold 2 reveal: Convolutions are out, attention is in - ZDNet
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DeepMind this week open-sourced AlphaFold 2, its AI system that predicts the shape of proteins, to accompany the publication of a paper in the journal Nature. With the codebase now available, DeepMind says it hopes to broaden access for researchers and organizations in the health care and life sciencefields.
The recipe for proteins large molecules consisting of amino acids that are the fundamental building blocks of tissues, muscles, hair, enzymes, antibodies, and other essential parts of living organisms are encoded in DNA. Its these genetic definitions that circumscribe their three-dimensional structures, which in turn determine their capabilities. But protein folding, as its called, is notoriously difficult to figure out from a corresponding genetic sequence alone. DNA contains only information about chains of amino acid residues and not those chains final form.
In December 2018, DeepMindattempted to tackle the challenge of protein folding with AlphaFold, the product of two years of work. The Alphabet subsidiary said at the time that AlphaFold could predict structures more precisely than prior solutions. Its successor, AlphaFold 2, announced in December 2020, improved on this to outgun competing protein-folding-predicting methods for a second time. In the results from the 14th Critical Assessment of Structure Prediction (CASP) assessment, AlphaFold 2 had average errors comparable to the width of an atom (or 0.1 of a nanometer), competitive with the results from experimental methods.
AlphaFold draws inspiration from the fields of biology, physics, and machine learning. It takes advantage of the fact that a folded protein can be thought of as a spatial graph, where amino acid residues (amino acids contained within a peptide or protein) are nodes and edges connect the residues in close proximity. AlphaFold leverages an AI algorithm that attempts to interpret the structure of this graph while reasoning over the implicit graph its building using evolutionarily related sequences, multiple sequence alignment, and a representation of amino acid residue pairs.
In the open source release, DeepMind says it significantly streamlined AlphaFold 2. Whereas the system took days of computing time to generate structures for some entries to CASP, the open source version is about 16 times faster. It can generate structures in minutes to hours, depending on the size of the protein.
DeepMind makes the case that AlphaFold, if further refined, could be applied to previously intractable problems in the field of protein folding, including those related to epidemiological efforts. Last year, the company predicted several protein structures of SARS-CoV-2, including ORF3a, whose makeup was formerly a mystery. At CASP14, DeepMind predicted the structure of another coronavirus protein, ORF8, that has since been confirmed by experimentalists.
Beyond aiding the pandemic response, DeepMind expects AlphaFold will be used to explore the hundreds of millions of proteins for which science currently lacks models. Since DNA specifies the amino acid sequences that comprise protein structures, advances in genomics have made it possible to read protein sequences from the natural world, with 180 million protein sequences and counting in the publicly available Universal Protein database. In contrast, given the experimental work needed to translate from sequence to structure, only around 170,000 protein structures are in the Protein Data Bank.
DeepMind says its committed to making AlphaFold available at scale and collaborating with partners to explore new frontiers, like how multiple proteins form complexes and interact with DNA, RNA, and small molecules. Earlier this year, the company announced a new partnership with the Geneva-based Drugs for Neglected Diseases initiative, a nonprofit pharmaceutical organization that hopes to use AlphaFold to identify compounds to treat conditions for which medications remain elusive.
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Self-driving data centres: Managing the transition from human-to-AI workload management – IT Brief Australia
Article by Infosys Australia and New Zealand vice president and regional head delivery & operations, Ashok Mysore.
Organisations have naturally accelerated their digital agendas as employees were forced to work remotely amid the pandemic.
Meanwhile the nature of work has continued to evolve, with data centre workloads having grown exponentially.
While data centre managers have always used conventional tools to react to shifts in workloads, they've never been able to forecast for change. This issue has come to the fore over the past 18 months as workload distribution has been increasingly subject to sudden change.
As a result, AI and automation have become powerful tools in workload management and an essential part of every CIO's strategy. Autonomous technologies help manage workloads within an enterprise's infrastructure in real-time by better identifying workload patterns, matching demands with data centre capacity, spotting anomalies, and predicting breakdowns and outages much earlier.
The ability to mitigate downtime and keep workload clusters up and running is crucial to maintaining efficient workload operations into the future. For example, Googles DeepMind AI system helped the company achieve a 15% reduction in energy consumption at some data centres, by using algorithms that manipulate computer servers and equipment such as cooling systems.
Infosys applied AI offers enterprises an integrated way to scale and future-proof their business by converging the powers of AI, analytics and cloud to deliver new business solutions.
Beyond improving overall operational efficiency, these technologies can help free the workforce from mundane tasks and create more time for creative thinking and tackling broader business issues.
How easily can your enterprise embrace AI-drive workload management?
It wont be long before all data centre managers are faced with the choice to embrace AI-driven reinvention for revenue growth. But how an organisation handles the transition from human-to-AI workload management depends on its technological maturity, scale of operations and its data centre's dynamism.
The Infosys Cloud Radar Report shows Australian enterprises have led the way in digital and cloud technology investment, but this is predicted to fall over the next few years.
Additionally, theFuture of Work study shows that approximately a third of global CEOs are concerned about the availability of critical skills amid the trend towards remote work. It also predicts that the nature of jobs themselves will change, requiring new skills and methods of attaining them
Where there are concerns about limited tech workplace talent, like in Australia, accelerating AI adoption and optimising workload management in data centres can contribute to more meaningful work.
Before leaders get comfortable handing essential business responsibilities to a piece of software, significant barriers need to be overcome to building robust and responsible AI-managed workload systems. For example, predictions made by the AI-powered workload management tool and its overall intent must be fully explainable to an enterprise's IT team, otherwise its scalability will be limited. Additionally, AI models are traditionally built for fixed and predictable environments; hence, testing a workload management model for data drift and bias is crucial to avoid blind spots.
It's encouraging to see more organisations looking for a comprehensive approach to scale enterprise-grade AI for their workload management. AIs powerful automation ability coupled with predictive insights for almost all workload operations, from maintenance and monitoring to data security, makes promise of its potential compelling.
For businesses who choose to scale automation and AI investments, new doors for meaningful work will be opened in the future.
I think all in all, getting the spectators here for us was really important, Slumbers explained on the eve of the Championship.
I've talked about what I think of The Open in terms of where I want it to be positioned as a world-class sporting event, and big-time sporting events need big-time crowds. We've worked really hard with government to do that.
We're very conscious of the environment that we're all operating in. There's very strict conditions for any of those spectators to be able to get into the grounds, and they're being held further back from the players than we would normally do. If you go out, you can see the ropes are further back.
But I think spectators play a massive part in sport; (and its) no different in the Open Championship.
When you wait and see what the 18th is like on Sunday afternoon when the winner is coming down, when the crowds are in the grandstand, that's what the Open is about for us.
A CHANCE FOR THE OUTSIDERS?
An extra layer of intrigue is provided by the identities of the last two men to be crowned Champion Golfer of the Year at Royal St Georges.
Clarke was ranked outside the worlds top 100 when he triumphed a decade ago, while Ben Curtis proved an even more unlikely victor in 2003, sensationally winning on his first major appearance when ranked 396th in the world.
That statistic alone will give hope to every player bidding to emulate Clarke, Curtis and a host of legendary names.
A special place in history awaits the winner of golfs original Championship. Royal St Georges is ready to provide the perfect stage.
Read more from the original source:
The scene is set | Royal St George's | The 149th Open - The Open
Business applications of cognitive computing are gaining popularity rapidly. Cognitive computing technology combines machine learning, reasoning, NLP, speech vision, and human-computer interaction in a way that mimics the human brain to improve decision-making. This AI-powered capability has the potential to transform several industries, right from sales forecasting, improving communications, supply chain operations, to better drug discovery, marketing, defense, fraud detection, financial sector, and agriculture.
Tech companies that have released these applications are working on preparing products and services to help clients put data to better work.
Aeseras AI Service Management Platform (AISM), helps customers and employees by optimizing processes for better productivity and slashed costs. The platform connects automated service experience with AI-based conversational engagement and workflow automation.
Accenture aims to leverage all of its clients and their processes with the companys unique approach to scaling AI, analytics, data, and automation. With applied intelligence, Accentures teams help organizations to invest in the right solutions and services that will suit their business goals.
AWSs machine learning services and supporting cloud infrastructure, enable every developer, data scientist, and expert practitioner to use machine learning capabilities. At present, AWS is helping more than a thousand clients accelerate their machine learning capabilities.
Alteryx, provides a platform that facilitates end-to-end analytics process automation. The company recently announced new products that innovatively deal with analytics and data science automation, analytics in the cloud, AI, and machine learning. These new launches focus on delivering a simple user experience with no-code, low-code approaches to leverage business outcomes.
C3 AI, provides enterprise AI software that accelerates digital transformation with fully integrated products like C3 AI Suite (an end-to-end platform for AI applications), C3 AI Applications (a bundle of industry-specific SaaS AI apps), C3 AI CRM (CRM applications for AI and ML), and C3 AI Machina, a no-code AI solution for everyday data science.
SparkCognition, provides three cognitive computing software for enterprises, SparkPredict, SparkSecure, and MindFabric. SparkPredict uses sophisticated algorithms to large pools of data with intelligence. SparkSecure Cognitive Insights add a cognitive layer to security solutions to improve threat detection, leverage IT abilities, and reduce the probability of false positives. MindFabric platform acts as a workspace for professionals for deep data-led insights.
Microsofts Cognitive Services boosts Microsofts machine learning APIs to help developers easily add intelligent features like emotion detection, voice recognition, and language understanding. With just a few lines of code, developers can build apps that can work across devices like iOS, Android, and Windows.
Expert System, provides software that is capable of working with language and technology to make sense out of unstructured content. Clients can extract insights and make human-level decisions with strengthened analytics. This software comprehends multiple languages, just like humans.
IBM Watson performs deep content analysis and uses evidence-based reasoning to leverage and improve decision making, reducing costs for better outcomes. For this, the software uses
A set of transformational technologies that use natural language, hypothesis generation, and evidence-based learning. Experts believe that Watson holds the power to transform the process of business problem solving as the system uses machine learning, statistical analysis, and NLP to find answers amidst the clues. Watson then compares the answers by ranking them based on confidence and accuracy.
Deepmind aims to solve intelligence-based business problems with the research. Deepmind uses real-world applications of AI technology to help industries like healthcare. It enables nurses, doctors, and support staff to quickly analyze test results, forms the right diagnosis and treatment, and escalate the case to a specialist. All these judgments can be made using the advanced technology of accurate analysis.
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