Sixty-five years ago, Arthur Samuel went on TV to show the world how the IBM 701 plays checkers. He was interviewed on a live morning news program, sitting remotely at the 701, with Will Rogers Jr. at the TV studio, together with a checkers expert who played with the computer for about an hour. Three years later, in 1959, Samuel published Some Studies in Machine Learning Using the Game of Checkers, in the IBM Journal of Research and Development, coining the term machine learning. He defined it as the programming of a digital computer to behave in a way which, if done by human beings or animals, would be described as involving the process of learning.

On February 24, 1956, Arthur Samuels Checkers program, which was developed for play on the IBM 701, ... [+] was demonstrated to the public on television

ADVERTISEMENT

A few months after Samuels TV appearance, ten computer scientists convened in Dartmouth, NH, for the first-ever workshop on artificial intelligence, defined a year earlier by John McCarthy in the proposal for the workshop as making a machine behave in ways that would be called intelligent if a human were so behaving.

In some circles of the emerging discipline of computer science, there was no doubt about the human-like nature of the machines they were creating. Already in 1949, computer pioneer Edmund Berkeley wrote inGiant Brains or Machines that Think: Recently there have been a good deal of news about strange giant machines that can handle information with vast speed and skill... These machines are similar to what a brain would be if it were made of hardware and wire instead of flesh and nerves A machine can handle information; it can calculate, conclude, and choose; it can perform reasonable operations with information. A machine, therefore, can think.

Maurice Wilkes, a prominent developer of one of those giant brains, retorted in 1953: Berkeley's definition of what is meant by a thinking machine appears to be so wide as to miss the essential point of interest in the question, Can machines think? Wilkes attributed this not-very-good human thinking to a desire to believe that a machine can be something more than a machine. In the same issue of the Proceeding of the I.R.E that included Wilkes article, Samuel published Computing Bit by Bit or Digital Computers Made Easy. Reacting to what he called the fuzzy sensationalism of the popular press regarding the ability of existing digital computers to think, he wrote: The digital computer can and does relieve man of much of the burdensome detail of numerical calculations and of related logical operations, but perhaps it is more a matter of definition than fact as to whether this constitutes thinking.

Samuels polite but clear position led Marvin Minsky in 1961 to single him out, according to Eric Weiss, as one of the few leaders in the field of artificial intelligence who believed computers could not think and probably never would. Indeed, he pursued his life-long hobby of developing checkers-playing computer programs and professional interest in machine learning not out of a desire to play God but because of the specific trajectory and coincidences of his career. After working for 18 years at Bell Telephone Laboratories and becoming an internationally recognized authority on microwave tubes, he decided at age 45 to move on, as he was certain, says Weiss in his review of Samuels life and work, that vacuum tubes soon will be replaced by something else.

The University of Illinois came calling, asking him to revitalize their EE graduate research program. In 1948, the project to build the Universitys first computer was running out of money. Samuel thought (as he recalled in an unpublished autobiography cited by Weiss) that it ought to be dead easy to program a computer to play checkers and that if their program could beat a checkers world champion, the attention it would generate will also generate the required funds.

The next year, Samuel started his 17-year tenure with IBM, working as a senior engineer on the team developing the IBM 701, IBMs first mass-produced scientific computer. The chief architect of the entire IBM 700 series was Nathaniel Rochester, later one of the participants in the Dartmouth AI workshop. Rochester was trying to decide the word length and order structure of the IBM 701 and Samuel decided to rewrite his checkers-playing program using the order structure that Rochester was proposing. In his autobiography, Samuel recalled that I was a bit fearful that everyone in IBM would consider checker-playing program too trivial a matter, so I decided that I would concentrate on the learning aspects of the program. Thus, more or less by accident, I became one of the first people to do any serious programing for the IBM 701 and certainly one of the very first to work in the general field later to become known as artificial intelligence. In fact, I became so intrigued with this general problem of writing a program that would appear to exhibit intelligence that it was to occupy my thoughts almost every free moment during the entire duration of my employment by IBM and indeed for some years beyond.

But in the early days of computing, IBM did not want to fan the popular fears that man was losing out to machines, so the company did not talk about artificial intelligence publicly, observed Samuel later. Salesmen were not supposed to scare customers with speculation about future computer accomplishments. So IBM, among other activities aimed at dispelling the notion that computers were smarter than humans, sponsored the movie Desk Set, featuring a methods engineer (Spencer Tracy) who installs the fictional and ominous-looking electronic brain EMERAC, and a corporate librarian (Katharine Hepburn) telling her anxious colleagues in the research department: They cant build a machine to do our jobthere are too many cross-references in this place. By the end of the movie, she wins both a match with the computer and the engineers heart.

ADVERTISEMENT

In his1959 paper, Samuel described his approach to machine learning as particularly suited for very specific tasks, in distinction to the Neural-Net approach, which he thought could lead to the development of general-purpose learning machines. Samuels program searched the computers memory to find examples of checkerboard positions and selected the moves that were previously successful. The computer plays by looking ahead a few moves and by evaluating the resulting board positions much as a human player might do, wrote Samuel.

His approach to machine learning still would work pretty well as a description of whats known as reinforcement learning, one of the basket of machine-learning techniques that has revitalized the field of artificial intelligence in recent years, wrote Alexis Madrigal in a 2017 survey of checkers-playing computer programs. One of the men who wrote the bookReinforcement Learning, Rich Sutton, called Samuels research the earliest work thats now viewed as directly relevant to the current AI enterprise.

The current AI enterprise is skewed more in favor of artificial neural networks (or deep learning) then reinforcement learning, although Googles DeepMind famously combined the two approaches in its Go-playing program which successfully beat Go master Lee Sedol in a five-game match in 2016.

ADVERTISEMENT

Already popular among computer scientists in Samuels time (in 1951, Marvin Minsky and Dean Edmunds built SNARCStochastic Neural Analog Reinforcement Calculatorthe first artificial neural network, using 3000 vacuum tubes to simulate a network of 40 neurons), the neural networks approach was inspired by a1943 paperby Warren S. McCulloch and Walter Pitts in which they described networks of idealized and simplified artificial neurons and how they might perform simple logical functions, leading to the popular (and very misleading) description of todays artificial neural networks-based AI as mimicking the brain.

Over the years, the popularity of neural networks have gone up and down a number of hype cycles, starting with thePerceptron, a 2-layer artificial neural network that was considered by the U.S. Navy, according to a 1958 New York Times report, to be "the embryo of an electronic computer that.. will be able to walk, talk, see, write, reproduce itself and be conscious of its existence." In addition to failing to meet these lofty expectations, neural networks suffered from a fierce competition from a growing cohort of computer scientists (including Minsky) who preferred the manipulation of symbols rather than computational statistics as the better path to creating a human-like machine.

Inflated expectations meeting the trough of disillusionment, no matter what approach was taken, resulted in at least two periods of gloomy AI Winter. But with the invention and successful application of backpropagation as a way to overcome the limitations of simple neural networks, sophisticated statistical analysis was againon the ascendance, now cleverly labeled as deep learning. In 1988, R. Colin Johnson and Chappell Brown published Cognizers: Neural Networks and Machine That Think, proclaiming that neural networks can actually learn to recognize objects and understand speech just like the human brain and, best of all, they wont need the rules, programming, or high-priced knowledge-engineering services that conventional artificial intelligence systems requireCognizers could very well revolutionize our society and will inevitably lead to a new understanding of our own cognition.

ADVERTISEMENT

Johnson and Brown predicted that as early as the next two years, neural networks will be the tool of choice for analyzing the contents of a large database. This predictionand no doubt similar ones in the popular press and professional journalsmust have sounded the alarm among those who did this type of analysis for a living in academia and in large corporations, having no clue of what the computer scientists were talking about.

InNeural Networks and Statistical Models, Warren Sarle explained in 1994 to his worried and confused fellow statisticians that the ominous-sounding artificial neural networks are nothing more than nonlinear regression and discriminant models that can be implemented with standard statistical software like many statistical methods, [artificial neural networks] are capable of processing vast amounts of data and making predictions that are sometimes surprisingly accurate; this does not make them intelligent in the usual sense of the word. Artificial neural networks learn in much the same way that many statistical algorithms do estimation, but usually much more slowly than statistical algorithms. If artificial neural networks are intelligent, then many statistical methods must also be considered intelligent.

Sarle provided his colleagues with a handy dictionary translating the terms used by neural engineers to the language of statisticians (e.g., features are variables). In anticipation of todays data science (a more recent assault led by computer programmers) and predictions of algorithms replacing statisticians (and even scientists), Sarle reassured his fellow statisticians that no black box can substitute for human intelligence: Neural engineers want their networks to be black boxes requiring no human interventiondata in, predictions out. The marketing hype claims that neural networks can be used with no experience and automatically learn whatever is required; this, of course, is nonsense. Doing a simple linear regression requires a nontrivial amount of statistical expertise.

ADVERTISEMENT

In a footnote to his mention of neural networks in his 1959 paper, Samuel cited Warren S. McCulloch who has compared the digital computer to the nervous system of a flatworm, and declared: To extend this comparison to the situation under discussion would be unfair to the worm since its nervous system is actually quite highly organized as compared to [the most advanced artificial neural networks of the day]. In 2019, Facebooks top AI researcher and Turing Award-winner Yann LeCun declared that Our best AI systems have less common sense than a house cat. In the sixty years since Samuel first published his seminal machine learning work, artificial intelligence has advanced from being not as smart as a flatworm to having less common sense than a house cat.

Read the original post:
On Thinking Machines, Machine Learning, And How AI Took Over Statistics - Forbes

Machine learning is a key aspect of Artificial Intelligence. However, one area that has always been an issue to worry about is adversarial attacks. It is because of this that the models trained to work in a particular way fail to do so and act in undesired ways.

Computer vision is one of those areas that has grabbed eyeballs from everywhere around. This is the area where the AI systems deployed aid in processing the visual data. What attackers do here is add a layer of noise to the images. This further makes matters worse as adding noise leads in misclassification. A defence method against such an adversarial attack is randomized smoothing. This is a method wherein the machine learning systems become resilient against imperceptible perturbations.

However, it has been observed that despite randomized smoothing, machine learning systems could fail. Here are some aspects of Adversarial machine learning.

This makes into the list of the most common techniques to target the data that forms the base of training the models. Data poisoning is where corrupt data is inserted into the dataset. Doing so, training the machine learning model is compromised. In some cases, such techniques are seen to trigger a specific undesirable behaviour in a computer vision system whereas in some it has been observed that the accuracy of a machine learning model is reduced drastically. What is an area of concern here is that it becomes virtually impossible to detect all these attacks because their modifications are not visible to the human eye.

It has been established that minimizing the empirical error wouldnt be that effective as the models are still vulnerable to adversarial attacks. Random smoothing is a technique that serves to be useful here. The technique works by cancelling out the effects of data poisoning by establishing an average certified radius (ACR) during the training of a machine learning model. Now here is the catch when a trained computer vision model classifies an image correctly, then adversarial perturbations within the certified radius will not affect its accuracy. However, larger the ACR, it becomes a little difficult to make the adversarial noise visible to the human eye.

This is yet another research paper wherein the researchers came up with a new data poisoning method called Poisoning Against Certified Defenses (PACD). This method employs bi-level optimization, a technique that has two major objectives to serve: one, to create poisoned data for models that have undergone robustness training, and the other to pass the certification procedure. This process takes a set of clean training examples and gradually adds noise to them until they reach a level that can circumvent the target training technique. When the target model is trained on the tainted dataset, its ACR is reduced drastically. Using PACD, the researchers have been successful in producing clean adversarial examples. Simply put, the perturbations are not visible to the human eye.

The researchers wanted to check whether a poisoned dataset targeted at one adversarial training technique would prove to be effective against others. On that note, it was found that PACD transfers across different training techniques.

The future

Adversarial attacks are presenting new challenges for the cybersecurity community. Hence, the coming years would see a lot of challenges in this area. Though PACD is effective, sound knowledge of the target machine learning model before formulating the poisoned data is the need of the hour. Yet another area of concern is the cost of producing the poisoned dataset. If all of this is taken into account, the future can see promising results for sure.

Read the original here:
Everything You Need to Know About Adversarial Machine Learning - Analytics Insight

Introduction: Global Machine Learning as a Service Market, 2020-28The research report on global Machine Learning as a Service market provides insightful data about market and all the important aspects related to it. The pattern in the Machine Learning as a Service industry gives an absolute overview of prime players by the weightlessness of their product definition, company summary, and business strategy at intervals in the market. a comprehensive analysis of the market performance throughout the years is offered in the research report. This analysis helps vendors and manufacturers to understand the change in the market dynamics over the years. In addition to that the research report also covers detailed analysis of all the crucial factors having an impact on the market growth. The detailed study of all the crucial aspects of the Machine Learning as a Service market is included in the market report such as market share, production, regions, key players, etc.

The study encompasses profiles of major companies operating in the Machine Learning as a Service MarketAmazon Web Services, Inc., BigML, Inc., Crunchbase Inc., Fair Isaac Corporation., Google LLC, H2O.ai., IBM, Microsoft Corporation, PREDICTRON LABS, and Yottamine Analytics, LLC.FPNV Positioning Matrix:The FPNV Positioning Matrix evaluates and categorizes the vendors in the Machine Learning as a Service Market on the basis of Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) that aids businesses in better decision making and understanding the competitive landscape.Competitive Strategic Window:The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies. The Competitive Strategic Window helps the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. During a forecast period, it defines the optimal or favorable fit for the vendors to adopt successive merger and acquisition strategies, geography expansion, research & development, and new product introduction strategies to execute further business expansion and growth.Cumulative

Available Sample Report in PDF Version along with Graphs and [emailprotected] https://www.orbisresearch.com/contacts/request-sample/5843973?utm_source=PoojaA3

Global Machine Learning as a Service Market: Understanding ScopeThe comprehensive analysis of potential customer base, market values and future scope is included in the global Machine Learning as a Service market report. Along with that the research report on the global market holds all the vital information regarding the latest technologies and trends being adopted or followed by the vendors across the globe. The detailed study offers an important microscopic view of the industry to define manufacturers footprints by awareness of manufacturers worldwide sales and costs, and manufacturers production over the forecast era. Leading and influential players in the global Machine Learning as a Service market are narrowly analyzed on the basis of key factors in the competition analysis portion of the study. The study includes a detailed overview and reliable athlete sales estimates for the forecasted timeframe. The analysis also offers methodical references to the prevailing developments in business dynamics.

By the product type, the market is primarily split into: by Component (Services and Software),

By the end-users/application, this report covers the following segments: (Augmented & Virtual Reality, Fraud Detection & Risk Management, Marketing & Advertising, Predictive Analytics, and Security & Surveillance), by End

In addition, the study report also provides full documentation of past, present and future projections related to market size and volume. The study further presents the industrys leading and dominant business leaders with best practices and growth-friendly measures. The research also includes SWOT analysis for the global Machine Learning as a Service industry, PESTEL analysis and Potters Five Forces analysis. A competitive analysis of the Machine Learning as a Service industry and main product segments of the market is given in the study. The research report also offers the detailed analysis of performances of all the regions across the globe in market terms. The Machine Learning as a Service market report takes a detailed note on the major industrial events in past years. These events include several operational business decisions, innovations, mergers, collaborations, major investments, etc. The research report provides a 360 degree view of the global Machine Learning as a Service market.

Read complete report along with TOC @ https://www.orbisresearch.com/reports/index/machine-learning-as-a-service-market-outlook-to-2028-key-trends-covid-19-impact-growth-opportunities-and-segment-analysis?utm_source=PoojaA3

The key regions covered in the Machine Learning as a Service market report are:North America (U.S., Canada, Mexico)South America (Cuba, Brazil, Argentina, and many others.)Europe (Germany, U.K., France, Italy, Russia, Spain, etc.)Asia (China, India, Russia, and many other Asian nations.)Pacific region (Indonesia, Japan, and many other Pacific nations.)Middle East & Africa (Saudi Arabia, South Africa, and many others.)

The study objectives of this report are:

To analyze global Machine Learning as a Service status, future forecast, growth opportunity, key market and key players. To present the Machine Learning as a Service development in North America, Europe, China, Japan, Southeast Asia, India and Central & South America. To strategically profile the key players and comprehensively analyze their development plan and strategies. To define, describe and forecast the market by type, market and key regions.

In this study, the years considered to estimate the market size of Machine Learning as a Service are as follows:History Year: 2015-2019Base Year: 2019Estimated Year: 2020Forecast Year 2020 to 2026

For the data information by region, company, type and application, 2019 is considered as the base year. Whenever data information was unavailable for the base year, the prior year has been considered.

Table of Contents Chapter One: Report Overview 1.1 Study Scope1.2 Key Market Segments1.3 Players Covered: Ranking by Machine Learning as a Service Revenue1.4 Market Analysis by Type1.4.1 Global Machine Learning as a Service Market Size Growth Rate by Type: 2020 VS 20281.5 Market by Application1.5.1 Global Machine Learning as a Service Market Share by Application: 2020 VS 20281.6 Study Objectives1.7 Years Considered

Chapter Two: Global Growth Trends by Regions 2.1 Machine Learning as a Service Market Perspective (2015-2028)2.2 Machine Learning as a Service Growth Trends by Regions2.2.1 Machine Learning as a Service Market Size by Regions: 2015 VS 2020 VS 20282.2.2 Machine Learning as a Service Historic Market Share by Regions (2015-2020)2.2.3 Machine Learning as a Service Forecasted Market Size by Regions (2021-2028)2.3 Industry Trends and Growth Strategy2.3.1 Market Top Trends2.3.2 Market Drivers2.3.3 Market Challenges2.3.4 Porters Five Forces Analysis2.3.5 Machine Learning as a Service Market Growth Strategy2.3.6 Primary Interviews with Key Machine Learning as a Service Players (Opinion Leaders)

Chapter Three: Competition Landscape by Key Players 3.1 Global Top Machine Learning as a Service Players by Market Size3.1.1 Global Top Machine Learning as a Service Players by Revenue (2015-2020)3.1.2 Global Machine Learning as a Service Revenue Market Share by Players (2015-2020)3.1.3 Global Machine Learning as a Service Market Share by Company Type (Tier 1, Tier Chapter Two: and Tier 3)3.2 Global Machine Learning as a Service Market Concentration Ratio3.2.1 Global Machine Learning as a Service Market Concentration Ratio (CRChapter Five: and HHI)3.2.2 Global Top Chapter Ten: and Top 5 Companies by Machine Learning as a Service Revenue in 20203.3 Machine Learning as a Service Key Players Head office and Area Served3.4 Key Players Machine Learning as a Service Product Solution and Service3.5 Date of Enter into Machine Learning as a Service Market3.6 Mergers & Acquisitions, Expansion Plans

Place Inquiry for Buying or Customization of Report @ https://www.orbisresearch.com/contacts/enquiry-before-buying/5843973?utm_source=PoojaA3

About Us:Orbis Research (orbisresearch.com) is a single point aid for all your market research requirements. We have vast database of reports from the leading publishers and authors across the globe. We specialize in delivering customized reports as per the requirements of our clients. We have complete information about our publishers and hence are sure about the accuracy of the industries and verticals of their specialization. This helps our clients to map their needs and we produce the perfect required market research study for our clients.

Contact Us:Hector CostelloSenior Manager Client Engagements4144N Central Expressway,Suite 600, Dallas,Texas 75204, U.S.A.Phone No.: USA: +1 (972)-362-8199 | IND: +91 895 659 5155

https://neighborwebsj.com/

Read more here:
Machine Learning as a Service Market Key Players Change the View of the Global Face of Industry by 2028: Amazon Web Services, Inc., BigML, Inc.,...

Lee Sedol, a world-class Go Champion, was flummoxed by the 37th move Deepminds AlphaGo made in the second match of the famous 2016 series. So flummoxed that it took him nearly 15 minutes to formulate a response. The move was strange to other experienced Go players as well, with one commentator suggesting it was a mistake. In fact, it was a canonical example of an artificial intelligence algorithm learning something that seemed to go beyond just pattern recognition in data learning something strategic and even creative. Indeed, beyond just feeding the algorithm past examples of Go champions playing games, Deepmind developers trained AlphaGo by having it play many millions of matches against itself. During these matches, the system had the chance to explore new moves and strategies, and then evaluate if they improved performance. Through all this trial and error, it discovered a way to play the game that surprised even the best players in the world.

If this kind of AI with creative capabilities seems different than the chatbots and predictive models most businesses end up with when they apply machine learning, thats because it is. Instead of machine learning that uses historical data to generate predictions, game-playing systems like AlphaGo use reinforcement learning a mature machine learning technology thats good at optimizing tasks. To do so, an agent takes a series of actions over time, and each action is informed by the outcome of the previous ones. Put simply, it works by trying different approaches and latching onto reinforcing the ones that seem to work better than the others. With enough trials, you can reinforce your way to beating your current best approach and discover a new best way to accomplish your task.

Despite its demonstrated usefulness, however, reinforcement learning is mostly used in academia and niche areas like video games and robotics. Companies such as Netflix, Spotify, and Google have started using it, but most businesses lag behind. Yet opportunities are everywhere. In fact, any time you have to make decisions in sequence what AI practitioners call sequential decision tasks there a chance to deploy reinforcement learning.

Consider the many real-world problems that require deciding how to act over time, where there is something to maximize (or minimize), and where youre never explicitly given the correct solution. For example:

If youre a company leader, there are likely many processes youd like to automate or optimize, but that are too dynamic or have too many exceptions and edge cases, to program into software. Through trial and error, reinforcement learning algorithms can learn to solve even the most dynamic optimization problems opening up new avenues for automation and personalization in quickly changing environments.

Many businesses think of machine learning systems as prediction machines and apply algorithms to forecast things like cash flow or customer attrition based on data such as transaction patterns or website analytics behavior. These systems tend to use whats called supervised machine learning. With supervised learning, you typically make a prediction: the stock will likely go up by four points in the next six hours. Then, after you make that prediction, youre given the actual answer: the stock actually went up by three points. The system learns by updating its mapping between input data like past prices of the same stock and perhaps of other equities and indicators and output prediction to better match the actual answer, which is called the ground truth.

With reinforcement learning, however, theres no correct answer to learn from. Reinforcement learning systems produce actions, not predictions theyll suggest the action most likely to maximize (or minimize) a metric. You can only observe how well you did on a particular task and whether it was done faster or more efficiently than before. Because these systems learn through trial and error, they work best when they can rapidly try an action (or sequence of actions) and get feedback a stock market algorithm that takes hundreds of actions per day is a good use case; optimizing customer lifetime value over the course of five years, with only irregular interaction points, is not. Significantly, because of how they learn, they dont need mountains of historical data theyll experiment and create their own data along the way.

They can therefore be used to automate a process, like placing items into a shipping container with a robotic arm; or to optimize a process, like deciding when and through what channel to contact a client who missed a payment, with the highest recouped revenue and lowest expended effort. In either case, designing the inputs, actions, and rewards the system uses is the key it will optimize exactly what you encode it to optimize and doesnt do well with any ambiguity.

Googles use of reinforcement learning to help cool its data centers is a good example of how this technology can be applied. Servers in data centers generate a lot of heat, especially when theyre in close proximity to one another, and overheating can lead to IT performance issues or equipment damage. In this use case, the input data is various measurements about the environment, like air pressure and temperature. The actions are fan speed (which controls air flow) and valve opening (the amount of water used) in air-handling units. The system includes some rules to follow safe operating guidelines, and it sequences how air flows through the center to keep the temperature at a specified level while minimizing energy usage. The physical dynamics of a data center environment are complex and constantly changing; a shift in the weather impacts temperature and humidity, and each physical location often has a unique architecture and set up. Reinforcement learning algorithms are able to pick up on nuances that would be too hard to describe with formulas and rules.

Here at Borealis AI, we partnered with Royal Bank of Canadas Capital Markets business to develop a reinforcement learning-based trade execution system called Aiden. Aidens objective is to execute a customers stock order (to buy or sell a certain number of shares) within a specified time window, seeking prices that minimize loss relative to a specified benchmark. This becomes a sequential decision task because of the detrimental market impact of buying or selling too many shares at once: the task is to sequence actions throughout the day to minimize price impact.

The stock market is dynamic and the performance traditional algorithms (the rules-based algorithms traders have used for years) can vary when todays market conditions differ from yesterdays. We felt this was a good reinforcement learning opportunity it had the right balance between clarity and dynamic complexity. We could clearly enumerate the different actions Aiden could take, and the reward we wanted to optimize (minimize the difference between the prices Aiden achieved and the market volume-weighted average price benchmark). The stock market moves fast and generates a lot of data, giving the algorithm quick iterations to learn.

We let the algorithm do just that through countless simulations before launching the system live to the market. Ultimately, Aiden proved able to perform well during some of the more volatile market periods during the beginning of the Covid-19 pandemic conditions that are particularly tough for predictive AIs. It was able to adapt to the changing environment, while continuing to stay close to its benchmark target.

How can you tell if youre overlooking a problem that reinforcement learning might be able to fix? Heres where to start:

Create an inventory of business processes that involve a sequence of steps and clearly state what you want to maximize or minimize. Focus on processes with dense, frequent actions and opportunities for feedback and avoid processes with infrequent actions and where its difficult to observe which worked best to collect feedback. Getting the objective right will likely require iteration.

Dont start with reinforcement learning if you can tackle a problem with other machine learning or optimization techniques. Reinforcement learning is helpful when you lack sufficient historical data to train an algorithm. You need to explore options (and create data along the way).

If you do want to move ahead, domain experts should closely collaborate with technical teams to help design the inputs, actions, and rewards. For inputs, seek the smallest set of information you could use to make a good decision. For actions, ask how much flexibility you want to give the system; start simple and later expand the range of actions. For rewards, think carefully about the outcomes and be careful to avoid falling into the traps of considering one variable in isolation or opting for short-term gains with long-term pains.

Will the possible gains justify the costs for development? Many companies need to make digital transformation investments to have the systems and dense, data-generating business processes in place to really make reinforcement learning systems useful. To answer whether the investment will pay off, technical teams should take stock of computational resources to ensure you have the compute power required to support trials and allow the system to explore and identify the optimal sequence. (They may want to create a simulation environment to test the algorithm before releasing it live.) On the software front, if youre planning to use a learning system for customer engagement, you need to have a system that can support A/B testing. This is critical to the learning process, as the algorithm needs to explore different options before it can latch onto which one works best. Finally, if your technology stack can only release features universally, you need likely to upgrade before you start optimizing.

And last but not least, as with many learning algorithms, you have to be open to errors early on while the system learns. It wont find the optimal path from day one, but it will get there in time and potentially find surprising, creative solutions beyond human imagination when it does.

While reinforcement learning is a mature technology, its only now starting to be applied in business settings. The technology shines when used to automate or optimize business processes that generate dense data, and where there could be unanticipated changes you couldnt capture with formulas or rules. If you can spot an opportunity, and either lean on an in-house technical team or partner with experts in the space, theres a window to apply this technology to outpace your competition.

Read more:
Why AI That Teaches Itself to Achieve a Goal Is the Next Big Thing - Harvard Business Review

DataIntelo has Published a brand-new market research study on the international Machine Learning in Finance Market. This industry report incorporates comprehensive market analysis about the chances that has emerged as a result of this COVID-19 pandemic. Whats more, it gives key insights about the creative approaches which are used by leading business players amidst the pandemic.

Major Players Covered in the Report:

Ignite LtdYodleeTrill A.I.MindTitanAccentureZestFinance

Get Free Sample Report: https://dataintelo.com/request-sample/?reportId=201715

The report covers the market drivers, restraints, threats, opportunities, and challenges which are anticipated to modify the dynamics of this market throughout the forecast period, 2021-2028. These afore-mentioned important parameters are expected to assist the reader make critical business decisions readily. The Machine Learning in Finance market research report offers information regarding the drivers, restraints, chances, pricing variables & tendencies, gains, revenue generation, and emerging trends of this market.

5 Crucial Insights That Are Covered in the Machine Learning in Finance Market Report

The global Machine Learning in Finance market is segmented on the basis of:

Products

Supervised LearningUnsupervised LearningSemi Supervised LearningReinforced Leaning

Application

BanksSecurities CompanyOthers

Regions

North America

Europe

Asia Pacific

Latin America

Middle East & Africa

Buy the complete report https://dataintelo.com/checkout/?reportId=201715

The Market Research report comprises revenue share, potential growth opportunities, and theorized growth rate of this market in above areas. DataIntelo has contemplated social-economic variables of the nations in the area to examine the regional market. Whats more, it has included the effect of the COVID-19 outbreak on the area and commerce regulations and government policies & policies which shapes the rise of the market in these areas.

Below is the TOC of the report:

Executive Summary

Assumptions and Acronyms Used

Research Methodology

Machine Learning in Finance Market Overview

Global Machine Learning in Finance Market Analysis and Forecast by Type

Global Machine Learning in Finance Market Analysis and Forecast by Application

Global Machine Learning in Finance Market Analysis and Forecast by Sales Channel

Global Machine Learning in Finance Market Analysis and Forecast by Region

North America Machine Learning in Finance Market Analysis and Forecast

Latin America Machine Learning in Finance Market Analysis and Forecast

Europe Machine Learning in Finance Market Analysis and Forecast

Asia Pacific Machine Learning in Finance Market Analysis and Forecast

Asia Pacific Machine Learning in Finance Market Size and Volume Forecast by Application

Middle East & Africa Machine Learning in Finance Market Analysis and Forecast

Competition Landscape

Why to Choose DataIntelo?

The companys research team has been constantly monitoring the Machine Learning in Finance market since few years, which has helped them to include actionable insights that can confer the esteemed reader with the leverage to grow their enterprise with a high CAGR and gain stellar ROI in the market.

Many regions are observing the second wave of the COVID-19 pandemic that has persuaded industry players to reanalyse their decisions and deploy strategies for the new normal. The research team has conducted interviews with the industry experts and top-executives amidst the pandemic to get in-depth insights of the market in a detailed manner. They have used Porters Five Analysis and implemented robust methodology to understand the complex nature of the global Machine Learning in Finance market.

The team provides quarterly updates of the market, that includes products latest developments, strategies implemented by top players, and latest trends of the market. Additionally, the research team can customize the report in accordance to the requirements.

Request for Exclusive Discount: https://dataintelo.com/ask-for-discount/?reportId=201715

DataIntelo Has extensive knowledge in the development of tailored market research reports in many industry verticals. We ensure in-depth market evaluation including producing creative business plans for the new entrants and also the emerging players of this marketplace. Our firm offers market hazard evaluation, market opportunity analysis, and profound insights into the present and market situation.

To supply the utmost quality of the research report, we spend in analysts which hold stellar Expertise in the company domain and also have excellent analytical and Communication abilities. Our committed staff undergo quarterly training that Helps them to admit the most recent industry practices and also to serve the Customers together with the foremost customer experience.

Contact Info:

Name: Alex Mathews

Address: 500 East E Street, Ontario,

CA 91764, United States.

Phone No: USA: +1 909 414 1393

Email:[emailprotected]

Website:https://dataintelo.com

https://neighborwebsj.com/

Read the original:
Machine Learning in Finance Market 2021 Industry Size, Share, Growth and Top Companies Analysis- Ignite Ltd, Yodlee, Trill AI, MindTitan, Accenture,...

The Machine Learning Market study is a perfect mix of qualitative and quantitative information and to get better understanding on how stats relates to growth, market sizing and share, the study is started with market overview and further detailed commentary is showcased on changing market dynamics that includes Influencing trends by regions, growth drivers, long term opportunities and short term challenges that industry players are facing. Furthermore, Market Factor Analysis gives insights on how various regulatory affairs, economic factors and policy action are factored in the past and future growth scenarios by various business segments and applications. The Competitive Landscape provides detailed company profiling of players and draws attention on development activities, SWOT, financial outlook and major business strategic action taken by players.

Industries and markets are ever-evolving; navigate these changes with ongoing research conducted by Adroit Market Research; Address the latest insights released on Global Machine Learning Market.

Relevant features of the study that are being offered with major highlights from the report:

1) Can Market be broken down by different set of application and types?

Additional segmentation / Market breakdown is possible subject to data availability, feasibility and depending upon timeline and toughness of survey. However a detailed requirement needs to be prepared before making any final confirmation.

** An additional country of your interest can be included at no added cost feasibility test would be conducted by Analyst team of ADROIT MARKET RESEARCH based on the requirement shared and accordingly deliverable time will also be disclosed.

2) How Study Have Considered the Impact of Economic Slowdown of 2020?

Analyst at Adroit Market Research have conducted special survey and have connected with opinion leaders and Industry experts from various region to minutely understand impact on growth as well as local reforms to fight the situation. A special chapter in the study presents Impact and Market factor Analysis on Global Machine Learning Market along with tables and graphs related to various country and segments showcasing impact on growth trends.

3) Which companies are profiled in current version of the report? Can list of players be customize based on regional geographies we are targeting

Considering heat map analysis and based on market buzz or voice the profiled list of companies in the report are Microsoft Corporation (Washington, US), IBM Corporation (New York, US), SAP SE (Walldorf, Germany), SAS Institute Inc. (North Carolina, US), Google, Inc. (California, US), Amazon Web Services Inc. (Washington, US), Baidu, Inc. (Beijing, China), BigML, Inc. (Oregon, US), Fair Isaac Corporation (FICO) (California, US), Hewlett Packard Enterprise Development LP (HPE) (California, US), Intel Corporation (California, US), KNIME.com AG (Zurich, Switzerland), RapidMiner, Inc. (Massachusetts, US), Angoss Software Corporation (Toronto, Canada), H2O.ai (California, US), Alpine Data (California, US), Domino Data Lab, Inc. (California, US), Dataiku (Paris, France), Luminoso Technologies, Inc. (Massachusetts, US), TrademarkVision (Pennsylvania, US), Fractal Analytics Inc. (New Jersey, US), TIBCO Software Inc. (California, US), Teradata (Ohio, US), Dell Inc. (Texas, US), and Oracle Corporation (California, US). Yes, further list of players can also be customized as per your requirement keeping in mind your areas of interest and adding local emerging players and leaders from targeted geography.

** List of companies covered may vary in the final report subject to Name Change / Merger & Acquisition Activity etc. based on the difficulty of survey since data availability needs to be confirmed by research team especially in case of privately held company. Up to 2 players can be added at no additional cost.

To comprehend Global Machine Learning market dynamics in the global market, the worldwide Machine Learning market is analyzed across major geographical regions. Adroit Market Research Market Intelligence also provides customized specific regional and country-level reports, see below break-ups.

North America: United States, Canada, and Mexico.

South & Central America: Argentina, Chile, LATAM, and Brazil.

Middle East & Africa: Saudi Arabia, UAE, Israel, Turkey, Egypt and South Africa.

Europe: UK, France, Italy, Germany, Spain, BeNeLux, and Russia.

Asia-Pacific: India, China, Japan, South Korea, Indonesia, Thailand, Singapore, and Australia.

2-Page company profiles for 10+ leading players is included with 3 years financial history to illustrate the recent performance of the market. Latest and updated discussion for 2019 major macro and micro elements influencing market and impacting the sector are also provided with a thought-provoking qualitative remarks on future opportunities and likely threats. The study is a mix of both statistically relevant quantitative data from the industry, coupled with insigAdroit Market Researchul qualitative comment and analysis from Industry experts and consultants.

Global Machine Learning Product Types In-Depth: by ServiceProfessional ServicesManaged ServicesMachine learning market by Deployment Model:CloudOn-premises

Global Machine Learning Major Applications/End users:by Organization Size:SMEsLarge Enterprises

Market Sizing by Geographical Break-down: North America (Covered in Chapter 9), United States, Canada, Mexico, Europe (Covered in Chapter 10), Germany, UK, France, Italy, Spain, Russia, Others, Asia-Pacific (Covered in Chapter 11), China, Japan, South Korea, Australia, India, South America (Covered in Chapter 12), Brazil, Argentina, Columbia, Middle East and Africa (Covered in Chapter 13), UAE, Egypt & South Africa

To ascertain a deeper view of Market Size, competitive landscape is provided i.e. Comparative Market Share Revenue Analysis (Million USD) by Players (2018-2019) & Segment Market Share (%) by Players (2018-2019) and further a qualitative analysis of all players is made to understand market concentration rate.

Competitive Landscape & Analysis:

Major players of Machine Learning Market are focusing highly on innovation in new technologies to improve production efficiency and re-arrange product lifecycle. Long-term growth opportunities for this sector are captured by ensuring ongoing process improvements of related players following NAICS standard by understanding their financial flexibility to invest in the optimal strategies. Company profile section of players such as Microsoft Corporation (Washington, US), IBM Corporation (New York, US), SAP SE (Walldorf, Germany), SAS Institute Inc. (North Carolina, US), Google, Inc. (California, US), Amazon Web Services Inc. (Washington, US), Baidu, Inc. (Beijing, China), BigML, Inc. (Oregon, US), Fair Isaac Corporation (FICO) (California, US), Hewlett Packard Enterprise Development LP (HPE) (California, US), Intel Corporation (California, US), KNIME.com AG (Zurich, Switzerland), RapidMiner, Inc. (Massachusetts, US), Angoss Software Corporation (Toronto, Canada), H2O.ai (California, US), Alpine Data (California, US), Domino Data Lab, Inc. (California, US), Dataiku (Paris, France), Luminoso Technologies, Inc. (Massachusetts, US), TrademarkVision (Pennsylvania, US), Fractal Analytics Inc. (New Jersey, US), TIBCO Software Inc. (California, US), Teradata (Ohio, US), Dell Inc. (Texas, US), and Oracle Corporation (California, US)includes vital information like legal name, website, headquarter, its market position, distribution and marketing channels, historical background and top 4 closest competitors by Market capitalization / turnover along with sales contact information. Each company / manufacturers revenue figures, growth rate, net profit and gross profit margin is provided in easy to understand tabular format for past 3 years and a separate section on market entropy covering recent development activities like mergers &acquisition, new product/service launch, funding activity etc.

In this study, the years considered to estimate the market size of Global Machine Learning are as follows:

History Year: 2014-2019, Base Year: 2019, Forecast Year 2020 to 2025

Key Stakeholders / Target Audience Covered:

In order to better analyze value chain/ supply chain of the Industry, a lot of attention given to backward & forward Integration

Machine Learning Manufacturers

Machine Learning Distributors/Traders/Wholesalers

Machine Learning Sub-component Manufacturers

Industry Association

Downstream Vendors

Actual Numbers & In-Depth Analysis of Machine Learning Market Size Estimation, Business opportunities, Available in Full Report.

Thanks for reading this article, you can also get individual chapter wise section or region wise report version like North America, LATAM, West Europe, MENA Countries, Southeast Asia or Asia Pacific.

About Us

Adroit Market Research is an India-based business analytics and consulting company incorporated in 2018. Our target audience is a wide range of corporations, manufacturing companies, product/technology development institutions and industry associations that require understanding of a markets size, key trends, participants and future outlook of an industry. We intend to become our clients knowledge partner and provide them with valuable market insights to help create opportunities that increase their revenues. We follow a code Explore, Learn and Transform. At our core, we are curious people who love to identify and understand industry patterns, create an insightful study around our findings and churn out money-making roadmaps.

Read the original here:
Machine Learning Forecast Machine Learning Markets Trying to Break Out by Microsoft Corporation (Washington, US), IBM Corporation (New York, US), SAP...

A fresh report on Worldwide Machine Learning in Education Market 2021 quotes a critical analysis for its business on a regional and international level. It clarifies how companies procurement expenditure, Machine Learning in Education business plans, media speculate, marketing/sales, practices, and Machine Learning in Education company plan are set to alter in 2021. The report permits you to examine different Machine Learning in Education market predictions together with challenges, provider selection standards, the present Machine Learning in Education market size and investment opportunities, and advertising budgets of senior-level officials. The report also determines the anticipated Machine Learning in Education expansion of buyers and suppliers combined with funds spending and e-procurement. The global Machine Learning in Education marketplace report not only assesses perspectives and strategies of Machine Learning in Education company decision-makers and rivals but investigates their activities circling company priorities. Additionally, the Machine Learning in Education report offers accessibility to data categorized by business type and dimensions, area.

Request a sample report : https://www.orbisreports.com/global-machine-learning-in-education-market/?tab=reqform

In contrast to the present market situation, the net Machine Learning in Education business report shows various facts connected with driving variables, trends, opportunities, limitations, together with major Machine Learning in Education challenges encountered by business players. The net Machine Learning in Education marketplace account has considered each the major along with minor facets concerning the maturation of the Machine Learning in Education marketplace.

IBMMicrosoftGoogleAmazonCognizanPearsonBridge-UDreamBox LearningFishtreeJellynoteQuantum Adaptive Learning

Listing offers a particular evaluation of the Machine Learning in Education marketplace by assessing the changing competitive aspects of the global marketplace. It delivers a particular evaluation of the net Machine Learning in Education market so you believe you ought to always get the greener grass in the side. This analysis further includes the effect of the coronavirus on leading companies within the Machine Learning in Education marketplace and gives a whole evaluation of COVID-19 effect investigation from the market by type, program, and areas for example (Americas, APAC, and also EMEA).

Fundamental Machine Learning in Education information because of the institutions, for example, market volume, percentage share, carrier information, product pictures are also exhibited. The Level of the Worldwide Machine Learning in Education Market list is Based on the following:

To study and forecast the Machine Learning in Education marketplace step and provides for esteem and volume. Evaluation of Machine Learning in Education compounds sources and numbers of how downstream buyers are given. To dissect present and future risks and replacement hazard with this Machine Learning in Education report provides better esteem to your client demands and their changing inclinations together with monetary/political ecological change. Inclining Machine Learning in Education marketplace numbers, respect, utilization, costs, as well as the cost is offered by places, by forms, by producers, and from applications till the forecast season 2027.

Form Analysis of the Machine Learning in Education market:

Cloud-BasedOn-Premise

Application Assessment of the Machine Learning in Education market:

Intelligent Tutoring SystemsVirtual FacilitatorsContent Delivery SystemsInteractive WebsitesOthers

The Machine Learning in Education study report profound evaluation, providing an extensive analysis of global marketplace prognosis, review, utilization, and measurement of the entire sector by diverse geological places. The Machine Learning in Education report has been produce through key heights of study with respect to the business. The run down of important Machine Learning in Education organizations/contenders is additionally comprised from the accounts along with the appendix together with decisions.

The clear insights of this Machine Learning in Education marketplace in addition to the opportunities, dangers, and market expansion are covered in this Machine Learning in Education analysis report. It exfoliates present Machine Learning in Education marketplace branches to predict expanding ones and provides detailed business segmentation Machine Learning in Education according to product types, software Machine Learning in Education, and important geographies. A comprehensive study of this Machine Learning in Education market share and its own participation can be cited in the report.

Do Inquiry before Accessing Report at: https://www.orbisreports.com/global-machine-learning-in-education-market/?tab=discount

The primary institutions in the Worldwide Machine Learning in Education market are poised to give you an general entire overview of the growth processes, financial status and administrations, and in addition Machine Learning in Education recent co ordinated campaigns in addition to progress.

The Machine Learning in Education business report basically covers the subjects of focus identified with the Machine Learning in Education company exactly like the business definition, an assortment of use, ask and supply requirement. A thorough analysis of the Machine Learning in Education report will aid all the market players using analyzing the current patterns and essential small business procedures. This aggressive and top to base evaluation of the Machine Learning in Education industry marketplace will forecast the business growth in light of their progress openings, development parts and viability of speculation. Organizing Machine Learning in Education business approaches by sectioning the fragments and present business portions will be the facilitate and surely will similarly be useful to perusers. Finally, the report Worldwide Machine Learning in Education market reflects growth process, data distribution, benchmark division, begin looking into discoveries as well as the choices.

It highlights the principal players in Machine Learning in Education advertising in addition to their different approaches and approaches utilized. Market dynamics which keep shifting over time plus an comprehensive look at marketplace resources Machine Learning in Education are also mentioned.

It plays a broader study of previous and present marketplace trends Machine Learning in Education to forecast future market growth concerning value and volume. Additionally, it computes basic business parameters Machine Learning in Education for example industrial advancement and expansion and Machine Learning in Education offers basic market amounts in the kind of tables, pie graphs, charts and flowcharts.

Major business applications Machine Learning in Education will also be decided on the basis of achievement and performance. The sanctuary of Unstable Industries to boost their ledge from the Machine Learning in Education marketplace can be discussed.

Click here to see full TOC: https://www.orbisreports.com/global-machine-learning-in-education-market/?tab=toc

About Us:

Orbis Reports is a frontline provider of illustrative market developments and workable insights to a wide spectrum of B2B entities seeking diversified competitive intelligence to create disruptive ripples across industries. Incessant vigor for fact-checking and perseverance to achieve flawless analysis have guided our eventful history and crisp client success tales.

Orbis Reports is constantly motivated to offer superlative run-down on ongoing market developments. To fulfill this, our voluminous data archive is laden with genuine and legitimately sourced data, subject to intense validation by our in-house subject experts. A grueling validation process is implemented to double-check details of extensive publisher data pools, prior to including their diverse research reports catering to multiple industries on our coherent platform. With an astute inclination for impeccable data sourcing, rigorous quality control measures are a part and parcel in Orbis Reports.

https://neighborwebsj.com/

Visit link:
Global Machine Learning in Education Market Analysis, Share, Regional Outlook, Competitive Strategies and Forecast by 2025 NeighborWebSJ -...

ATLANTA, March 10, 2021 /PRNewswire/ --Syncron, the largest privately-owned global provider of cloud-based after-market service solutions, announced today the general availability of Syncron Price Version 20.4, which delivers several new capabilities to further automate and accelerate after-market pricing functions.The new features include usability enhancements and more sophisticated controls, to enable more optimized pricing to be donein less timeand with better outcomes.

"As the global economy begins to recover in the post-pandemic era, manufactures must provide even more sophisticated techniques to drive smarter pricing decisions," said Erik Lindholm, Vice President of Product Management at Syncron. "Price must be driven by increasingly sophisticated machine learning, algorithms, and comprehensive analytics that can automatically pinpoint sources of revenue and margin changes using real-time data. Today's companies leverage our technologies to transform their pricing strategies into competitive advantages to maintain relevance and viability in an ever-changing, increasingly sophisticated market."

Syncron Price is a leading after-market pricing tool, which leverages real-time market conditions, input costs, and competitive perspectives to help manufacturer improve productivity, reduce costs, and free valuable time to focus on handling and monitoring non-standard, complex situations.

What's in in this release:

"One of our primary goals at Al-Futtaim is to improve customer satisfaction, and we are continuing to invest in digital platforms like Syncron Price that enhance our service levels," said James Henderson, head of pricing - global aftersales at Al-Futtaim."The new updates to Syncron Price will drive greater efficiencies that help us differentiate our services and harmonize pricing and inventory management."

To learn more about Syncron Price, visit syncron.com/price.

About SyncronSyncron empowers the world's leading manufacturers to maximize product uptime and deliver exceptional after-market service experiences, while driving significant revenue and profit improvements. From industry-leading investments in research and development, to providing the fastest time-to-value, Syncron's award-winning service parts inventory, price and uptime management solutions are designed to continually exceed customer expectations. Top brands from around the world trust Syncron, the largest privately-owned global provider of cloud-based after-market service solutions, to transform their service operations into competitive differentiators. For more information, visit syncron.com.

SOURCE Syncron

Syncron

More:
Expanding Its Use of AI and Machine Learning Technologies, Syncron Adds New Capabilities to Syncron Price, Further Accelerating Innovation in...

Machine learning is perhaps old hat by now, but what's never going to be old hat is cats. People just can't seem to get enough of them. Learning Factory is an Early Access game that released last month about building an automated factory that produces the things cats want to buy, then sells them. Your job is to keep the shelves stocked and the cats happyand earn money by selling at optimal prices.

By making offers to cats your factory can train up machine learning models that will then automatically adjust market prices to account for trends and the wallets of the cats in question. Rich cats want fancy expensive cat towers and food, while normal cats just want a good deal on a ball of yarn, and construction worker cats want raw materials. It's a neat concept that bears out pretty well in action: Do you want to make a huge, all-inclusive single machine learning model or instead focus on specific models tailored to each customer type?

Learning Factory has just released on Steam Early Access. It's not that complicated yet, with about six hours of gameplay for me, but there's a lot on the developer's roadmap. Luden.io's previous game, While True: Learn(), also focused on Machine Learning and catsbut from the angle of language rather than commerce. You can learn more about Learning Factory on the official website.

If you're into factory games, have you checked out Dyson Sphere Program yet?

More:
Here's an adorable factory game about machine learning and cats - PC Gamer

One of the hardest parts of genetic research is reading DNA. Every cell of our body contains a copy of our entire genetic code, but only some of that genetic code is actually used.

Now, researchers at Harvard Universitys Department of Stem Cell and Regenerative Biology working with GPU manufacturer NVIDIA have developed a method of quickly and accurately identifying the wadded up DNA buried in our cells, using machine learning and GPUs. It might help us detect cancer and genetic disease earlier and faster.

Researching genomes is a laborious process that requires looking at chromatin, a mix of DNA and protein inside chromosomes. In 2013, scientists invented Assay for Transposase-Accessible Chromatin using sequencing (ATAC-Seq), a method of rooting around in chromatin to see whats going on. The problem is that ATAC-Seq takes hours and produces lots of noisy data. Even with high-precision scientific tools, folded up sequences of DNA are hard to sort through.

A chromatin dataset studied by ATAC-Seq was around 50 million reads of cells conducted over 15 hours. Enter AtacWorks, a machine learning program that augments ATAC-Seq and makes the data much easier to read. It can use 1 million reads in 30 minutes to get the same result that would take ATAC-Seq alone more than half a day.

AtacWorks is a residual neural network that studies past chromatin datasets and builds predictive models based on what its learned. To train AtacWork, scientists fed it a raw chromatin dataset and the same dataset after it had been cleaned up using ATAC-Seq. AtacWorks then looks at the two, learns how AtacWorks functions, then replicates it faster than humans can.

Fundamentally, what is happening here is that AIpowered by GPUs typically used for gaming (and increasingly, of course, research)is making key genetic research both much easier and much faster.

With AtacWorks, were able to conduct single-cell experiments that would typically require 10 times as many cells, Jason Buenrostro, assistant professor at Harvard and the developer of the ATAC-seq method, said in a blog post. Denoising low-quality sequencing coverage with GPU-accelerated deep learning has the potential to significantly advance our ability to study epigenetic changes associated with rare cell development and diseases.

Researchers published a study about AtacWorks in Nature Communications on March 8. According to the paper, its possible that AtacWorks will greatly speed up the process of epigenetic research and allow scientists to better research Alzheimer's, cancer, and rare diseases.

Based on these advancements, we anticipate that AtacWorks will broadly enhance the utility of epigenetic assays, providing a powerful platform to investigate the regulatory circuits that underlie cellular heterogeneity, the paper said.

It might also help develop treatment for those diseases. With very rare cell types, its not possible to study differences in their DNA using existing methods. Lead researcher Avantika Lal said in a blog. AtacWorks can help not only drive down the cost of gathering chromatin accessibility data, but also open up new possibilities in drug discovery and diagnostics.

See original here:
GPU and Machine Learning Identify Spots on DNA That Are Likely to Mutate - VICE