Machine Learning as a Service (MLaaS) Market is systematic exploration that delivers key statistics on the market status of the development trends, competitive landscape analysis, and key regions development status. The report has included strong players and analyses their limitations and strong points of the well-known players through SWOT analysis. This Report covers growing trends that are linked with major opportunities for the expansion of the Machine Learning as a Service (MLaaS) industry.

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Yottamine AnalyticsGoogleFuzzy.aiAT&TErsatz Labs, Inc.Hewlett PackardIBMBigMLSift Science, Inc.HypergiantMicrosoftAmazon Web Services

The Geographical Analysis Covers the Following Regions

The recent outbreak of the COVID-19 (Corona Virus Disease) Provide extra commentary on the newest scenario, an economic slowdown on the overall industry. In addition to this, the report also includes the development of the Machine Learning as a Service (MLaaS) market in the major regions across the world.

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Global Machine Learning as a Service (MLaaS) Market Segmentation: By Types

Cloud and Web-based Application Programming Interface (APIs)Software ToolsOthers

Global Machine Learning as a Service (MLaaS) Market Segmentation: By Applications

Cloud and Web-based Application Programming Interface (APIs)Software ToolsOthers

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This research report represents a 360-degree overview of the competitive landscape of the Machine Learning as a Service (MLaaS) Market. Furthermore, it offers enormous statistics relating to current trends, technological advancements, tools, and methodologies.

Global Machine Learning as a Service (MLaaS) Market Research Report 2020

Chapter 1 About the Machine Learning as a Service (MLaaS) Industry

Chapter 2 World Market Competition Landscape

Chapter 3 World Machine Learning as a Service (MLaaS) Market share

Chapter 4 Supply Chain Analysis

Chapter 5 Company Profiles

Chapter 6 Globalization & Trade

Chapter 7 Distributors and Customers

Chapter 8 Import, Export, Consumption and Consumption Value by Major Countries

Chapter 9 World Machine Learning as a Service (MLaaS) Market Forecast through 2027

Chapter 10 Key success factors and Market Overview

It concludes by throwing light on the recent developments that took place in the Machine Learning as a Service (MLaaS) market and their influence on the future growth of this market.

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Global Machine Learning as a Service (MLaaS) Market Size, Growth, Trends and Forecast Analysis Report 2020 to 2027 - 3rd Watch News

Deep Claim model demonstrates potential to save the U.S. billions in wasted healthcare spending

SOUTH SAN FRANCISCO, Calif., July 16, 2020 /PRNewswire/ --Alpha Health Inc, the first Unified Automation company for healthcare, announced today its paper describing the company's method of using a neural network to predict health care billing claim denials will be featured as a spotlight session during theHealthcare Systems, Population Health and the Role of Health-Tech Workshopduring the International Conference of Machine Learning 2020(ICML2020). Lead author of the paper, Byung-Hak Kim, Ph.D, and AI Technical Lead at Alpha Health will be featured in a pre-recordedspotlight session that airs during the workshop's live session on Friday, July 17th. The paper was co-authored by other members of the Alpha Health technical team, including; Co-Founder and Chief Technology Officer, Varun Ganapathi, Ph.D; Co-Founder and Vice President of Engineering, Andy Atwal and Lead Machine Learning Engineer Seshadri Sridharan.

Thepaperdescribes one of the company's machine learning models believed to be the first published deep learning-based system that successfully predicts how a claim will be paid in advance of submission to a payer. Called Deep Claim, this machine learning model predicts whether, when, and how much a payer will pay for a given hospital expense or claim.

"Deep Claim is an innovative neural network-based framework. It focuses on a part of the healthcare system that has received very little attention thus far," said Varun Ganapathi, Ph.D., co-author of the paper and Co-Founder and Chief Technology Officer at Alpha Health. "While much attention has focused on the potential of artificial intelligence and machine learning in diagnostics and drug discovery, this paper demonstrates the opportunity to apply these same approaches at scale to the back office of healthcare which could save the U.S. billions annually in wasted healthcare spending."

"I am deeply honored to have my work and the work of the team at Alpha Health featured in the Spotlight Session alongside five other papers from prestigious academic research centers, including University of Cambridge, Johns Hopkins University and NASA Frontier Development Labs, among others," said Byung-Hak Kim, Ph.D., lead author of the paper and AI Technical Lead at Alpha Health. "The fact that our model was trained on real-world claims data and that development included real deployment scenarios will enable us to integrate our research directly into our solution more quickly than a conceptual or theoretical research approach would otherwise allow. This helps us ensure that our research will directly benefit our health system customers as quickly as possible."

For this paper, Byung-Hak Kim and the Alpha Health team used almost three million de-identified claims to test the Deep Claim system. The data included in these claims contains demographic information, diagnoses, treatments, and billed amounts as inputs. The Deep Claim system then uses those inputs to predict the first response date, denial probability, denial reason codes with probability, and questionable fields in the claim. The ability to predict denial reason codes and questionable fields are especially promising as these key insights are required to proactively improve claims before they are submitted. The developers of Deep Claim demonstrated that the system performed about 22 percent better than the best baseline system.

The paper demonstrates that this deep learning system can accurately predict how an insurance company will respond to a claim. Automating this process could save individual hospitals millions of dollars each year. One of the machine learning scientists who reviewed the paper said it was "excellent work."

"Grappling with the claims system is a key question that has been understudied in (Machine Learning) for health," another reviewer wrote.

The U.S. spent about $3.6 trillion on healthcare in 2018, more than $11,000 per person, according to the Centers for Medicare and Medicaid Services. Recent studies have found that fully one-quarter of healthcare spending in the U.S. is wasteful. Administrative costs contribute the most significant share of that wasteful spending, and wereestimated to cost about $266 billionannually. Estimates are that hospitals and healthcare systems spend about $120 in administrative costs for each claim just to recoup money owed them. This system of claim preparation and billing is at the core of the healthcare system in the U.S, and is a key driver of healthcare costs. Identifying ways to eliminate some of it by improving efficiency, correcting billing errors, and saving time could significantly reduce wasteful spending.

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Alpha Health Predictive AI Research Featured In Spotlight Session at the International Conference on Machine Learning - Yahoo Finance

The measure of data put away by banks is quickly expanding and gives a chance to banks to lead prescient examinations and improve their organizations. In any case, data researchers are confronting significant difficulties, dealing with the considerable measure of data effectively, and producing bits of data with genuine business esteem.

Various advanced procedures and internet-based life trades produce data trails. Frameworks, sensors, and cell phones transmit data. Big data is touching base from different sources with disturbing speed, volume, and assortment. Consistently 2.5 quintillion bytes of data are made, and 90% of the data on the planet today was delivered inside the previous two years.

In this significant data period, the measure of data put away by any bank is quick extending, and the idea of the data has turned out to be increasingly unpredictable. These patterns give a gigantic chance to a bank to upgrade its organizations. Generally, banks have attempted to extricate data from an example of its inside data and delivered occasional reports to improve future essential leadership. These days, with the accessibility of immense measures of standardized and unstructured data from both inside and outside sources. There is expanded weight and spotlight on getting an endeavor perspective on the client efficiently. This further empowers a bank to direct significant scale client experience investigation and addition more profound bits of data for clients, channels, and the whole showcase.

With the advancement of new financial administrations, banks databases are developing to adjust to business needs. Subsequently, these databases have turned out to be incredibly mind-boggling. Since customarily organized data is spared in tables, there is much open door for expanded intricacy. For instance, another table in a database is included for another business or another database replaces the past one for a business framework update. Besides the internal data sources, there are standardized data from outside sources like financial, statistic, and geographic data. To guarantee the consistency and precision of the data, a standard data arrangement is characterized by organized data.

The development of unstructured data makes a much higher multifaceted nature. While some unstructured data can start from inside a bank, including web log documents, call records, and video replays, increasingly more can be gotten from outside sources, for example, internet based life data from Twitter**, Facebook**, and WeChat. The unstructured data is usually put away as records as opposed to database tables. A great many documents with tens or several terabytes of data can be successfully overseen on the BigInsights stage. this is an Apache Hadoop-based, equipment freethinker programming stage that gives better approaches for utilizing different and big-scale data accumulations alongside implicit explanatory capacities

Since unstructured data isnt sorted out in a well-characterized way, extra work must be done to move the data into a regularized or schematized structure before displaying it. The IBM SPSS Analytic Server (AS) gives big data investigation capacities, including incorporated help for unstructured prescient examination from the Hadoop condition. It very well may be utilized to draw legitimately and inquiry the data put away in BigInsights, dispensing with the need to move data and empowering ideal execution on a lot of data. Using apparatuses given by AS, strategies for normalizing unstructured data can be planned and actualized on a standard calendar without composing complex code and contents.

Indeed, even organized data needs extra data planning to improve the data quality on BigInsights with Big SQL (Structured Query Language), which is, an apparatus given by BigInsights as a blend of a SQL interface and parallel preparing for taking care of big data. It very well may be utilized to deal with insufficient, erroneous, or insignificant data effectively. Besides, some factual techniques are executed using Big SQL to lessen the effect of the clamor in the data. For instance, a few data nonsensical qualities are recognized and dispensed with; a few highlights are standardized or positioned. Along these lines, some exceptionally suspected anomalies are controlled from impeding the investigation. This progression helps separate signs from the commotion in significant data examination.

When every one of the data has been arranged and purified, a data combination procedure is directed on BigInsights. Data from numerous sources are consolidated, and the coordinated data is put away in a data stockroom, in which the connections between tables are well-characterized. The data clashes because of heterogeneous sources are settled. Each full join between meals with a great many occurrences should be possible on BigInsights in minutes, which for the most part, takes hours without the parallel processing procedure. Given the data stockroom, many traits can be related to every client, and a united undertaking client view is produced.

1. Customer division and inclination examination: This module delivers fine-grained client divisions in which clients share similar inclination for various sub-branches or market locales. Because of these outcomes, banks can get further bits of data in their client qualities and preferences, to improve consumer loyalty and accomplish exactness advertising by customizing banking items and administrations, just as showcasing messages. This is one of the most significant advantages of big data analytics in banking sector.

2. Potential client distinguishing proof: This module enables banks to recognize potential high-income or steadfast clients who are probably going to wind up beneficial to the bank. However, we are at present, not clients. With this strategy, banks can get an increasingly complete and exact objective client list for high-esteem clients, which can improve showcasing productivity and carry tremendous benefits to the banks.

3. Customer system investigation: By getting client and item proclivity through an examination of internet-based life systems, the client organizes inquiry can improve client maintenance, strategically pitch, and up-sell.

4. Market potential examination: Using financial, statistic, and geographic data, this module creates spatial conveyance for both existing clients and potential clients. With the market potential conveyance map, banks can have an unmistakable diagram of the objective clients areas. To distinguish the client from concentrating/lacking territories for contributing/stripping, which will bolster the banks client promoting and investigation.

5. Channel assignment and activity streamlining: Based on the banks system and spatial conveyance of client assets, this module improves the arrangement (i.e., area, type) and tasks of administration channels (i.e., retail bank or computerized/automated teller machine). Expanding income, consumer loyalty, and reach against expenses can improve client maintenance and draw in new clients.

Business data (BI) devices are fit for recognizing potential dangers related to cash loaning forms in banks. With the assistance of big data examination, banks can dissect the market inclines and choose to bring down or to expand loan fees for various people crosswise over different locales.

Data section blunders from manual structures can be decreased to a base as extensive data bring up peculiarities in client data as well.

With misrepresentation recognition calculations, clients who have poor FICO ratings can be distinguished, so banks dont advance cash to them. One more big application in banking is restricting the rates of deceitful or questionable exchanges that could improve the enemy of social exercises or psychological warfare.

big data examination can help banks in understanding client conduct dependent on the sources of info obtained from their speculation designs, shopping patterns, inspiration to contribute, and individual or money related foundations. This data assumes an urgent job in winning client unwaveringly by planning customized banking answers for them. This prompts a cooperative connection between banks and clients. Altered financial arrangements can extraordinarily expand lead age as well.

A more significant part of bank representatives guarantee that guaranteeing banking administrations meet all the administrative consistence criteria set by the Government 68% of bank workers state that their greatest worry in banking administrations is

BI instruments can help break down and monitor all the administrative prerequisites by experiencing every individual application from the clients for exact approval.

With execution examination, worker execution can be evaluated whether they have accomplished the month to month/quarterly/yearly targets. Because of the figures obtained from current offers of workers, significant data examination can decide approaches to enable them to scale better. Notwithstanding banking administrations overall can be checked to recognize what works and what doesnt.

Banks client assistance focuses will have a ton of requests and criticism age all the time. Indeed, even web-based social networking stages fill in as a sounding board for client encounters today. Big Data apparatuses can help in filtering through high volumes of data and react to every one of them sufficiently and quickly. Clients who feel that their banks esteem their input immediately will stay faithful to the brand.

At last, banks that dont advance and ride the big data wave wont just get left behind yet additionally become outdated. Receiving Big Data investigation and other howdy tech instruments to change the existing financial segment will assume a big job in deciding the lifespan of banks in the digital age.

The financial segment has consistently been moderately delayed to improve: 92 of the best 100 world driving banks still depend on IBM centralized servers in their tasks. No big surprise fintech appropriation is so high. Contrasted with the client inspired and nimble new businesses, customary budgetary establishments stand zero chance.

Be that as it may, with regards to big data, things deteriorate: most heritage frameworks cant adapt to the outstanding developing burden. Attempting to gather, store, and dissect the required measures of data utilizing an obsolete framework can put the strength of your whole structure in danger.

Thus, associations face the test of developing their preparing limits or totally re-assembling their frameworks to respond to the call.

Besides, where theres data, theres a hazard (particularly considering the heritage issue weve referenced previously). Unmistakably banking suppliers need to ensure the client data they aggregate and procedure stays safe consistently.

However, just 38% of associations worldwide are prepared to deal with the danger, as per ISACA International. That is the reason cybersecurity stays one of the most consuming issues in banking.

Furthermore, data security guidelines are getting stringent. The presentation of GDPR has put certain limitations on organizations worldwide that need to gather and apply clients data. This ought to likewise be considered.

With such big numbers of various types of data in banking and its total volume, its nothing unexpected that organizations battle to adapt to it. This turns out to be much progressively evident when attempting to isolate the useful data from the pointless.

While the portion of possibly valuable data is developing, there is still a lot of unimportant data to deal with. This implies organizations need to plan themselves and reinforce their techniques for breaking down much more data. If conceivable, locate another application for the data that has been viewed as unimportant.

In spite of the referenced difficulties, the upsides of big data in banking effectively legitimize any dangers. The bits of data it gives you the assets it opens up, the cash it spares. Data is an all-inclusive fuel that can move your business to the top.

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Do Machine Learning and AI Go Hand-in-Hand in Digital Transformation? - Techiexpert.com - TechiExpert.com

Were approaching the computational limits of deep learning. Thats according to researchers at the Massachusetts Institute of Technology, MIT-IBM Watson AI Lab, Underwood International College, and the University of Brasilia, who found in a recent study that progress in deep learning has been strongly reliant on increases in compute. Its their assertion that continued progress will require dramatically more computationally efficient deep learning methods, either through changes to existing techniques or via new as-yet-undiscovered methods.

We show deep learning is not computationally expensive by accident, but by design. The same flexibility that makes it excellent at modeling diverse phenomena and outperforming expert models also makes it dramatically more computationally expensive, the coauthors wrote. Despite this, we find that the actual computational burden of deep learning models is scaling more rapidly than (known) lower bounds from theory, suggesting that substantial improvements might be possible.

Deep learning is the subfield of machine learning concerned with algorithms inspired by the structure and function of the brain. These algorithms called artificial neural networks consist of functions (neurons) arranged in layers that transmit signals to other neurons. The signals, which are the product of input data fed into the network, travel from layer to layer and slowly tune the network, in effect adjusting the synaptic strength (weights) of each connection. The network eventually learns to make predictions by extracting features from the data set and identifying cross-sample trends.

The researchers analyzed 1,058 papers from the preprint server Arxiv.org as well as other benchmark sources to understand the connection between deep learning performance and computation, paying particular mind to domains including image classification, object detection, question answering, named entity recognition, and machine translation. They performed two separate analyses of computational requirements reflecting the two types of information available:

The coauthors report highly statistically significant slopes and strong explanatory power for all benchmarks except machine translation from English to German, where there was little variation in the computing power used. Object detection, named-entity recognition, and machine translation in particular showed large increases in hardware burden with relatively small improvements in outcomes, with computational power explaining 43% of the variance in image classification accuracy on the popular open source ImageNet benchmark.

The researchers estimate that three years of algorithmic improvement is equivalent to a 10 times increase in computing power. Collectively, our results make it clear that, across many areas of deep learning, progress in training models has depended on large increases in the amount of computing power being used, they wrote. Another possibility is that getting algorithmic improvement may itself require complementary increases in computing power.

In the course of their research, the researchers also extrapolated the projections to understand the computational power needed to hit various theoretical benchmarks, along with the associated economic and environmental costs. According to even the most optimistic of calculation, reducing the image classification error rate on ImageNet would require 105more computing.

To their point, a Synced report estimated that the University of Washingtons Grover fake news detection model cost $25,000 to train in about two weeks. OpenAI reportedly racked up a whopping $12 million to train its GPT-3language model, and Google spent an estimated $6,912 trainingBERT, a bidirectional transformer model that redefined the state of the art for 11 natural language processing tasks.

In a separate report last June, researchers at the University of Massachusetts at Amherst concluded that the amount of power required for training and searching a certain model involves the emissions of roughly 626,000 pounds of carbon dioxide. Thats equivalent to nearly five times the lifetime emissions of the average U.S. car.

We do not anticipate that the computational requirements implied by the targets The hardware, environmental, and monetary costs would be prohibitive, the researchers wrote. Hitting this in an economical way will require more efficient hardware, more efficient algorithms, or other improvements such that the net impact is this large a gain.

The researchers note theres historical precedent for deep learning improvements at the algorithmic level. They point to the emergence of hardware accelerators like Googles tensor processing units, field-programmable gate arrays (FPGAs), and application-specific integrated circuits (ASICs), as well as attempts to reduce computational complexity through network compression and acceleration techniques. They also cite neural architecture search and meta learning, which use optimization to find architectures that retain good performance on a class of problems, as avenues toward computationally efficient methods of improvement.

Indeed, an OpenAI study suggests that the amount of compute needed to train an AI model to the same performance on classifying images in ImageNet has been decreasing by a factor of 2 every 16 months since 2012. Googles Transformer architecture surpassed a previous state-of-the-art model seq2seq, which was also developed by Google with 61 times less compute three years after seq2seqs introduction. And DeepMinds AlphaZero, a system that taught itself from scratch how to master the games of chess, shogi, and Go, took eight times less compute to match an improved version of the systems predecessor, AlphaGoZero, one year later.

The explosion in computing power used for deep learning models has ended the AI winter and set new benchmarks for computer performance on a wide range of tasks. However, deep learnings prodigious appetite for computing power imposes a limit on how far it can improve performance in its current form, particularly in an era when improvements in hardware performance are slowing, the researchers wrote. The likely impact of these computational limits is forcing machine learning towards techniques that are more computationally-efficient than deep learning.

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MIT researchers warn that deep learning is approaching computational limits - VentureBeat

This is the first time a Vietnamese company has been ranked in the Top 30 contributing institutions at ICML 2020, shoulder to shoulder with the leading research centers of Apple, NEC and NTT.

The three accepted papers of VinAI Research focus on important issues in current AI research including the development of an optimal computational method to compare distributions from large data; in depth learning from key representations from image data, video for optimal control problems and proposals for effective deductive methods for complex non-linear dynamic neural systems.

The research on comparing distributions from large data is the basis for many machine learning algorithms, contributing to the promotion of unattended machine learning - one of the most relevant issues in artificial intelligence, computer vision, and natural language processing and understanding. Meanwhile, the research on data representation and nonlinear dynamic systems forms the basis of a breakthrough in the development of the automation of robots or self-driving cars.

This is the first time a Vietnamese company has been featured in the Top 30 contributing institutions at the International Conference on Machine Learning (ICML) which has always been attended by developed nations in terms of technology such as the USA, UK, China, Canada, etc.

The event not only marks the position of VinAI in the world technology community, but also shows its transformation into a leading technology corporation, gradually integrating and reaching a global technology peak.

The world has gradually become aware of Vietnam's AI research thanks to VinAI's efforts. We will continue to cooperate with leading research institutes and universities from across the world in order to build up a network of exchange and research to gradually bring the world's artificial intelligence closer to Vietnam - said Dr. Bui Hai Hung - Director of the VinAI Research Institute.

Previously, in December 2019, VinAI announced its first two scientific research papers at NeurIPS (Neural Information Processing Systems) - the annual international conference on artificial neural network information processing systems. Besides intensive research, VinAI Researchs engineering team is making every effort to develop high-quality AI core applications and technologies.

In May 2020, VinAI became one of the first companies in the world to successfully research face recognition technology when using masks.

The International Conference on Machine Learning (ICML) 2020 took place beginning July 12, 2020. The event was attended virtually by the world's leading experts in artificial intelligence and machine learning. With 40 years of organizational experience, ICML provides and publishes advanced research works on all aspects of machine learning. ICML, along with NeurIPS, is one the leading international academic conferences on artificial intelligence.

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Three Vietnamese papers accepted at the International Conference on Machine Learning - Nhan Dan Online

Engineers at Caltech have successfully designed a new method to control the movement of drones within a swarm to stop them from flying into one another. The new method relies on data to control the movement of the drones through cluttered unmapped spaces.

The team, led by Soon-Jo Chung and Yisong Yue with the help from Caltech graduates Benjamin Rivire, Wolfgang Hnig, and Guanya Shi, needed to take on two major challenges that arise when multiple drones are flying together..

The first is having the drones fly into a new environment for the first time and needing to make split second decisions to ensure they dont hit each other and obstacles surrounding them. The second is having multiple drones; the more drones flying, the less space available for each of them to maneuver around obstacles and one another.

The team was able to develop GLAS, aka Global-to-Local Safe Autonomy Synthesis, which means the drones dont need to have a picture of their surroundings before they commence flight. Rather, these drones generate their trajectory on the fly. The GLAS algorithm is used alongside Neural-Swarm, which learns the complex aerodynamic interactions in close-proximity flight.

Heres Soon-Jo Chung, Bren professor of aerospace at Caltech:

Our work shows some promising results to overcome the safety, robustness, and scalability issues of conventional black-box artificial intelligence (AI) approaches for swarm motion planning with GLAS and close-proximity control for multiple drones using Neural-Swarm.

Soon-Jo Chung, Bren professor of aerospace at Caltech

The team tested GLAS and the Neural-Swarm with 16 drones by flying them in an open arena at CaltechsCenter for Autonomous Systems and Technologies(CAST). The tests found that GLAS was able to outperform current algorithms by 20%, while the Neural-Swarm outperformed current controllers. Tracking errors were reduced by up to a factor of four.

Yisong Yue, professor of computing and mathematical sciences at Caltech, also commented on the GLAS system.

These projects demonstrate the potential of integrating modern machine-learning methods into multi-agent planning and control, and also reveal exciting new directions for machine-learning research.

What do you think about this method that ensures the drones dont crash into one another mid-air? Let us know your thoughts in the comments below.

Photo: Caltech

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These drones wont fly into one other, thanks to machine learning - DroneDJ

Available via Altairs units-based licensing model, the new version streamlines the entire workflow. At the outset, data is improved automatically by replacing missing values and dealing with outliers. New Delhi: Global technology company Altair on Thursday released a new version of Altair Knowledge Studio that is claimed to bring enhanced flexibility and transparency to data modeling and predictive analytics.

As per the release, the updated version of Knowledge Studio now employs automated machine learning (AutoML) to optimize the modeling process.

Available via Altairs units-based licensing model, the new version streamlines the entire workflow. At the outset, data is improved automatically by replacing missing values and dealing with outliers.

AutoML then builds and compares many different models to identify the best available option, said the company.

Altair said Knowledge Studio does not adopt a black box approach that shuts out users. Although models are developed automatically, explainable AI helps users in understanding, interpreting and evaluating the process.

Commenting on the new version, Sam Mahalingam, Altair chief technology officer said, "As a powerful solution that can be used by data scientists and business analysts alike, Knowledge Studio continues to lead the data science and machine learning market."

He further added, Without requiring a single line of code, Knowledge Studio visualizes data fast, and quickly generates explainable results.

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Altair introduces new version of its machine learning and predictive analytics solution - ETAuto.com

Watch all the Transform 2020 sessions on-demand right here.

Supervised learning is a more commonly used form of machine learning than reinforcement learning in part because its a faster, cheaper form of machine learning. With data sets, a supervised learning model can be mapped to inputs and outputs to create image recognition or machine translation models. A reinforcement learning algorithm, on the other hand, must observe, and that can take time, said UC Berkeley professor Ion Stoica.

Stoica works on robotics and reinforcement learning at UC Berkeleys RISELab, and if youre a developer working today, then youve likely used or come across some of his work that has built part of the modern infrastructure for machine learning. He spoke today as part of Transform 2020, an annual AI event hosted by VentureBeat that this year takes place online.

With reinforcement learning, you have to learn almost like a program because reinforcement learning is actually about a sequence of decisions to get a desired result to maximize a desired reward, so I think these are some of the reasons for greater adoption, he said. The reason we saw a lot of successes in gaming is because with gaming, its easy to simulate them, so you can do these trials very fast but when you think about the robot which is navigating in the real world, the interactions are much slower. It can lead to some physical damage to the robot if you make the wrong decisions. So yeah, its more expensive and slower, and thats why it takes much longer and is more typical.

Reinforcement learning is a subfield of machine learning that draws on multiple disciplines which began to coalesce in the 1980s. It involves an AI agent whose goal is to interact with an environment to learn a policy to maximize on a reward task. Achieving a task reward function reinforces what actions or policy the agent should follow.

Popular reinforcement learning examples include game-playing AI like DeepMinds AlphaGo and AlphaStar, which plays StarCraft 2. Engineers and researchers have also used reinforcement learning to train agents to learn how to walk, work together, and consider concepts like cooperation. Reinforcement learning is also applied in sectors like manufacturing, to help design language models, or even to generate tax policy.

While at RISELabs predecessor AMPLab, Stoica helped develop Apache Spark, an open source big data and machine learning framework that can operate in a distributed fashion. He is also creator of the Ray framework for distributed reinforcement learning.

We started Ray because we wanted to scale up some machine learning algorithms. So when we started Ray initially with distributed learning, we started to focus on reinforcement learning because its not only very promising, but its very demanding, a very difficult workload, he said.

In addition to AI research as a professor, Stoica also cofounded a number of companies, including Databricks, which he founded with other Apache Spark creators. Following a funding round last fall, Databricks received a $6.2 billion valuation. Other prominent AI startups cofounded by UC Berkeley professors include Ambidextrous Robotics, Covariant, andDeepScale.

Last month, Stoica joined colleagues in publishing a paper about Dex-Net AR at the International Conference on Robotics and Automation (ICRA). The latest iteration of the Dex-Net robotics project from RISELab uses Apples ARKit and a smartphone to scan objects, which data is then used to train a robotic arm to pick up an object.

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Why supervised learning is more common than reinforcement learning - VentureBeat