New Jersey, United States: Market Research Intellect has added a new report to its huge database of research reports, entitled Gyms, Health and Fitness Clubs Market Size and Forecast to 2027. The report offers a comprehensive assessment of the market including insights, historical data, facts, and industry-validated market data. It also covers the projections using appropriate approximations and methods.

Gyms, Health and Fitness Clubs Market Overview

The Gyms, Health and Fitness Clubs Market Report provides comprehensive data on market dynamics, market trends, product growth rate, and price. The Gyms, Health and Fitness Clubs market report has various facts and statistics assuming the future predictions of the upcoming market participants. In addition, it offers business security taking into account sales, profit, market volume, demand and market supply ratio. The in-depth study provides vital information related to market growth, driving factors, major challenges, opportunities, and threats that will prove to be very helpful for market participants in making upcoming decisions.

Gyms, Health and Fitness Clubs Market: Competitive Landscape

The Gyms, Health and Fitness Clubs Market report consists of the Competitive Landscape section which provides a complete and in-depth analysis of current market trends, changing technologies, and enhancements that are of value to companies competing in the market. The report provides an overview of sales, demand, futuristic costs and data supply as well as a growth analysis in the forecast year. The key vendors in the market that are performing the analysis are also clearly presented in the report. Their development plans, their growth approaches, and their merger and acquisition plans are also identified. Information specific to a keyword in each of these regions is also provided. This report also discusses the submarkets of these regions and their growth prospects.

Prominent players operating in the market:

Gyms, Health and Fitness Clubs Market Segmentation

The report contains the market size with 2019 as the base year and an annual forecast up to 2027 in terms of sales (in million USD). For the forecast period mentioned above, estimates for all segments including type and application have been presented on a regional basis. We implemented a combination of top-down and bottom-up approaches to market size and analyzed key regional markets, dynamics and trends for different applications.

Gyms, Health and Fitness Clubs Market Segment by Type:

Gyms, Health and Fitness Clubs Market Segment by Application:

Gyms, Health and Fitness Clubs Market Regional overview:

In the report, experts analyze and forecast the Gyms, Health and Fitness Clubs market on a global as well as regional level. Taking into account all aspects of the market in terms of regions, the focus of the report is on North America, Europe, Asia Pacific, the Middle East and Africa, and South America. The prevailing trends and various opportunities in these regions are studied that can convince the growth of the market in the forecast period 2020 to 2027.

Reasons to Buy the Gyms, Health and Fitness Clubs Market Report:

Outlook analysis of the Gyms, Health and Fitness Clubs market sector with current trends and SWOT analysis. This study evaluates the dynamics, competition, industrial strategies and strategies of the emerging countries. This report has a comprehensive guide that provides market insights and detailed data on each market segment Market growth factors and risks are presented. More precise information provision on the Gyms, Health and Fitness Clubs market for different countries. Provide visions on factors influencing the growth of the market. Market segmentation analysis, including quantitative and qualitative research considering the impact of economic and non-economic aspects Comprehensive company profiles with product offerings, important financial information and the latest developments.

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Gyms, Health and Fitness Clubs Market Size, Trends, Growth, Scope, Overall Analysis and Forecast by 2027 - The Haitian-Caribbean News Network

Mobile Health and Fitness SensorMarket 2020 Latest Industry Demand Analysis and Business Opportunities across the globe.

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Mobile Health and Fitness Sensor Market anticipated to grow at a strong CAGR by 2026: focuses on top players - Murphy's Hockey Law

Amazfit has been on a roll lately releasing smartwatch after smartwatch and each with better and better specs. This is even true of their more affordable series, the Bip. Their latest, the Amazfit Bip U not only stays true to the Bip formula with a lightweight body and affordable pricing but also adds a ton of new features found in their more expensive watches. The Bip U is now more in line with the rest of their products and is a great choice for those looking for a feature-rich health and fitness tracker that doesnt break the bank.

The Amazfit Bip U follows a familiar formula that doesnt diverge too much from the Bip series. A quick glance and youd be hard-pressed to tell the difference between this and past iterations. Its only upon closer inspection that you really see the differences.

For starters, the Bip U is a tiny bit smaller and more square than before. This makes it look better for those with smaller wrists. The body is still made of polycarbonate and the glass is 2.5D Corning Gorilla Glass 3 with an anti-fingerprint coating. The strap is made of silicone rubber with a 20mm width.

If you look even closer, youll also notice that the actual display is now a larger 1.43 full-color TFT display with curved corners. The display is very crisp and very bright with a resolution of 320302. If you compare this to the Bip S, youll see that the display looks much better with more modern UI graphics.

Lastly, on the back, the Amazfit Bip U has the updated sensors that all the current gen, higher priced Amazfit watches have. That also means a new charger so the old one will not work with this.

Overall, still a classy design and the materials used help keep the watch at a light-weight 31g.

The Amazfit Bip U seems a considerable upgrade in software and features. The software is even more in line with their higher-priced models with many of the same features as well. This is because the Bip U now utilized BioTracker 2 PPG Biological Optical Sensors instead of the original Biotracker. It also features an acceleration sensor and a gyroscope sensor. There is no GPS on this however, but that will show up later in a Pro model.

With the upgraded BioTracker 2 sensor comes new metrics. The Amazfit Bip U can now track 60+ different sports modes, your blood-oxygen levels (SpO2) with OxygenBeats, sleep quality with SomnusCare, 24/7 heart rate tracking, stress monitoring, and PAI (Personal Activity Intelligence). PAI is basically a scoring system that combines a bunch of different metrics into a single, easy to understand score. Lastly, if youre a woman, the Bip U can help track your menstrual cycle.

Aside from the health and fitness-related features, the Amazfit GTS 2 also acts like a smartwatch. You get features like alarm, schedules, weather, and being able to control your music through your watch. You also get message notifications, call notifications, and mobile phone music control along with remote control for your mobile phone camera.

With all these added features and upgraded display, theres bound to be a negative here somewhere, right? Well basically, the battery in this new Amazfit Bip U doesnt last as long per charge as it did in the Bip S. Instead of a possible 40-days of battery life, youre now getting around 9-days. That seems like a significant drop, but thats a small price to pay for more features and upgraded tech in a smaller body.

Like all Amazfit watches now, youll need to use the Zepp app to set up the watch as well as view all metrics collected. Youll also need to use it to swap watch faces if you want to customize your watch a bit.

Those looking for an affordable, yet feature-rich health and fitness tracker should give the Amazfit Bip U serious consideration. While it doesnt have the super long-lasting battery life like previous Bips, the addition of all the new features, hardware updates, and a better display makes the Amazfit Bip U more appealing. It pretty much does everything the more expensive models do and all in a lightweight package.

With that said, you cant go wrong with the Amazfit Bip U, especially at its current price point.

The Amazfit Bip U is available now and you can grab yourself one over on Amazon.

Originally posted here:
Amazfit Bip U Makes this Affordable Fitness & Health Tracker Even Better [Review] - Gstyle magazine

LTZEN/RCKEN, GERMANY- It would seem an interesting locale for a Texans fan, this small village located in the eastern expanses of modern day Germany. When we say small, we do mean small, as it barely exceeded 600 people (in what population statistics are available). However, aside from maybe winning a Euro or two in a German geography bet at the bar, this town does have one other claim to fame. Its most famous resident is also the newest celebrity fan of the Houston Texans.

Of course I am a fan of the Texans opined Friedrich Nietzsche Why wouldnt I be? The famed German philosopher, whose writings in the second half of the 19th century did much to drive modern thought on life, God and the constant struggle of man to find his place in the world. Love what I see going on there with that team, or at least, if I was capable of such a thing as love, which I am not.

Setting aside the fact that Nietzsche has been dead for 120 years What, weve already killed off God, why wouldnt we have killed off Death? If Death is dead, then we do not die, and we can transverse between life and death. Since there is no Death, and since we killed off God, there is no one to regulate the realm between life and death, thus, we can have this conversation, despite what is said about my life and death.

As we attempted to decipher that last statement or three, Nietzsche proceed to describe how he came to view the Texans as worthy of his attention. Always had a thing for those sort of out there folks, especially if they take the view of life that ultimately, it is not filled with any real hope or purpose. My boys Wagner and Dostoyevsky, they fit my style perfectly. Long-winded, bombastic at times, and the endings, all filled with no expectation of hope or victory...perfect.

When asked how exactly that fit into the Texans, Nietzsche did not answer right away. Ahhhmy first hit in a while. What? Oh, just had to pop a couple of opioid pills. Damn, where the hell was this back in the day? Had to go with the old, need

Herr Nietzsche?! The Texans!

What about the Texans? What is this Texans thing you speak of?

The point of this interview.

What is the point of this interview? What is the point of any interview? What is the point of is?

KNOCK IT OFF!!!

What is theoh, hell, my buzz just endedok, where were we? Actually, where are we? Where will we be, or can we be? Alright, the Texans, yes, anyway, my reasons why

Go on.

Well, as you know, I tend to see life as a whats the point? sort of game. Yes, this American football is a game of sorts and well, even if there is a situation where there is a winner or loser, there is presumption of hope. Yet, I then see the Texans and I notice, Where is the hope and where is the purpose?

Yes, Watson is a great player and I am damned glad he is on my fantasy team, but what is the point of what he is doing? He will put up all of those stats and he will throw it all over the place, but to what purpose? There is no championship in his future for this year. Also, there is the built-in torment of false hope with the whole worst team getting best draft picks, but since the Texans dont have to worry about that. It is as if they are the perfect team for me, playing with no short or long term hope.

When asked if couldnt have just been a Cleveland or Detroit fan, Nietzsche just blanched Why the hell would anyone waste their life cheering for those losers?

We did ask if he perhaps had followed Green Bay at one point Alright, Im going to stop you right there. Ever since the 1960s, I always hear that damned joke, especially from that douche Heidegger Hey, how was it playing for Lombardi? or I remember that great game against Detroit when you limped the pick-six into the end zone. Thats usually when I tell him that he was a dumbass in the Hawthorne short storysuch is the afterlife for us philosophers. Of course, since Death is now dead, and there is still no God in the way since we killed him, there is really not such a thing as afterlife or life or life-after-death. Of course, if we killed God, but then killed Death, how could God still be dead? If that is the case, then God is alive, and then there is once against Death, but then, we just kill them all again, for them to kill us again

So, anyway, in the existence that we occupy at a given point and space, the lame-arse Nitschke jokes are so pass. Besides, Heidegger knows that during that time, I was all about the Butkus. Big bruising linebacker, treating other players like we treated the French in the Franco-Prussian Waror at least until the one night at the French brothelstill drives me nuts, literally. He [Butkus] was more my style, toiling away on a team that did noting and went nowhere. Kinda like [J.J.] Watt now.

When asked if any other folks he knew were Texans fans, he demurred Well, they are certainly gaining some converts in the nihilist school right now. Their lack of purpose or hope, stuck just living and playing, it does match our beat. Heard Wagner thought of updating the Gotterdammerung to have the BOB coda, but that could just be the long-standing ringing in my ears that hasnt stopped since 1889. Anyway, Ill keep tabs on the team. They seem like they will be the poster children for my school of thought for seasons to come.

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Totally Not Fake News: The Latest Texans Fan - Battle Red Blog

Like High School Musical on some sort of absinthe/Xanax cocktail, The Prom is an outrageous work of steroidal show tune madness, directed by the dark master himself, Ryan Glee Murphy, who is to jazz-hands musical theatre what Nancy Meyers is to upscale romcom or Friedrich Nietzsche to classical philology.

Meryl Streep and James Corden play Dee Dee Allen and Barry Glickman, two fading Broadway stars in trouble after their latest show closes ignominiously; it is called Eleanor!, a misjudged musical version of the life of Eleanor Roosevelt with Dee Dee in the title role and Barry as Franklin D Roosevelt. Barry also has financial difficulties (I had to declare bankruptcy after my self-produced Notes on a Scandal). After unhelpful press notices turn their opening night party at Sardis into a wake, Dee Dee and Barry find themselves drowning their sorrows with chorus-line trooper Angie (Nicole Kidman) and unemployed-actor-turned-bartender Trent (played by The Book of Mormons Andrew Rannells). How on earth are they going to turn their careers around?

Then Angie sees a news story trending on Twitter: a gay teenager in Indiana has been prevented by her high school from bringing a girl as a date to the prom. The teen in question is Emma (a nice performance from Jo Ellen Pellman, like a young Elisabeth Moss), her secret girlfriend is Alyssa (Ariana DeBose) and it is Alyssas fiercely conservative mom (Kerry Washington) who is behind the ban. Our heroic foursome declare that they will sweep into hicksville with all their enlightened values and glamorous celebrity, and campaign against this homophobia, boosting their prestige in the biz. They gatecrash a tense school meeting, declaring dramatically: We are liberals from Broadway!

The Prom is based on the Broadway stage musical by Matthew Sklar and Chad Beguelin which incredibly is based on a real-life case from 2010. This movie starts in Manhattan but doesnt fully come to life until it moves to the school, with all its deeply serious drama, and then the raddled showbiz grownups arrive as desperate, insecure, lonely and status-obsessed as any teenager thus proving the ancient maxim that adult life is just high school with money.

The Prom is as corny as you like, and there is hardly a plot turn, transition or song-cue that cant be guessed well in advance; but its so goofy that you just have to enjoy it, and there are some very funny lines. One narcissistic girl sings to herself in the mirror: You have to hand it to me / Even I would do me. When the local hotel doesnt have a suite for Dee Dee, she slams both her Tony awards on the reception counter to prove how important she is, and then poor Barry does the same with his mystifying New York Drama Desk award statuette and no one knows what it is. The night of the revived prom brings a location-cheat editing trick that I havent seen since The Silence of the Lambs.

Could this be the first film of the Joe Biden era, as the liberals from the big city have to get over their snobbish disdain for the basket of deplorables and all come together? Well, maybe. It is amusing when the schools principal Mr Hawkins (Keegan-Michael Key) happens to be a massive fan of Dee Dee and there is a spark but Dee Dee cannot grasp the idea that a man could like Broadway musicals and be heterosexual. But of course there is no question of the music-theatre megastars seriously conceding anything to conservative-minded locals, other than the time-honoured virtue of putting aside your self-love for a bit. But self-love is the whole point.

The Prom is released on 4 December in cinemas, and on 11 December on Netflix.

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The Prom review is Ryan Murphy's musical the first film of the Biden era? - The Guardian

Companies are collecting increasing amounts of data, a trend that is driving the development of better analytical tools and tougher security. Analysis and security are now converging as confidential computing prepares to deliver a critical boost to artificial intelligence.

Intel has been investing heavily in confidential computing as a way to expand the amount and types of data companies will manage through cloud services. According to Intel Fellow Ron Perez, who works on security architecture with the Intel Data Center Group, the company believes the emerging security standard will allow enterprises and large organizations to explore new ways to share the data needed to fuel AI and machine learning.

We see this as a long-term effort, Perez said. But the reason why were investing is that it has the potential to be a huge shift for cloud and utility computing.

Confidential computing is a standard that moves past policy-based privacy and security to implement safeguards on a deeper technical level. By using encryption that can only be unlocked via keys the client holds, confidential computing ensures companies hosting data and applications in the cloud have no way to access underlying data, whether it is stored in a database or passes through an application.

The concept is gaining momentum because it allows data to remain encrypted even as its being processed and used in applications. Because the company hosting the data cant access it, this security standard should prevent hackers from grabbing unencrypted data when it moves to the application layer. It would also theoretically allow companies to share data, even between competitors, to perform security checks on customers and weed out fraud.

In August 2019, Intel became one of the founding members of the Confidential Computing Consortium, an open source effort managed by the Linux Foundation that aims to develop the hardware and software standards needed to further adoption. Companies like IBM,Google, and Microsoft have begun to highlight their work in this area as a way to encourage large enterprises, particularly in areas such as finance and health care, to put more of their sensitive data in the cloud.

Perez leads a group of senior technologists at Intel focused on security architecture through a program dubbed Pathfinding. Perez describes it as the pursuit of interesting challenges that our customers are facing. In Perezs case, the goal is to develop a pipeline of security technologies for Intels datacenter customers.

Intel began its work in this area before the term confidential computing came into vogue, with Perez pointing to the companys launch of software guard extensions in 2015. The SGXs are security coding built directly into Intel processors that create separate memory enclaves where data could be placed to limit access. This idea of using hardware and software to protect data while allowing it to be processed is at the heart of confidential computing.

Microsoft used these Intel processors for its Azure cloud to enable its own confidential computing service. Last month, Intel announced it was expanding these capabilities in a new generation of its Xeon Scalable platform.

Our approach has been to drive continuous innovation and deep collaboration with our technology partners to improve the confidentiality and integrity of all data, wherever it is, Perez said.

Proponents of confidential computing argue that it will lead to a new wave of cloud innovation as companies become more comfortable putting their most sensitive data online. Perez said that helps drive AI and machine learning in a couple of ways.

The first is indirect. AI and ML have advanced in recent years, thanks to the growing datasets available to refine algorithms. Confidential computing, by bringing even more and richer data online, will benefit that development.

The main connection to machine learning and artificial intelligence is the fact that were generating more and more data, Perez said. Were analyzing this data with various machine learning technologies. And that explosion of data is whats really driving the interest in confidential computing, whether its used for machine learning or not. Machine learning just happens to be one of its main uses.

No matter the type of underlying data, if it must be decrypted to be used, the security of algorithms it passes through is critical.

How do you protect these algorithms across this very broad spectrum of use cases? Perez said. We see confidential computing as a paradigm shift for cloud computing. The infrastructure providers are providing the capabilities that allow cloud companies to deliver these services as a utility, and they dont have to take responsibility for the protection of the data themselves.

Beyond that, confidential computing is enabling different types of collaboration around data to drive machine learning. Perez pointed to the example of a brain tumor project at the University of Pennsylvania.

Penns Perelman School of Medicine has teamed up with 29 other health care and research institutions around the world, including in the U.K., Germany, and India. The group is using Intels confidential computing to develop a distributed approach to machine learning that allows them to share patient data, including medical imaging. Because such data can remain encrypted while it is being used for machine learning, the group can safely share that data and collaborate in a way that otherwise might not be possible.

Thats critical because data is urgently needed to train machine learning, but no single institution has enough to achieve this on its own. Previously, Penn Medicine and Intel Labs published a study showing that federated learning (a collaborative approach) could train a machine learning model far more effectively than working alone. In this case, the group believes the combination of confidential computing and federated learning will allow them to make rapid breakthroughs in AI models that identify brain tumors.

Merchants are also tapping the ability to allow new types of collaboration for customer and partner data, as are enterprises. While analysts like Gartner believe the real impact of confidential computing may still be several years away, Perez said it is already helping some sectors accelerate their AI and machine learning capabilities in the short term.

There are multiple aspects of the computing stack that need to be protected, Perez said. Confidential computing solves problems that couldnt be solved before. The concept that I can use any computing capability that may reside in any country around the world and still have some preservation of the privacy and confidentiality of my data, thats pretty powerful.

See original here:

Why Intel believes confidential computing will boost AI and machine learning - VentureBeat

December 03, 2020 04:47 ET | Source: Fortune Business Insights

Pune, Dec. 03, 2020 (GLOBE NEWSWIRE) -- The global machine learning market size is anticipated to rise remarkably on account of the advancement in deep learning. This, coupled with the amalgamation of analytics-driven solutions with ML abilities, is expected to aid in favor of the market in the coming years. As per a recent report by Fortune Business Insights, titled, Machine Learning Market Size, Share & Covid-19 Impact Analysis, By Component (Solution, and Services), By Enterprise Size (SMEs, and Large Enterprises), By Deployment (Cloud and On-premise), By Industry (Healthcare, Retail, IT and Telecommunication, BFSI, Automotive and Transportation, Advertising and Media, Manufacturing, and Others), and Regional Forecast, 2020-2027, the value of this market was USD 8.43 billion in 2019 and is likely to exhibit a CAGR of 39.2% to reach USD 117.19 billion by the end of 2027.

Click here to get the short-term and long-term impacts of COVID-19 on this Market.

Please visit: https://www.fortunebusinessinsights.com/machine-learning-market-102226

Coronavirus has not only brought about health issues and created social distance among people but it has also hampered the industrial and commercial sectors drastically. The whole world is following home quarantine, and we are unsure when we can freely roam the streets again. The governments of various nations are also making considerable efforts to bring the COVID-19 situation under control, and hopefully, we will overcome this obstacle soon.

Fortune Business Insights is offering special reports on various markets impacted by the COVID-19 pandemic. These reports provide a thorough analysis of the market and will be helpful for the players and investors to accordingly study and chalk out the growth strategies for better revenue generation.

What Are the Objectives of the Report?

The report is based on a 360-degree overview of the market that discusses major factors driving, repelling, challenging, and creating opportunities for the market. It also talks about the current trends prevalent in the market, recent industry developments, and other interesting insights that will help investors accordingly chalk out growth strategies for the future. The report also highlights the names of major segments and significant players operating in the market. For more information on the report, log on to the company website.

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Drivers & Restraints-

Huge Investment in Artificial Intelligence to Aid in Favor of Market

The e-commerce sector has showcased significant growth in the past few years, with the advent of retail analytics. Companies such as Alibaba, eBay, Amazon, and others are utilizing advanced data analytics solutions for boosting their sales graph. Thus, the advent of analytical solutions into the e-commerce sector, offering enhanced consumer experience and rise in sales graph is one of the major factors promoting the machine learning market growth. In addition to this, the use of machine intelligence solutions for encrypting and protecting data is adding boost to the market. Furthermore, massive investments in artificial intelligence (AI) and efforts to introduce innovations in this field are further expected to add impetus to the market in the coming years.

On the flipside, national security threat issues such as deep fakes and other fraudulent cases, coupled with the misuse of robots, may hamper the overall market growth. Nevertheless, the introduction and increasing popularity of self-driving cars from the automotive industry is projected to create new growth opportunities for the market in the coming years.

Speak To Our Analyst- https://www.fortunebusinessinsights.com/enquiry/speak-to-analyst/machine-learning-market-102226

Segment:

IT and Telecommunication Segment Bagged Major Share Soon to be Overpowered by Healthcare Sector

Based on segmentation by industry, the IT and telecommunication segment earned 22.0% machine learning market share and emerged dominant. But the current COVID-19 pandemic increased the popularity of wearable medical devices to keep track of personal health and diet. This is expected to help the healthcare sector emerge dominant in the coming years.

Regional Analysis-

Asia Pacific to Exhibit Fastest Growth Rate Owing to Rising Adoption by Developing Economies

Region-wise, North America emerged dominant in the market, with a revenue of USD 3.07 billion in 2019. This is attributable to the presence of significant players such as IBM Corporation, Oracle Corporation, Amazon.com, and others and their investments in research and development of better software solutions for this technology. On the other side, the market in Asia Pacific is expected to exhibit a rapid CAGR in the forecast period on account of the increasing adoption of artificial intelligence, machine learning, and other latest advancements in the rising economies such as India, China, and others.

Competitive Landscape-

Players Focusing on Development of Responsible Machine Learning to Strengthen their position

The global market generates significant revenues from companies such as Microsoft Corporation, IBM Corporation, SAS Institute Inc., Amazon.com, and others. The principal objective of these players is to develop responsible machine learning that will help prevent unauthorized use of such solutions for fraudulent or data theft crimes. Other players are engaging in collaborative efforts to strengthen their position in the market.

Major Industry Developments of this Market Include:

March 2019 The latest and most advanced ML capability was added to the 365 platforms by Microsoft. This new feature will help strengthen the internet-facing virtual machines by increasing security when merged with the integration of machine learning by Azures security center.

Some of the Key Players of the Machine Learning Market Include:

Quick Buy:Machine Learning Market Research Report: https://www.fortunebusinessinsights.com/checkout-page/102226

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Machine Learning Market to Grow Notably Attributed to Increasing Adoption of Analytics-driven Solutions by Developing Economies, says Fortune Business...

Machine learning is all the rage in todays analytical market. According to Kenneth Research, the value of machine learning is growing sharply and is expected to reach over $23B by 2023 an annual growth rate of 43 percent between 2018-2023. IDC enforces this point predicting that worldwide spend on cognitive & AI systems, which includes machine learning, will reach $110B by 2024. Likewise, Gartner believes the business value machine learning and AI will create will be about $3.9T in 2022. With these kinds of predictions, its no surprise organizations want to incorporate these popular (and lucrative) methods into their analytical processes.

Machine learning is not a new concept in the analytical lifecycle data scientists have been using machine learning to help facilitate analytical processes and drive insights for decades. What is new is the use of machine learning for data preparation tasks to accelerate data processes and expedite analytical efforts. Here are four ways data preparation efforts can leverage machine learning for more effective and faster data reconditioning efforts:

1. Data transformation recommendations built into solutions suggest how data needs to be standardized and converted to meet analytical needs. This feature can proactively look at the quality of the data set and identify what quality transformation should be executed to ensure the data is ready for analytics. These recommendations are based on historical preparation tasks while using AI/machine learning to present new recommendations to the user.

2. Automated analytical partitioning applies AI/machine learning to determine the best way to partition the data for analytics. It also provides transparency on which method should be used and why. This helps speed up the analytical process because the data is automatically grouped together for training, validation and test buckets.

3. Smart matching incorporates AI/machine learning to proactively group like data elements together. Using the most effective matching discipline allows the user to decide if they want to automatically build a golden record and assign unique keys to the data.

4. Intelligent data assignment provides the data and analytics community quick understanding of the classification of the data type (e.g., name, address, product, sku), which allows simple tasks like gender assignment to be performed without user intervention. Data automatically populates a data catalog and uses natural language processing to explain the data, while contributing to the lineage for quick impact analysis.

The main objective of applying machine learning techniques to the data preparation process in innovative ways is to find hidden treasures in the data. These found treasures in the data can have a positive impact across many facets of business enterprises such as competitive advantage, regulation requirements, supply chain fulfillment and optimization, manufacturing health, medical insights, etc. To be specific, here is an exploration of how machine learning can impact a critical business initiative like fraud detection and prevention.

1. Unsupervised learning added to the fraud environment enables organizations to find edge cases in the data and proactively identify abnormal behaviors not found in traditional methods. These abnormal behaviors can be moved into a supervised learning process, like regression or classification analytics, to predict if these outliers are new types of fraudulent activities that require additional investigation.

2. Text analytics provide unique insights by disambiguating certain data attributes that numerical data cant identify and therefore helping to identify unknown patterns between text and traditional data components. These insights may lead to new fraud patterns for consideration.

3. Hibernation can be used for smart alerting to apply a scoring model across all data - active and historical - to identify new fraud patterns that need attention. This process consolidates scores into one entity-level score for risk assessment and transaction monitoring, helping to identify new, out-of-threshold incidents for additional investigation.

4. Adding automated natural language processing (NLP) to the fraud mix provides human language translations to complex analytical findings, delivering the information in a way that humans can use and understand. Coupling NLP with image recognition helps identify document types using context analytics on text classifications, improving the accuracy rates of fraud detection.

5. Through dynamic ranking, more data is available for machine learning processes, resulting in more complete cluster analysis, identification of better risk predictors and elimination of false variables. Machine learning will teach itself about the normal data conditions and proactively monitor and update risk scores for more data-driven results.

6. Intelligent due diligence provides entity resolutions across product and business lines. Machine learning creates profiling for peer groupings and identifies expected behaviors using network and graph analytics. Because machine learning identifies expected behaviors, it can also point out unexpected behaviors that may indicate suspicious activities or a market shift that needs to be addressed.

7. Smart alerting takes traditional alerting data and combines it with additional data to unearth new conditions that need to be investigated. With machine learning, the tools can teach themselves what alerts can be handled automatically and what alerts need a human eye. Intelligent detection optimizes existing detection models by including more data and AI/machine learning techniques to identify new scenarios using newly combined targeted subgroups to find additional detections or alerts for consideration.

In summary, the machine learning marketspace is exploding, bringing business value to organizations across all industries. Machine learning produces new insights and allows organizations to leverage more or all the data to make better and smarter decisions. So, lets start speaking the new machine learning language of data and analytics today!

Kim Kaluba, Senior Manager for Data Management Solutions, SAS

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Machine learning: The new language of data and analytics - ITProPortal

No matter what kind of traditional HPC simulation and modeling system you have, no matter what kind of fancy new machine learning AI system you have, IBM has an appliance that it wants to sell you to help make these systems work better and work better together if you are mixing HPC and AI.

It is called the Bayesian Optimization Accelerator, and it is a homegrown statistical analytics stack that runs on one or more of Big Blues Witherspoon Power AC922 hybrid CPU-GPU supercomputer nodes the ones that are used in the Summit supercomputer at Oak Ridge National Laboratories and the Sierra supercomputer used at Lawrence Livermore National Laboratory.

IBM has been touting the ideas behind the BOA system for more than two years now, and it is finally being commercialized after some initial testing in specific domains that illustrate the principles that can be modified and applied to all kinds of simulation and modeling workloads. Dave Turek, now retired from IBM but the longtime executive steering the companys HPC efforts, walked us through the theory behind the BOA software stack, which presumably came out of IBM Research, way back at SC18 two years ago. As far as we can tell, this is still the best English language description of what BOA does and how it does it. Turek gave us an update on BOA at our HPC Day event ahead of SC19 last year, focusing specifically on how Bayesian statistical principles can be applied to ensembles of simulations in classical HPC applications to do better work and get to results faster.

In the HPC world, we tend to try to throw more hardware at the problem and then figure out how to scale up frameworks to share memory and scale out applications across the more capacious hardware, but this is different. With BOA, the ideas can be applied to any HPC system, regardless of vendor or architecture. This is not only transformational for IBM in that it feels more like a service encapsulated in an appliance and will have an annuity-like revenue stream across many thousands of potential HPC installations. It is also important for IBM in that the next generation exascale machines in the United States, where IBM won the big deals for Summit and Sierra, are not based on the combination of IBM Power processors, Nvidia GPU accelerators, and Mellanox InfiniBand interconnects. The follow-on Frontier and El Capitan systems at these labs are rather using AMD CPU and GPU compute engines and a mix of Infinity Fabric for in-node connectivity and Cray Slingshot Ethernet (now part of Hewlett Packard Enterprise) for lashing nodes together. Even these machines might benefit from BOA, which gives Big Blue some play across the HPC spectrum, much as its Spectrum Scale (formerly GPFS) parallel file system is often used in systems where IBM is not the primary contractor. BOA is even more open in this sense, although like GPFS, the underlying software stack used in the BOA appliance is not open source anymore than GPFS is. This is very unlikely to change, even with IBM acquiring Red Hat last year and becoming the largest vendor of support contracts for tested and integrated open source software stacks in the world.

So what is this thing that IBM is selling? As the name suggests, it is based on Bayesian optimization, a field of mathematics that was created by Jonas Mockus in the 1970s and that has been applied to all kinds of algorithms including various kinds of reinforcement learning systems in the artificial intelligence field. But it is important to note that Bayesian optimization does not itself involve machine learning based on neural networks, but what IBM is in fact doing is using Bayesian optimization and machine learning together to drive ensembles of HPC simulations and models. This is the clever bit.

With Bayesian optimization, you know there is a function in the world and it is in a black box (mathematically speaking, not literally). You have a set of inputs and you see how it behaves through its outputs. The optimization part is to build a database of inputs and outputs and to statistically infer something about what is going on between the two, and then create a mathematical guess about what a better set of inputs might be to get a desired output. The trick is to use machine learning training to watch what a database of inputs yields for outputs, and you use the results of that to infer what the next set of inputs should be. In the case of HPC simulations, this means you can figure out what should be simulated instead of trying to simulate all possible scenarios or at least a very large number of them. BOA doesnt change the simulation code one bit and that is important. It just is given a sense of the desired goal of the simulation thats the tricky part that requires the domain expertise that IBM Research can supply and watches the inputs and outputs of simulations and offers suggested inputs.

The net effect of BOA is that, over time, you need less computing to run an HPC ensemble, and you also can converge to the answer is less time as well. Or, more of that computing can be dedicated to driving larger or more fine-grained simulations because the number of runs in an ensemble is a lot lower. We all know that time is fluid money and that hardware is also frozen money depreciated one little trickle at a time through use, and add them together and there is a lot of money that can potentially be saved.

Chris Porter, offering manager for HPC cloud for Power Systems at IBM, walked us through how BOA is being commercialized and some of the data from the early use cases where BOA was deployed.

One of the early use cases was at the Texas Advanced Computing Center at the University of Texas at Austin, where Mary Wheeler, a world-renowned expert in numerical methods for partial differential equations as they apply to oil and gas reservoir models, used the BOA appliance in some simulations. To be specific, Wheelers reservoir model is called the Integrated Parallel Accurate Reservoir Simulator, or IPARS, and it has gradient descent/ascent model built within it. Using their standard technique for maximizing the oil extraction from a reservoir with the model, it would take on the order of 200 evaluations of the model to get what Porter characterized as a good result. But by injecting BOA into the flow of simulations, they could get the same result with only 73 evaluations. That is a 63.5 percent reduction in the number of evaluations performed.

IBMs own Power10 design team also used BOA in its electronic design automation (EDA) workflow, specifically to check the signal integrity of the design. To do so using the raw EDA software took over 5,600 simulations, and IBM did all of that work as it normally would do. But then IBM added BOA to the stack and redid all of the work, and go to the same level of accuracy in analyzing the signal integrity of the Power10 chips traces with only 140 simulations. That is a 97.5 percent reduction in computing needed or a factor of 40X speedup if you want to look at it that way. (Porter warns that not all simulations will see this kind of huge bump.)

In a third use case, a petroleum company that creates industrial lubricants, whom Porter could not name, was creating a lubricant that had three components. There are myriad different proportions to mix them in to get a desired viscosity and slipperiness, and the important factor is that one of these components was very expensive and the other two were not. Maximizing the performance of the lubricant while minimizing the amount of the expensive item was the task in this case, and this company ran the simulation without and then with the BOA appliance plugged in. Heres the fun bit: BOA found a totally unusual configuration that this companys scientists would have never thought of and was able to find the right mix with four orders of magnitude more certainty than prior ensemble simulations and did one-third as many simulations to get to the result.

These are dramatic speedups, and demonstrate the principle that changing algorithms and methods is as important as changing hardware to run older algorithms and methods.

IBM is being a bit secretive about what is in the BOA software stack, but it is using PyTorch and TensorFlow for machine learning frameworks in different stages and GP Pro for sparse Gaussian process analysis, all of which have been tuned to run across the IBM Power9 and Nvidia V100 GPU accelerators in a hybrid (and memory coherent) fashion. The BOA stack could, in theory, run on any system with any CPU and any GPU, but it really is tuned up for the Power AC922 hardware.

At the moment, IBM is selling two different configurations of the BOA appliance. One has two V100 GPU accelerators, each with 16 GB of HBM2 memory, and two Power9 processors with a total of 40 cores running at a base 2 GHz and a turbo boost 2.87 GHz and 256 GB of their own DDR4 memory. The second BOA hardware configuration has a pair of Power9 chips with a total of 44 cores running at a base 1.9 GHz and a turbo boost to 3.1 GHz with its own 1 TB of memory, plus four of the V100 GPU accelerators with 16 GB of HBM2 memory each.

IBM is not providing pricing for these two machines, or the BOA stack on top of it, but Porter says that it is sold under an annual subscription that runs to hundreds of thousands of dollars per server per year. That may sound like a lot, but considering the cost of an HPC cluster, which runs from millions of dollars to hundreds of millions of dollars, this is a small percentage of the overall cost and can help boost the effective performance of the machine by an order of magnitude or more.

The BOA appliance became available on November 27. Initial target customers are in molecular modeling, aerospace and auto manufacturing, drug discovery, and oil and gas reservoir modeling and a bit of seismic processing, too.

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Injecting Machine Learning And Bayesian Optimization Into HPC - The Next Platform

By Erik Fogg | November 30, 2020

While the human element will still exist, incorporating AI/ML will improve the QA testing within an organization.

The needle in quality assurance (QA) testing is moving in the direction of increased use of artificial intelligence (AI) and machine learning (ML). However, the integration of AI/ML in the testing process is not across the board. The adoption of advanced technologies still tends to be skewed towards large companies.

Some companies have held back, waiting to see if AI met the initial hype as being a disruptor in various industries. However, the growing consensus is that the use of AI benefits the organizations that have implemented it and improves efficiencies.

Small- and mid-sized could benefit from testing software using AI/ML to meet some of the challenges faced by QA teams. While AI and ML are not substitutes for human testing, they can be a supplement to the testing methodology.

See also: Real-time Applications and Business Transformation

As development is completed and moves to the testing stage of the system development life cycle, QA teams must prove that end-users can use the application as intended and without issue. Part of end-to-end (E2E) testing includes identifying the following:

E2E testing plans should incorporate all of these to improve deployment success. Even while facing time constraints and ever-changing requirements, testing cycles are increasingly quick and short. Yet, they still demand high quality in order to meet end-user needs.

Lets look at some of the specific ways AI and ML can streamline the testing process while also making it more robust.

AI in software testing reduces the time spent on manually testing. Teams are then able to apply their efforts to more complex tasks that require human interpretation.

Developers and QA staff will need to apply less effort in designing, prioritizing, writing, and maintaining E2E tests. This will expedite timelines for delivery and free up resources to work on developing new products rather than testing a new release.

With more rapid deployment, there is an increased need for regression testing, to the point where humans cannot realistically keep up. Companies can use AI for some of the more tedious regression testing tasks, where ML can be used to generate test scripts.

In the example of a UI change, AI/ML can be used to scan for color, shape, size, or overlap. Where these would otherwise be manual tests, AI can be used for validation of the changes that a QA tester may miss.

When introducing a change, how many tests are needed to pass QA and validate that there are no issues? Leveraging ML can determine how many tests to run based on code changes and the outcomes of past changes and tests.

ML can also select the appropriate tests to run by identifying the particular subset of scenarios affected and the likelihood of failure. This creates more targeted testing.

With changes that may impact a large number of fields, AI/ML automate the validation of these fields. For example, a scenario might be Every field that is a percentage should display two decimals. Rather than manually checking each field, this can be automated.

ML can adapt to minor code changes so that the code can self-correct or self-heal over time. This is something that could otherwise take hours for a human to fix and re-test.

While QA testers are good at finding and addressing complex problems and proving out test scenarios, they are still human. Errors can occur in testing, especially from burnout syndrome of completing tedious processing. AI is not affected by the number of repeat tests and therefore yields more accurate and reliable results.

Software development teams are also ultimately composed of people, and therefore personalities. Friction can occur between developers and QA analysts, particularly under time constraints or the outcomes found during testing. AI/ML can remove those human interactions that may cause holdups in the testing process by providing objective results.

Often when a failure occurs during testing, the QA tester or developer will need to determine the root cause. This can include parsing out the code to determine the exact point of failure and resolving it from there.

In place of going through thousands of lines of codes, AI will be able to sort through the log files, scan the codes, and detect errors within seconds. This saves hours of time and allows the developer to dive into the specific part of the code to fix the problem.

While the human element will still exist, introducing testing software that incorporates AI/ML will overall improve the QA testing within an organization. Equally as important as knowing when to use AI and ML is knowing when not to use it. Specific scenario testing or applying human logic in a scenario to verify the outcome are not well suited for AI and ML.

But for understanding user behavior, gathering data analytics will build the appropriate test cases. This information identifies the failures that are most likely to occur, which makes for better testing models.

AI/ML can also specify patterns over time, build test environments, and stabilize test scripts. All of these allow the organization to spend more time developing new product and less time testing.

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QA Increasingly Benefits from AI and Machine Learning - RTInsights