Learning Curve

As businesses and organizations strive to manage ever-growing volumes of data and, even more important, derive value from that data, they are increasingly turning to data engineering and machine learning tools to improve and even automate their big data processes and workflows.

As part of the 2021 Big Data 100, CRN has compiled a list of data science and machine learning tool companies that solution providers should be aware of. While most of these are not exactly household names, some, including DataRobot, Dataiku and H2O, have been around for a number of years and have achieved significant market presence. Others, including dotData, are more recent startups.

This week CRN is running the Big Data 100 list in slideshows, organized by technology category, with vendors of business analytics software, database systems, data management and integration software, data science and machine learning tools, and big data systems and platforms.

(Some vendors market big data products that span multiple technology categories. They appear in the slideshow for the technology segment in which they are most prominent.)

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The Coolest Data Science And Machine Learning Tool Companies Of The 2021 Big Data 100 - CRN

Travel Guard has specific cruise insurance policies, which makes it simpler than trying to find an add-on. If youre getting a quote online, theyll ask you to specify if youre taking a plane, a cruise, or both. They cover any emergency travel assistance, trip interruption, delay, or cancellation.

Cruise travel insurance secures non-refundable investments related to your trip. It reimburses you if you have to cancel your international cruise unexpectedly prior to your departure. It also provides medical coverage for unexpected injuries and illnesses. Cruise travel insurance policies provide medical coverage while you are on a holiday. A cancellation after this can mean a huge financial loss, but a cruise travel insurance policyholder shall be covered for cancellation or postponement of trips.

The aim of the report is to equip relevant players in deciphering essential cues about the various real-time market based developments, also drawing significant references from historical data, to eventually present a highly effective market forecast and prediction, favoring sustainable stance and impeccable revenue flow despite challenges such as sudden pandemic, interrupted production and disrupted sales channel in the Cruise Travel Insurance market.

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Key players profiled in the report includes:

Allianz, AIG, Munich RE, Generali, Tokio Marine, Sompo Japan, CSA Travel Protection, AXA, Pingan Baoxian, Mapfre Asistencia, USI Affinity, Seven Corners, Hanse Merkur, MH Ross, STARR

Market Segmentation by type:

Market Segmentation by application:

This report is well documented to present crucial analytical review affecting the Cruise Travel Insurance market amidst COVID-19 outrage. The report is so designed to lend versatile understanding about various market influencers encompassing a thorough barrier analysis as well as an opportunity mapping that together decide the upcoming growth trajectory of the market. In the light of the lingering COVID-19 pandemic, this mindfully drafted research offering is in complete sync with the current ongoing market developments as well as challenges that together render tangible influence upon the holistic growth trajectory of the Cruise Travel Insurance market.

Besides presenting a discerning overview of the historical and current market specific developments, inclined to aid a future-ready business decision, this well-compiled research report on the Cruise Travel Insurance market also presents vital details on various industry best practices comprising SWOT and PESTEL analysis to adequately locate and maneuver profit scope. Therefore, to enable and influence a flawless market-specific business decision, aligning with the best industry practices, this specific research report on the market also lends a systematic rundown on vital growth triggering elements comprising market opportunities, persistent market obstacles and challenges, also featuring a comprehensive outlook of various drivers and threats that eventually influence the growth trajectory in the Cruise Travel Insurance market.

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Global Cruise Travel Insurance Geographical Segmentation Includes:

North America (U.S., Canada, Mexico)

Europe (U.K., France, Germany, Spain, Italy, Central & Eastern Europe, CIS)

Asia Pacific (China, Japan, South Korea, ASEAN, India, Rest of Asia Pacific)

Latin America (Brazil, Rest of L.A.)

Middle East and Africa (Turkey, GCC, Rest of Middle East)

Some Major TOC Points:

Chapter 1. Report Overview

Chapter 2. Global Growth Trends

Chapter 3. Market Share by Key Players

Chapter 4. Breakdown Data by Type and Application

Chapter 5. Market by End Users/Application

Chapter 6. COVID-19 Outbreak: Cruise Travel Insurance Industry Impact

Chapter 7. Opportunity Analysis in Covid-19 Crisis

Chapter 9. Market Driving Force

And More

In this latest research publication a thorough overview of the current market scenario has been portrayed, in a bid to aid market participants, stakeholders, research analysts, industry veterans and the like to borrow insightful cues from this ready-to-use market research report, thus influencing a definitive business discretion. The report in its subsequent sections also portrays a detailed overview of competition spectrum, profiling leading players and their mindful business decisions, influencing growth in the Cruise Travel Insurance market.

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Machine Learning and Artificial Intelligence in Healthcare Market 2021 inclining trends with NVIDIA Corporation, Intel Corporation, GENERAL ELECTRIC...

SINGAPORE -Media OutReach-5 February2021 -Despite majority of Singapore employers(89%) reporting that the COVID-19 pandemic has accelerated the adoption of cloudcomputing and Machine Learning (ML) in their companies, obstacles abound. Singaporebusiness leaders say that the largest hindrance to adopting cloud computing andML technologies is the shortage of relevant in-house IT support (64%), amongstother reasons such as 'employees do not have the relevant skill sets' (58%) and'the lack of financial resources' (46%).

alt="NTUC LearningHub Survey Reveals Accelerated Business Needs In Cloud Computing And Machine Learning Outpacing Singapore Talent Supply; Skills Gap A Hindrance To Implementing These Technologies"

These are some ofthe key findings from the recently launched NTUC LearningHub (NTUC LHUB)Industry Insights report on cloud computing and ML in Singapore. The report is basedon in-depth interviews with industry experts, such as Amazon Web Services (AWS)and NTUC LHUB, and a survey with 300 hiring managers across industries inSingapore.

While organisationsare keen to adopt cloud computing and ML to improve the company's businessperformance (64%), obtain business insights from Big Data (59%) and performmundane or tedious tasks (53%), a third of Singapore employers (32%) say theircompanies have insufficient talent to implement cloud computing and MLtechnologies.

To overcome thisshortage, companies say they have been upskilling employees that have relevantskill sets/ roles (55%), and reskilling employees that have completelydifferent skill sets/ roles (44%). In a further show of how organisations werewilling to take steps to overcome this skills gap, three in five (61%) stronglyagree or agree that they will be open to hiring individuals with relevantmicro-credentials, even if these candidates has no relevant experience oreducation degrees.

Looking to thefuture, four in five employers (81%) agree or strongly agree that ML will bethe most in-demand Artificial Intelligence (AI) skill in 2021. Meanwhile, sevenout of 10 surveyed (70%) indicated they will be willing to offer a premium fortalent with AI and ML skills.

"The report reinforces the growing demand for a cloud-skilled workforce inSingapore, and the critical need to upskill and reskill local talent", said TanLee Chew, Managing Director, ASEAN, Worldwide Public Sector, AWS. "Thecollaboration across government, businesses, education and traininginstitutions will be instrumental in helping Singapore employers address theseskills gaps. AWS will continue to collaborate with training providers like NTUCLearningHub to make skills training accessible to help Singaporeans, fromstudents to adult learners, to remain relevant today and prepare for the future."

NTUC LHUB's Head ofICT, Isa Nasser also adds, "While much of the talent demand encompasses technicalpositions such as data scientists and data engineers, businesses are alsolooking for staff to pick up practical ML and data science skills sets that canbe applied to their existing work. Thatis why in today's digital age, most professionals would benefit greatly frompicking up some data science skills to enable them to deploy ML applicationsand use cases in their organization. We highly urge workers to get started on equipping themselveswith ML skills, including understanding the core concepts of data science, aswell as familiarising themselves on the use of cloud or ML platforms such as AmazonSageMaker."

To download theIndustry Insights: Cloud Computing and ML report, visit

https://www.ntuclearninghub.com/machine-learning-cloud.

NTUCLearningHub is the leading Continuing Education and Training provider in Singapore,which aims to transform the lifelong employability of working people. Since ourcorporatisation in 2004, we have been working employers and individual learnersto provide learning solutions in areas such as Cloud, Infocomm Technology,Healthcare, Employability & Literacy, Business Excellence, Workplace Safety& Health, Security, Human Resources and Foreign Worker Training.

Todate, NTUC LearningHub has helped over 25,000 organisations and achieved over2.5 million training places across more than 500 courses with a pool of over460 certified trainers. As a Total Learning Solutions provider toorganisations, we also forge partnerships and offer a wide range of relevantend-to-end training solutions and work constantly to improve our trainingquality and delivery. In 2020, we have accelerated our foray into onlinelearning with our Virtual Live Classes and, through working with best-in-classpartners such as IBM, DuPont Sustainable Solutions and GO1, asynchronous onlinecourses.

For moreinformation, visitwww.ntuclearninghub.com.

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NTUC LearningHub Survey Reveals Accelerated Business Needs In Cloud Computing And Machine Learning Outpacing Singapore Talent Supply; Skills Gap A...

What is Machine Learning?

A useful way to introduce the machine learning methodology is by means of a comparison with the conventional engineering design flow.

This starts with an in-depth analysis of the problem domain, which culminates with the definition of a mathematical model. The mathematical model is meant to capture the key features of the problem under study and is typically the result of the work of a number of experts. The mathematical model is finally leveraged to derive hand-crafted solutions to the problem.

For instance, consider the problem of defining a chemical process to produce a given molecule. The conventional flow requires chemists to leverage their knowledge of models that predict the outcome of individual chemical reactions, in order to craft a sequence of suitable steps that synthesize the desired molecule. Another example is the design of speech translation or image/ video compression algorithms. Both of these tasks involve the definition of models and algorithms by teams of experts, such as linguists, psychologists, and signal processing practitioners, not infrequently during the course of long standardization meetings.

The engineering design flow outlined above may be too costly and inefficient for problems in which faster or less expensive solutions are desirable. The machine learning alternative is to collect large data sets, e.g., of labeled speech, images, or videos, and to use this information to train general-purpose learning machines to carry out the desired task. While the standard engineering flow relies on domain knowledge and on design optimized for the problem at hand, machine learning lets large amounts of data dictate algorithms and solutions. To this end, rather than requiring a precise model of the set-up understudy, machine learning requires the specification of an objective, of a model to be trained, and of an optimization technique.

Returning to the first example above, a machine learning approach would proceed by training a general-purpose machine to predict the outcome of known chemical reactions based on a large data set, and by then using the trained algorithm to explore ways to produce more complex molecules. In a similar manner, large data sets of images or videos would be used to train a general-purpose algorithm with the aim of obtaining compressed representations from which the original input can be recovered with some distortion.

When to Use Machine Learning?

Based on the discussion above, machine learning can offer an efficient alternative to the conventional engineering flow when development cost and time are the main concerns, or when the problem appears to be too complex to be studied in its full generality. On the flip side, the approach has the key disadvantages of providing generally suboptimal performance, or hindering interpretability of the solution, and applying only to a limited set of problems. In order to identify tasks for which machine learning methods may be useful, suggests the following criteria:1. the task involves a function that maps well-defined inputs to well-defined outputs;2. large data sets exist or can be created containing input-output pairs;3. the task provides clear feedback with clearly definable goals and metrics;4. the task does not involve long chains of logic or reasoning that depend on diverse background knowledge or common sense;5. the task does not require detailed explanations for how the decision was made;6. the task has a tolerance for error and no need for provably correct or optimal solutions;7. the phenomenon or function being learned should not change rapidly over time; and8. no specialized dexterity, physical skills, or mobility is required.

These criteria are useful guidelines for the decision of whether the machine learning methods are suitable for a given task of interest. They also offer a convenient demarcation line between machine learning as is intended today, with its focus on training and computational statistics tools, and more general notions of Artificial Intelligence (AI) based on knowledge and common sense.

In short, Machine learning is very useful and so progressive in the field of programming and topics related to computers.

See original here:
What is Machine Learning and its Uses? - Technotification

Originally published in UX Collective, Jan 19, 2021.

For an industry that prides itself on moving fast, the tech community has been remarkably slow to adapt to the differences of designing with AI. Machine learning is an intrinsically fuzzy science, yet when it inevitably returns unpredictable results, we tend to react like its a puzzle to be solved; believing that with enough algorithmic brilliance, we can eventually fit all the pieces into place and render something approaching objective truth. But objectivity and truth are often far afield from the true promise of AI, as well soon discuss.

I think a lot of the confusion stems from language;in particular the way we talk about machine-like efficiency. Machines are expected to make precise measurements about whatever theyre pointed at; to produce data.

But machinelearningdoesnt produce data. Machine learning producespredictionsabout how observations in the present overlap with patterns from the past. In this way, its literally aninversionof the classicif-this-then-thatlogic thats driven conventional software development for so long. My colleague Rick Barraza has a great way of describing the distinction:

To continue reading this article, click here.

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When Are We Going to Start Designing AI With Purpose? Machine Learning Times - The Predictive Analytics Times

The classrooms in present era are not only expanding to use more technologies and digital tools but they are also engaging in machine learning

Technology in the classroom is becoming more and more popular as we pass through the 21st century. Laptops are replacing our textbooks, and on our smart phones, we can study just about everything we want. Social media has become ubiquitous, and the way we use technology has changed the way we live our lives fully.

Technology has become the core component of distance education programs. It enhances teachers and students to digitally interconnect and exchange material and student work, retaining a human link, which is important for the growth of young minds. Enhanced connections and customized experience can allow educators torecognizeopportunities for learning skills and enhance the potential of a student.

Hence, the classrooms in present era are not only expanding to use more technologies and digital tools but they are also engaging in machine learning.

Machine learning is an artificial intelligence (AI) element, which lets machines or computers learn from all previous knowledge and make smart decisions. The architecture for machine learning involves gathering and storing a rich collection of information and turning it into a standardized knowledge base for various uses in different fields. Educators could save time in their non-classroom practices in the field of education by concentrating on machine learning.

For instance, teachers may use virtual helpers to work for their students directly from home. This form of assistance helps to boost the learning environment of students and can promote growth and educational success.

According to ODSC, Last years report by MarketWatch has revealed that Machine Learning in education will remain one of the top industries to drive investment, with the U.S. and China becoming the top key players by 2030. Major companies, like Google and IBM, are getting involved in making school education more progressive and innovative.

Analyzing all-round material

By making the content more up-to-date and applicable to an exact request, the use of machine learning in education aims to bring the online learning sector to a new stage. How? ML technologies evaluate the content of courses online and help to assess whether the quality of the knowledge presented meets the applicable criteria. On the other hand, know how users interpret the data and understand what is being explained. Users then obtain the data according to their particular preferences and expertise, and the overall learning experience increases dramatically.

Customized Learning

This is the greatest application of machine learning. It is adaptable and it takes care of individual needs. Students are able to guide their own learning through this education system. They can have theirown speed and decide what to study and how to learn. They can select the topics they are interested in, the instructor they want to learn from, and what program they want to pursue, expectations and trends.

Effective Grading

In education, there is another application of machine learning that deals with grades and scoring. Since the learning skills of a large number of students are expressed in each online course, grading them becomes a challenge. ML technology makes the grading process a few seconds problem. In this context, we talk more about the exact sciences. There are places where teachers cannot be replaced by computers, but even in such situations, they can contribute to enhance current approaches of grading and evaluation.

According to TechXplore, Researchers at University of Tbingen and Leibniz Institute fr Wissensmedien in Germany, as well as University of Colorado Boulder, have recently investigated the potential of machine-learning techniques for assessing student engagement in the context of classroom research. More specifically, they devised a deep-neural-network-based architecture that can estimate student engagement by analyzing video footage collected in classroom environments.

They also mentioned that, We used camera data collected during lessons to teach a deep-neural-network-based model to predict student engagement levels, Enkelejda Kasneci the leading HCI researcher in the multidisciplinary team that carried out the study, told TechXplore. We trained our model on ground-truth data (e.g., expert ratings of students level of engagement based on the videos recorded in the classroom). After this training, the model was able to predict, for instance, whether data obtained from a particular student at a particular point in time indicates high or low levels of engagement.

See more here:
Can Machine Learning be the Best Remedy in the Education Sector? - Analytics Insight

As financial services firms increasingly turn to artificial intelligence (AI), banking regulators warn that despite their astonishing capabilities, these tools must be relied upon with caution.

Last week, the Board of Governors of the Federal Reserve (the Fed) held a virtual AI Academic Symposium to explore the application of AI in the financial services industry. Governor Lael Brainard explained that particularly as financial services become more digitized and shift to web-based platforms, a steadily growing number of financial institutions have relied on machine learning to detect fraud, evaluate credit, and aid in operational risk management, among many other functions.[i]

In the AI world, machine learning refers to a model that processes complex data sets and automatically recognizes patterns and relationships, which are in turn used to make predictions and draw conclusions.[ii] Alternative data is information that is not traditionally used in a particular decision-making process but that populates machine learning algorithms in AI-based systems and thus fuels their outputs.[iii]

Machine learning and alternative data have special utility in the consumer lending context, where these AI applications allow financial firms to determine the creditworthiness of prospective borrowers who lack credit history.[iv] Using alternative data such as the consumers education, job function, property ownership, address stability, rent payment history, and even internet browser history and behavioral informationamong many other datafinancial institutions aim to expand the availability of affordable credit to so-called credit invisibles or unscorables.[v]

Yet, as Brainard cautioned last week, machine-learning AI models can be so complex that even their developers lack visibility into how the models actually classify and process what could amount to thousands of nonlinear data elements.[vi] This obscuring of AI models internal logic, known as the black box problem, raises questions about the reliability and ethics of AI decision-making.[vii]

When using AI machine learning to evaluate access to credit, the opaque and complex data interactions relied upon by AI could result in discrimination by race, or even lead to digital redlining, if not intentionally designed to address this risk.[viii] This can happen, for example, when intricate data interactions containing historical information such as educational background and internet browsing habits become proxies for race, gender, and other protected characteristicsleading to biased algorithms that discriminate.[ix]

Consumer protection laws, among other aspects of the existing regulatory framework, cover AI-related credit decision-making activities to some extent. Still, in light of the rising complexity of AI systems and their potentially inequitable consequences, AI-focused legal reforms may be needed. At this time, to help ensure that financial services are prepared to manage these risks, the Fed has called on stakeholdersfrom financial services firms to consumer advocates and civil rights organizations as well as other businesses and the general publicto provide input on responsible AI use.[x]

[i] Lael Brainard, Governor, Bd. of Governors of the Fed. Reserve Sys., AI Academic Symposium: Supporting Responsible Use of AI and Equitable Outcomes in Financial Services (Jan. 12, 2021), available at https://www.federalreserve.gov/newsevents/speech/brainard20210112a.htm.

[ii] Pratin Vallabhaneni and Margaux Curie, Leveraging AI and Alternative Data in Credit Underwriting: Fair Lending Considerations for Fintechs, 23 No. 4 Fintech L. Rep. NL 1 (2020).

[iii] Id.

[iv] Id.; Brainard, supra n. 1.

[v] Vallabhaneni and Margaux Curie, supra n.2; Kathleen Ryan, The Big Brain in the Black Box, Am. Bar Assoc. (May 2020), https://bankingjournal.aba.com/2020/05/the-big-brain-in-the-black-box/.

[vi] Brainard, supra n.1; Ryan, supra n.5.

[vii] Brainard, supra n.1; Ryan, supra n.5.

[viii] Brainard, supra n.1.

[ix] Id. (citing Carol A. Evans and Westra Miller, From Catalogs to Clicks: The Fair Lending Implications of Targeted, Internet Marketing, Consumer Compliance Outlook (2019)).

[x] Id.

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AI in Credit Decision-Making Is Promising, but Beware of Hidden Biases, Fed Warns - JD Supra

Cloud Machine Learning Market research report is the new statistical data source added by A2Z Market Research.

Cloud Machine Learning Market is growing at a High CAGR during the forecast period 2021-2027. The increasing interest of the individuals in this industry is that the major reason for the expansion of this market.

Cloud Machine Learning Market research is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors. Business strategies of the key players and the new entering market industries are studied in detail. Well explained SWOT analysis, revenue share and contact information are shared in this report analysis.

Get the PDF Sample Copy (Including FULL TOC, Graphs and Tables) of this report @:

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Note In order to provide more accurate market forecast, all our reports will be updated before delivery by considering the impact of COVID-19.

Top Key Players Profiled in this report are:

Amazon, Oracle Corporation, IBM, Microsoft Corporation, Google Inc., Salesforce.Com, .

The key questions answered in this report:

Various factors are responsible for the markets growth trajectory, which are studied at length in the report. In addition, the report lists down the restraints that are posing threat to the global Cloud Machine Learning market. It also gauges the bargaining power of suppliers and buyers, threat from new entrants and product substitute, and the degree of competition prevailing in the market. The influence of the latest government guidelines is also analyzed in detail in the report. It studies the Cloud Machine Learning markets trajectory between forecast periods.

Regions Covered in the Global Cloud Machine Learning Market Report 2021: The Middle East and Africa (GCC Countries and Egypt) North America (the United States, Mexico, and Canada) South America (Brazil etc.) Europe (Turkey, Germany, Russia UK, Italy, France, etc.) Asia-Pacific (Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia)

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The cost analysis of the Global Cloud Machine Learning Market has been performed while keeping in view manufacturing expenses, labor cost, and raw materials and their market concentration rate, suppliers, and price trend. Other factors such as Supply chain, downstream buyers, and sourcing strategy have been assessed to provide a complete and in-depth view of the market. Buyers of the report will also be exposed to a study on market positioning with factors such as target client, brand strategy, and price strategy taken into consideration.

The report provides insights on the following pointers:

Market Penetration: Comprehensive information on the product portfolios of the top players in the Cloud Machine Learning market.

Product Development/Innovation: Detailed insights on the upcoming technologies, R&D activities, and product launches in the market.

Competitive Assessment: In-depth assessment of the market strategies, geographic and business segments of the leading players in the market.

Market Development: Comprehensive information about emerging markets. This report analyzes the market for various segments across geographies.

Market Diversification: Exhaustive information about new products, untapped geographies, recent developments, and investments in the Cloud Machine Learning market.

Table of Contents

Global Cloud Machine Learning Market Research Report 2021 2027

Chapter 1 Cloud Machine Learning Market Overview

Chapter 2 Global Economic Impact on Industry

Chapter 3 Global Market Competition by Manufacturers

Chapter 4 Global Production, Revenue (Value) by Region

Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions

Chapter 6 Global Production, Revenue (Value), Price Trend by Type

Chapter 7 Global Market Analysis by Application

Chapter 8 Manufacturing Cost Analysis

Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter 10 Marketing Strategy Analysis, Distributors/Traders

Chapter 11 Market Effect Factors Analysis

Chapter 12 Global Cloud Machine Learning Market Forecast

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Comprehensive Report on Cloud Machine Learning Market 2021 | Trends, Growth Demand, Opportunities & Forecast To 2027 |Amazon, Oracle Corporation,...

If the portfolio of a corporate venture capital firm can be taken as a signal for the strategic priorities of their parent companies, then National Grid has high hopes for automation as the future of the utility industry.

The heavy emphasis on automation and machine learning from one of the nations largest privately held utilities with a customer base numbering around 20 million people is significant. And a sign of where the industry could be going.

Since its launch, National Grids venture firm, National Grid Partners, has invested in 16 startups that featured machine learning at the core of their pitch. Most recently, the company backed AI Dash, which uses machine learning algorithms to analyze satellite images and infer the encroachment of vegetation on National Grid power lines to avoid outages.

Another recent investment, Aperio, uses data from sensors monitoring critical infrastructure to predict loss of data quality from degradation or cyberattacks.

Indeed, of the $175 million in investments the firm has made, roughly $135 million has been committed to companies leveraging machine learning for their services.

AI will be critical for the energy industry to achieve aggressive decarbonization and decentralization goals, said Lisa Lambert, the chief technology and innovation officer at National Grid and the founder and president of National Grid Partners.

National Grid started the year off slowly because of the COVID-19 epidemic, but the pace of its investments picked up and the company is on track to hit its investment targets for the year, Lambert said.

Modernization is critical for an industry that still mostly runs on spreadsheets and collective knowledge that has locked in an aging employee base, with no contingency plans in the event of retirement, Lambert said. Its that situation thats compelling National Grid and other utilities to automate more of their business.

Most companies in the utility sector are trying to automate now for efficiency reasons and cost reasons. Today, most companies have everything written down in manuals; as an industry, we basically still run our networks off spreadsheets, and the skills and experience of the people who run the networks. So weve got serious issues if those people retire. Automating [and] digitizing is top of mind for all the utilities weve talked to in the Next Grid Alliance.

To date, a lot of the automation work thats been done has been around basic automation of business processes. But there are new capabilities on the horizon that will push the automation of different activities up the value chain, Lambert said.

ML is the next level predictive maintenance of your assets, delivering for the customer. Uniphore, for example: youre learning from every interaction you have with your customer, incorporating that into the algorithm, and the next time you meet a customer, youre going to do better. So thats the next generation, Lambert said. Once everything is digital, youre learning from those engagements whether engaging an asset or a human being.

Lambert sees another source of demand for new machine learning tech in the need for utilities to rapidly decarbonize. The move away from fossil fuels will necessitate entirely new ways of operating and managing a power grid. One where humans are less likely to be in the loop.

In the next five years, utilities have to get automation and analytics right if theyre going to have any chance at a net-zero world youre going to need to run those assets differently, said Lambert. Windmills and solar panels are not [part of] traditional distribution networks. A lot of traditional engineers probably dont think about the need to innovate, because theyre building out the engineering technology that was relevant when assets were built decades ago whereas all these renewable assets have been built in the era of OT/IT.

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National Grid sees machine learning as the brains behind the utility business of the future - TechCrunch

The development of connected and autonomous vehicles (CAVs) is technology-driven and data-centric. Zenzics Roadmap to 2030 highlights that 'the intelligence of self-driving vehicles is driven by advanced features such as artificial intelligence (AI) or machine learning (ML) techniques'.[1] Developers of connected and automated mobility (CAM) technologies are engineering advances in machine learning and machine analysis techniques that can create valuable, potentially life-saving, insights from the massive well of data that is being generated.

Diego Black and Lucy Pegler take a look at the legal and regulatory issues involved in protecting data and innovations in CAVs.

The data of driving

It is predicted that the average driverless car will produce around 4TB of data per day, including data on traffic, route choices, passenger preferences, vehicle performance and many more data points[2].

'Data is foundational to emerging CAM technologies, products and services driving their safety, operation and connectivity'.[3]

As Burges Salmon and AXA UK outlined in their joint report as part of FLOURISH, an Innovate UK-funded CAV project, the data produced by CAVs can be broadly divided into a number of categories based on its characteristics. For example, sensitive commercial data, commercial data, personal data. How data should be protected will depend on its characteristics and importantly, the purposes for which it is used. The use of personal data (i.e. data from which an individual can be identified) attracts particular consideration.

The importance of data to the CAM industry and, in particular, the need to share data effectively to enable the deployment and operation of CAM, needs to be balanced against data protection considerations. In 2018, the Open Data Institute (ODI) published a report setting out that it considered that all journey data is personal data[4] consequently bringing journey data within the scope of the General Data Protection Regulation.[5]

Additionally, the European Data Protection Board (EDPB) has confirmed that the ePrivacy directive (2002/58/EC as revised by 2009/136/EC) applies to connected vehicles by virtue of 'the connected vehicle and every device connected to it [being] considered as a 'terminal equipment'.'[6] This means that any machine learning innovations deployed in CAVs will inevitably process vast amounts of personal data. The UK Information Commissioners Office has issued guidance on how to best deal with harnessing both big data and AI in relation to personal data, including emphasising the need for industry to deploy ethical principles, create ethics boards to monitor the new uses of data and ensure that machine learning algorithms are auditable.[7]

Navigating the legal frameworks that apply to the use of data is complex and whilst the EDPB has confirmed its position in relation to connected vehicles, automated vehicles and their potential use cases raise an entirely different set of considerations. Whilst the market is developing rapidly, use case scenarios for automated mobility will focus on how people consume services. Demand responsive transport and ride sharing are likely to play a huge role in the future of personal mobility.

The main issue policy makers now face is the ever evolving nature of the technology. As new, potentially unforeseen, technologies are integrated into CAVs, the industry will require both a stringent data protection framework on the one hand, and flexibility and accessibility on the other hand. These two policy goals are necessarily at odds with one another, and the industry will need to take a realistic, privacy by design approach to future development, working with rather than against regulators.

Whilst the GDPR and ePrivacy Directive will likely form the building blocks of future regulation of CAV data, we anticipate the development of a complementary framework of regulation and standards that recognises the unique applications of CAM technologies and the use of data.

Cyber security

The prolific and regular nature of cyber-attacks poses risks to both public acceptance of CAV technology and to the underlying business interests of organisations involved in the CAV ecosystem.

New technologies can present threat to existing cyber security measures. Tarquin Folliss of Reliance acsn highlights this noting that 'a CAVs mix of operational and information technology will produce systems complex to monitor, where intrusive endpoint monitoring might disrupt inadvertently the technology underpinning safety'. The threat is even more acute when thinking about CAVs in action and as Tarquin notes, the ability for 'malign actors to target a CAV network in the same way they target other critical national infrastructure networks and utilities, in order to disrupt'.

In 2017, the government announced 8 Key principles of Cyber Security for Connected and Automated Vehicles. This, alongside the DCMS IoT code of practice, the CCAVs CAV code of practice and the BSIs PAS 1885, provides a good starting point for CAV manufacturers. Best practices include:

Work continues at pace on cyber security for CAM. In May this year, Zenzic published its Cyber Resilience in Connected and Automated Mobility (CAM) Cyber Feasibility Report which sets out the findings of seven projects tasked with providing a clear picture of the challenges and potential solutions in ensuring digital resilience and cyber security within CAM.

Demonstrating the pace of work in the sector, in June 2020 the United Nations Economic Commission for Europe (UNECE) published two new UN Regulations focused on cyber security in the automotive sector. The Regulations represent another step-change in the approach to managing the significant cyber risk of an increasingly connected automotive sector.

Protecting innovation

As innovation in the CAV sector increases, issues regarding intellectual property and its protection and exploitation become more important. Companies that historically were not involved in the automotive sector are now rapidly becoming key partners providing expertise in technologies such as IT security, telecoms, block chain and machine learning. In autonomous vehicles many of the biggest patent filers in this area have software and telecoms backgrounds[8].

With the increasing use of in and inter-car connectivity and the accumulative amount of data having to be handled per second as levels of autonomy rises, innovators in the CAV space are having to handle issues regarding data security as well as determining how best to handle the large data sets. Furthermore, the recent UK government call for evidence on automated lane keeping systems is being seen by many as the first step of standards being introduced in autonomous vehicles.

In view of these developments new challenges are now being faced by companies looking to benefit from their innovations. Unlike more traditional automotive innovation where the innovations lay in improvements to engineering and machinery many of the innovations in the CAV space reside in electronics and software development. The ability to protect and exploit inventions in the software space has become increasingly of relevance in the automotive industry.

Multiple Intellectual Property rights exist that can be used to protect innovations in CAVs. Some rights can be particularly effective in areas of technology where standards exist, or are likely to exist. Two of the main ways seen at present are through the use of patents and trade secrets. Both can be used in combination, or separately, to provide an effective IP strategy. Such an approach is seen in other industries such as those involved in data security.

For companies that are developing or improving machine learning models, or training sets, the use of trade secrets is particularly common. Companies relying on trade secrets may often license access to, or sell the outputs of, their innovations. Advantageously, trade secrets are free and last indefinitely.

An effective strategy in such fields is to obtain patents that cover the technological standard. By definition if a third party were to adhere to the defined standard, they would necessarily fall within the scope of the patent, thus providing the owner of the patent with a potential revenue stream through licensing agreements. If, as anticipated, standards will be set in CAVs any company that can obtain patents to cover the likely standard will be at an advantage. Such licenses are typically offered under a fair, reasonable and non-discriminatory (FRAND) basis, to ensure that companies are not prevented by patent holders from entering the market.

A key consideration is that the use of trade secrets may be incompatible with the use of standards. If technology standards are introduced for autonomous vehicles, in order to comply with the standards companies would have to demonstrate that their technology complies with the standard. The use of trade secrets may be incompatible with the need to demonstrate compliance with a standard.

However, whilst a patent provides a stronger form of protection in order to enforce a patent the owner must be able to demonstrate a third party is performing the acts as defined in the patent. In the case of machine learning and mathematical-based methods such information is often kept hidden making providing infringement difficult. As a result patents in such areas are often directed towards a visible, or tangible, output. For example in CAVs this may be the control of a vehicle based on the improvements in the machine learning. Due to the difficulty in demonstrating infringement, many companies are choosing to protect their innovations with a mixture of trade secrets and patents.

Legal protections for innovations

For the innovations typically seen in the software side of CAVs, trade secrets and patents are the two main forms of protection.

Trade secrets are, as the name implies, where a company will keep all, or part of, their innovation a secret. In software-based inventions this may be in form of a black-box disclosure where the workings and functionality of the software are kept secret. However, steps do need to be taken to keep the innovation secret, and they do not prevent a third party from independently implementing, or reverse engineering, the innovation. Furthermore, once a trade secret is made public, the value associated with the trade secret is gone.

Patents are an exclusive right, lasting up to 20 years, which allow the holder to prevent, or request a license from, a third party utilising the technology that is covered by the scope of the patent in that territory. Therefore it is not possible to enforce say, a US patent in the UK. Unlike trade secrets publication of patents is an important part of the process.

In order for inventions to be patented they must be new (that is to say they have not been disclosed anywhere in the world before), inventive (not run-of-the-mill improvements), and concern non-excluded subject matter. The exclusions in the UK and Europe cover software, and mathematical methods, amongst other fields, as such. In the case of CAVs a large number of inventions are developed that could fall in the software and mathematical methods categories.

The test regarding whether or not an invention may be seen as excluded subject matter varies between jurisdictions. In Europe if an invention is seen to solve a technical problem, for example relating to the control of vehicles it would be deemed allowable. Many of the innovations in CAVs can be tied to technical problems relating to, for example, the control of vehicles or improvements in data security. As such on the whole CAV inventions may escape the exclusions.

What does the future hold?

Technology is advancing at a rapid rate. At the same time as industry develops more and more sophisticated software to harness data, bad actors gain access to more advanced tools. To combat these increased threats, CAV manufacturers need to be putting in place flexible frameworks to review and audit their uses of data now, looking toward the developments of tomorrow to assess the data security measures they have today. They should also be looking to protect some of their most valuable IP assets from the outset, including machine learning developments in a way that is secure and enforceable.

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Connected and autonomous vehicles: Protecting data and machine learning innovations - Lexology

AWS's AmazonSageMaker software, a set of tools for deploying machine learning, is not only spreading throughout many companies, it is becoming a key tool for some of the more demanding kinds of practitioners of machine learning, one of the executives in charge of it says.

"We are seeing very, very sophisticated practitioners moving to SageMaker because we take care of the infrastructure, and so it makes them an order-of-magnitude more productive," said Bratin Saha, AWS's vice president in charge of machine learning and engines.

Saha spoke with ZDNet during the third week of AWS's annual re:Invent conference, which this year was held virtually because of the pandemic.

The benefits of SageMaker have to do with all the details of how to stage training tasks and deploy inference tasks across a variety of infrastructure.

SageMaker, introduced in 2017, can automate a lot of the grunt work that goes into setting up and running such tasks.

"Amazon dot com has invested in machine learning for more than twenty years, and they are moving on to SageMaker, and we have very sophisticated machine learning going on at Amazon dot com," says Amazon AWS's vice president for ML and engines, Bratin Saha.

While SageMaker might seem like something that automates machine learning for people who don't know how to do the basics, Saha told ZDNet that even experienced machine learning scientists find value in speeding up the routine tasks in a program's development.

"What they had to do up till now is spin up a cluster, make sure that the cluster was well utilized, spend a lot of time checking as the model is deployed, am I getting traffic spikes," said Saha, describing the traditional deployment tasks that had to be carried out by a machine learning data scientist. That workflow extends from initially gathering the data to labeling the data (in the case of labeled training), refine the model architecture, and then deploying trained models for inference usage and monitoring and maintaining those inference models as long as they are running live.

"You don't have to do any of that now," said Saha. "SageMaker gives you training that is server-less, in the sense that your billing starts when your model starts training, and stops when your model stops training."

Also: Amazon AWS unveils RedShift ML to 'bring machine learning to more builders'

Added Saha, "In addition, it works with spotinstances in a very transparent way; you don't have to say, Hey, have my spot instances been pre-empted, is my job getting killed, SageMaker takes care of all of that." Such effective staging of jobs can reduce costs by ninety percent, Saha contends.

Saha said that customers such as Lyft and Intuit, despite having machine learning capabilities of their own, are more and more taking up the software to streamline their production systems.

"We have some of the most sophisticated customers working on SageMaker," said Saha.

"Look at Lyft, they are standardizing their training on SageMaker, their training times have come down from several days to a few hours," said Saha. "MobileEye is using SageMaker training," he said, referring to the autonomous vehicle chip unit within Intel. "Intuit has been able to reduce their training time from six months to a few days." Other customers include the NFL, JP Morgan Chase, Georgia Pacific, Saha noted.

Also: Amazon AWS analytics director sees analysis spreading much more widely throughout organizations

Amazon itself has moved its AI work internally to SageMaker, he said. "Amazon dot com has invested in machine learning for more than twenty years, and they are moving on to SageMaker, and we have very sophisticated machine learning going on at Amazon dot com." As one example, Amazon's Alexa voice-activated appliance uses SageMaker Neo, an optimization tool that compiles trained models into a binary program with settings that will make the model run most efficiently when being used for inference tasks.

There are numerous other parts of SageMaker, such as pre-built containers with select machine learning algorithms; a "Feature Store" where one can pick out attributes to use in training; and what's known as the Data Wrangler to create original model features from training data.

AWS has been steadily adding to the tool set.

During his AWS re:Invent keynote two weeks ago, Amazon's vice president of machine learning, Swami Sivasubramanian, announced that SageMaker can now automatically break up the parts of a large neural net and distribute those parts across multiple computers. This form of parallel computing, known as model parallelism, is usually something that takes substantial effort.

Amazon was able to reduce neural network training time by forty percent, said Sivasubramanian, for very large deep learning networks, such as "T5," a version of Google's Transformer natural language processing.

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Amazon AWS says Very, very sophisticated practitioners of machine learning are moving to SageMaker - ZDNet

This article is part ofAI education, a series of posts that review and explore educational content on data science and machine learning. (In partnership withPaperspace)

Machine learning and deep learning have become an important part of many applications we use every day. There are few domains that the fast expansion of machine learning hasnt touched. Many businesses have thrived by developing the right strategy to integrate machine learning algorithms into their operations and processes. Others have lost ground to competitors after ignoring the undeniable advances in artificial intelligence.

But mastering machine learning is a difficult process. You need to start with a solid knowledge of linear algebra and calculus, master a programming language such as Python, and become proficient with data science and machine learning libraries such as Numpy, Scikit-learn, TensorFlow, and PyTorch.

And if you want to create machine learning systems that integrate and scale, youll have to learn cloud platforms such as Amazon AWS, Microsoft Azure, and Google Cloud.

Naturally, not everyone needs to become a machine learning engineer. But almost everyone who is running a business or organization that systematically collects and processes can benefit from some knowledge of data science and machine learning. Fortunately, there are several courses that provide a high-level overview of machine learning and deep learning without going too deep into math and coding.

But in my experience, a good understanding of data science and machine learning requires some hands-on experience with algorithms. In this regard, a very valuable and often-overlooked tool is Microsoft Excel.

To most people, MS Excel is a spreadsheet application that stores data in tabular format and performs very basic mathematical operations. But in reality, Excel is a powerful computation tool that can solve complicated problems. Excel also has many features that allow you to create machine learning models directly into your workbooks.

While Ive been using Excels mathematical tools for years, I didnt come to appreciate its use for learning and applying data science and machine learning until I picked up Learn Data Mining Through Excel: A Step-by-Step Approach for Understanding Machine Learning Methods by Hong Zhou.

Learn Data Mining Through Excel takes you through the basics of machine learning step by step and shows how you can implement many algorithms using basic Excel functions and a few of the applications advanced tools.

While Excel will in no way replace Python machine learning, it is a great window to learn the basics of AI and solve many basic problems without writing a line of code.

Linear regression is a simple machine learning algorithm that has many uses for analyzing data and predicting outcomes. Linear regression is especially useful when your data is neatly arranged in tabular format. Excel has several features that enable you to create regression models from tabular data in your spreadsheets.

One of the most intuitive is the data chart tool, which is a powerful data visualization feature. For instance, the scatter plot chart displays the values of your data on a cartesian plane. But in addition to showing the distribution of your data, Excels chart tool can create a machine learning model that can predict the changes in the values of your data. The feature, called Trendline, creates a regression model from your data. You can set the trendline to one of several regression algorithms, including linear, polynomial, logarithmic, and exponential. You can also configure the chart to display the parameters of your machine learning model, which you can use to predict the outcome of new observations.

You can add several trendlines to the same chart. This makes it easy to quickly test and compare the performance of different machine learning models on your data.

In addition to exploring the chart tool, Learn Data Mining Through Excel takes you through several other procedures that can help develop more advanced regression models. These include formulas such as LINEST and LINREG formulas, which calculate the parameters of your machine learning models based on your training data.

The author also takes you through the step-by-step creation of linear regression models using Excels basic formulas such as SUM and SUMPRODUCT. This is a recurring theme in the book: Youll see the mathematical formula of a machine learning model, learn the basic reasoning behind it, and create it step by step by combining values and formulas in several cells and cell arrays.

While this might not be the most efficient way to do production-level data science work, it is certainly a very good way to learn the workings of machine learning algorithms.

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Beyond regression models, you can use Excel for other machine learning algorithms. Learn Data Mining Through Excel provides a rich roster of supervised and unsupervised machine learning algorithms, including k-means clustering, k-nearest neighbor, nave Bayes classification, and decision trees.

The process can get a bit convoluted at times, but if you stay on track, the logic will easily fall in place. For instance, in the k-means clustering chapter, youll get to use a vast array of Excel formulas and features (INDEX, IF, AVERAGEIF, ADDRESS, and many others) across several worksheets to calculate cluster centers and refine them. This is not a very efficient way to do clustering, youll be able to track and study your clusters as they become refined in every consecutive sheet. From an educational standpoint, the experience is very different from programming books where you provide a machine learning library function your data points and it outputs the clusters and their properties.

In the decision tree chapter, you will go through the process calculating entropy and selecting features for each branch of your machine learning model. Again, the process is slow and manual, but seeing under the hood of the machine learning algorithm is a rewarding experience.

In many of the books chapters, youll use the Solver tool to minimize your loss function. This is where youll see the limits of Excel, because even a simple model with a dozen parameters can slow your computer down to a crawl, especially if your data sample is several hundred rows in size. But the Solver is an especially powerful tool when you want to finetune the parameters of your machine learning model.

Learn Data Mining Through Excel shows that Excel can even advanced machine learning algorithms. Theres a chapter that delves into the meticulous creation of deep learning models. First, youll create a single layer artificial neural network with less than a dozen parameters. Then youll expand on the concept to create a deep learning model with hidden layers. The computation is very slow and inefficient, but it works, and the components are the same: cell values, formulas, and the powerful Solver tool.

In the last chapter, youll create a rudimentary natural language processing (NLP) application, using Excel to create a sentiment analysis machine learning model. Youll use formulas to create a bag of words model, preprocess and tokenize hotel reviews and classify them based on the density of positive and negative keywords. In the process youll learn quite a bit about how contemporary AI deals with language and how much different it is from how we humans process written and spoken language.

Whether youre making C-level decisions at your company, working in human resources, or managing supply chains and manufacturing facilities, a basic knowledge of machine learning will be important if you will be working with data scientists and AI people. Likewise, if youre a reporter covering AI news or a PR agency working on behalf a company that uses machine learning, writing about the technology without knowing how it works is a bad idea (I will write a separate post about the many awful AI pitches I receive every day). In my opinion, Learn Data Mining Through Excel is a smooth and quick read that will help you gain that important knowledge.

Beyond learning the basics, Excel can be a powerful addition to your repertoire of machine learning tools. While its not good for dealing with big data sets and complicated algorithms, it can help with the visualization and analysis of smaller batches of data. The results you obtain from a quick Excel mining can provide pertinent insights in choosing the right direction and machine learning algorithm to tackle the problem at hand.

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CHICAGO, Dec. 13, 2020 /PRNewswire/ -- Artificial intelligence (AI) has the potential to revolutionize healthcare, but integrating AI-based techniques into routine medical practice has proven to be a significant challenge. A plenary session at the virtual 2020 AACC Annual Scientific Meeting & Clinical Lab Expo will explore how one clinical lab overcame this challenge to implement a machine learning-based test, while a second session will take a big picture look at what machine learning is and how it could transform medicine.

Machine learning is a type of AI that uses statistics to find patterns in massive amounts of data. It could launch healthcare into a new era by mining medical data to find cures for diseases, identify vulnerable patients before they become ill, and better personalize testing and treatments. In spite of this technology's promise, though, the medical community continues to grapple with numerous barriers to adoption, and in the field of laboratory medicine in particular, very few machine learning tests are currently offered as part of regular care.

A 10-year machine learning project undertaken by Ulysses G.J. Balis, MD, and his colleagues at the University of Michigan in Ann Arbor could help to change this by providing a blueprint for other healthcare institutions looking to harness AI. As Dr. Balis will discuss in his plenary session, his institute developed and implemented a machine learning test called ThioMon to guide treatment of inflammatory bowel disease (IBD) with azathioprine. With an approximate cost of only $20 a month, azathioprine is much cheaper than other IBD medications (which can cost thousands of dollars a month), but its dosage needs to be finetuned for each patient, making it difficult to prescribe. ThioMon solves this issue by analyzing a patient's routine lab test results to determine if a particular dose of azathioprine is working or not.

Balis's team found that the test performs just as well as a colonoscopy, which is the current gold standard for assessing IBD patient response to medication. Even more exciting is that clinical labs could use ThioMon's general approachanalyzing routine lab test results with machine learning algorithmsto solve any number of other patient care challenges.

"There are dozens, if not hundreds of additional diagnoses that we can extract from the routine lab values that we've been generating for decades," said Dr. Balis. "This lab data is, in essence, a gold mine, and the development of these machine learning tools marks the start of a new gold rush."

One of the additional conditions that this machine learning approach can diagnose is, in fact, COVID-19. In the session, "How Clinical Laboratory Data Is Impacting the Future of Healthcare?" Jonathan Chen, MD, PhD, of Stanford University, and Christopher McCudden, PhD, of the Eastern Ontario Regional Laboratory Association, will touch on a new machine learning test that analyzes routine lab test results to determine if patients have COVID-19 even before their SARS-CoV-2 test results come back. As COVID-19 cases in the U.S. reach record highs, this test could enable labs to diagnose COVID-19 patients quickly even if SARS-CoV-2 test supply shortages worsen or if SARS-CoV-2 test results become backlogged due to demand.

Beyond this, Drs. Chen and McCudden plan to give a bird's eye view of what machine learning is, how it works, and how it can improve efficiency, reduce costs, and improve patient outcomesparticularly by democratizing patient access to medical expertise.

"Medical expertise is the scarcest resource in the healthcare system," said Dr. Chen, "and computational, automated tools will allow us to reach the tens of millions of people in the U.S.and the billions of people worldwidewho currently don't have access to it."

Machine Learning Sessions at the 2020 AACC Annual Scientific MeetingAACC Annual Scientific Meeting registration is free for members of the media. Reporters can register online here:https://www.xpressreg.net/register/aacc0720/media/landing.asp

Session 14001: Between Scylla and Charybdis: Navigating the Complex Waters of Machine Learning in Laboratory Medicine

Session 34104: How Clinical Laboratory Data Is Impacting the Future of Healthcare?

Abstract A-005: Machine Learning Outperforms Traditional Screening and Diagnostic Tools for the Detection of Familial Hypercholesterolemia

About the 2020 AACC Annual Scientific Meeting & Clinical Lab ExpoThe AACC Annual Scientific Meeting offers 5 days packed with opportunities to learn about exciting science from December 13-17, all available on an online platform. This year, there is a concerted focus on the latest updates on testing for COVID-19, including a talk with current White House Coronavirus Task Force testing czar, Admiral Brett Giroir. Plenary sessions include discussions on using artificial intelligence and machine learning to improve patient outcomes, new therapies for cancer, creating cross-functional diagnostic management teams, and accelerating health research and medical breakthroughs through the use of precision medicine.

At the virtual AACC Clinical Lab Expo, more than 170 exhibitors will fill the digital floor with displays and vital information about the latest diagnostic technology, including but not limited to SARS-CoV-2 testing, mobile health, molecular diagnostics, mass spectrometry, point-of-care, and automation.

About AACCDedicated to achieving better health through laboratory medicine, AACC brings together more than 50,000 clinical laboratory professionals, physicians, research scientists, and business leaders from around the world focused on clinical chemistry, molecular diagnostics, mass spectrometry, translational medicine, lab management, and other areas of progressing laboratory science. Since 1948, AACC has worked to advance the common interests of the field, providing programs that advance scientific collaboration, knowledge, expertise, and innovation. For more information, visit http://www.aacc.org.

Christine DeLongAACCSenior Manager, Communications & PR(p) 202.835.8722[emailprotected]

Molly PolenAACCSenior Director, Communications & PR(p) 202.420.7612(c) 703.598.0472[emailprotected]

SOURCE AACC

http://www.aacc.org

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Artificial Intelligence Advances Showcased at the Virtual 2020 AACC Annual Scientific Meeting Could Help to Integrate This Technology Into Everyday...

Advances in innovation to capture and process a lot of data have left us suffocating in information. This makes it hard to extricate insights from data at the rate we get it. This is the place where machine learning offers some benefit to a digital business.

We need strategies to improve machine learning performance all the more effectively. Since, supposing that we put forth efforts in the wrong direction, we cant get a lot of progress and burn through a lot of time. Then, we need to get a few expectations toward the path we picked, for instance, how much precision can be improved.

There are by and large two kinds of organizations that participate in machine learning: those that build applications with a trained ML model inside as their core business proposition and those that apply ML to upgrade existing business work processes. In the latter case, articulating the issue will be the underlying challenge. Diminishing the expense or increasing income should be limited to the moment that it gets solvable by gaining the right data.

For example, if you need to minimize the churn rate, data may assist you with detecting clients with a high fly risk by analyzing their activities on a website, a SaaS application, or even online media. In spite of the fact that you can depend on traditional metrics and make suppositions, the algorithm may unwind shrouded dependencies between the data in clients profiles and the probability to leave.

Resource management has become a significant part of a data scientists duties. For instance, it is a challenge having a GPU worker on-prem for a group of five data scientists. A lot of time is spent sorting out some way to share those GPUs simply and effectively. Allocation of compute resources for machine learning can be a major agony, and takes time away from doing data science tasks.

Data science is an expansive field of practices pointed toward removing significant insights from data in any structure. Furthermore, utilizing data science in decision-making is a better method to stay away from bias. Nonetheless, that might be trickier than you might suspect. Indeed, even Google has as of late fallen into a trap of indicating more esteemed jobs to men in their ads than to women. Clearly, it isnt so much that Google data scientists are sexist, but instead the data that the algorithm utilizes is one-sided on the grounds that it was gathered from our interactions on the web.

Machine learning is compute-intensive. A scalable machine learning foundation should be compute agnostic. Joining public clouds, private clouds, and on-premise resources offers flexibility and agility as far as running AI workloads. Since the kinds of workloads shift significantly between AI workloads, companies that construct a hybrid cloud infrastructure can dispense assets all the more deftly in custom sizes. You can bring down CapEx expenditure with public cloud, and offer the scalability required for times of high compute demands. In companies with strict security demands, the expansion of private cloud is essential, and can bring down OpEx over the long-term. Hybrid cloud encourages you to accomplish the control and flexibility necessary to improve planning of resources.

A large portion of the models are created on a static subset of information, and they capture the conditions of the time frame when the data was gathered. When you have a model or various them deployed, they become dated over time and give less exact expectations. Contingent upon how effectively the patterns in your business climate change, you should pretty much regularly replace models or retrain them

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Improve Machine Learning Performance with These 5 Strategies - Analytics Insight

Given the growing ubiquity of machine learning and artificial intelligence in healthcare settings, it's become increasingly important to meet patient needs and engage users.

And as panelists noted during a HIMSS Machine Learning and AI for Healthcare Forum session this week, designing technology with the user in mind is a vital way to ensure tools become an integral part of workflow.

"Big Tech has stumbled somewhat" in this regard, said Bill Fox, healthcare and life sciences lead at SambaNova Systems. "The patients, the providers they don't really care that much about the technology, how cool it is, what it can do from a technological standpoint.

"It really has to work for them," Fox added.

Jai Nahar, a pediatric cardiologist at Children's National Hospital, agreed, stressing the importance of human-centered AI design in healthcare delivery.

"Whenever we're trying to roll out a productive solution that incorporates AI," he said, "right from the designing [stage] of the product or service itself, the patients should be involved."

That inclusion should also expand to provider users too, he said: "Before rolling out any product or service, we should involve physicians or clinicians who are going to use the technology."

The panel, moderated by Rebekah Angove, vice president of evaluation and patient experience at the Patient Advocate Foundation, noted that AI is already affecting patients both directly and indirectly.

In ideal scenarios, for example, it's empowering doctors to spend more time with individuals. "There's going tobe a human in the loop for a very long time," said Fox.

"We can power the clinician with better information from a much larger data set," he continued. AI is also enabling screening tools and patient access, said the experts.

"There are many things that work in the background that impact [patient] lives and experience already," said Piyush Mathur, staff anesthesiologist and critical care physician at the Cleveland Clinic.

At the same time, the panel pointed to the role clinicians can play in building patient trust around artificial intelligence and machine learning technology.

Nahar said that as a provider, he considers several questions when using an AI-powered tool for his patient. "Is the technology really needed for this patient to solve this problem?" he said he asks himself. "How will it improve the care that I deliver to the patient? Is it something reliable?"

"Those are the points, as a physician, I would like to know," he said.

Mathur also raised the issue of educating clinicians about AI. "We have to understand it a little bit better to be able to translate that science to the patients in their own language," he said. "We have to be the guardians of making sure that we're providing the right data for the patient."

The panelists discussed the problem of bias, about which patients may have concerns and rightly so.

"There are multiple entry points at which bias can be introduced," said Nahar.

During the design process, he said, multiple stakeholders need to be involved to closely consider where bias could be coming from and how it can be mitigated.

As panelists have pointed out at other sessions, he also emphasized the importance of evaluating tools in an ongoing process.

Developers and users should be asking themselves, "How can we improve and make it better?" he said.

Overall, said Nahar, best practices and guidances need to be established to better implement and operationalize AI from the patient perspective and provider perspective.

The onus is "upon us to make sure we use this technology in the correct way to improve care for our patients," added Mathur.

Kat Jercich is senior editor of Healthcare IT News.Twitter: @kjercichEmail: kjercich@himss.orgHealthcare IT News is a HIMSS Media publication.

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Human-centered AI can improve the patient experience - Healthcare IT News

AutoML (automated machine learning) is an active area of research in academia and the industry. The cloud vendors promote some or the other form of AutoML services. Likewise, Tech unicorns also offer various AutoML services for its platform users. Additionally, many different open source projects are available, offering exciting new approaches.

The growing desire to gain business value from artificial intelligence (AI) has created a gap between the demand for data science expertise and the supply of data scientist. Running AI and AutoML on the latest Intel architecture addresses this challenge by automating many tasks required to develop AI and machine learning applications.

Using AutoML, businesses can automate tedious and time-consuming manual work required by todays data science. With AutoML, data-savvy users of all levels have access to powerful machine learning algorithms to avoid human error.

With better access to the power of ML, businesses can generate advanced machine learning models without the requirement to understand complex algorithms. Data scientists can apply their specialisation to fine-tune ML models for purposes ranging from manufacturing to retailing to healthcare, and more.

With AutoML, the productivity of repetitive tasks can be increased as it enables a data scientist to focus more on the problem rather than the models. Automating ML pipeline also helps to avoid errors that might creep in manually. AutoML is a step towards democratizing ML by making the power of ML accessible to everybody.

Enterprises seek to automate machine learning pipelines and different steps in the ML workflow to address the increase in tendency and requirement for speeding up AI adoption.

Not everything but many things can be automated in the data science workflow. The pre-implemented model types and structures can be presented or learnt from the input datasets for selection. AutoML simplifies the development of projects, proof of value initiatives, and help business users to stimulate ML solutions development without extensive programming knowledge. It can serve as a complementary tool for data scientists that help them to either quickly find out what algorithms they could try or see if they have skipped some algorithms, and that could have been a valuable selection to obtain better outcomes.

Here are some reasons why business leaders should hire data scientists if they have AutoML tools on their hands:

Essentially, the purpose of AutoML is to automate the repetitive tasks like pipeline creation and hyperparameter tuning so data scientists can spend time on the business problem at hand.

AutoML aims to make the technology available to everyone rather a select few. AutoML and data scientists can work in conjunction to speed up the machine learning process to utilise the real effectiveness of ML.

Whether or not AutoML becomes a success depends mainly on its adoption and the advancements that are made in this sector. However, AutoML is a big part of the future of machine learning.

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AutoML is the Future of Machine Learning - Analytics Insight

These eight women are at the forefront of the field of artificial intelligence today. They hail from ... [+] academia, startups, large technology companies, venture capital and beyond.

It is a simple truth: the field of artificial intelligence is far too male-dominated. According to a 2018 study from Wired and Element AI, just 12% of AI researchers globally are female.

Artificial intelligence will reshape every corner of our lives in the coming yearsfrom healthcare to finance, from education to government. It is therefore troubling that those building this technology do not fully represent the society they are poised to transform.

Yet there are many brilliant women at the forefront of AI today. As entrepreneurs, academic researchers, industry executives, venture capitalists and more, these women are shaping the future of artificial intelligence. They also serve as role models for the next generation of AI leaders, reflecting what a more inclusive AI community can and should look like.

Featured below are eight of the leading women in the field of artificial intelligence today.

Joy Buolamwini has aptly been described as the conscience of the A.I. revolution.

Her pioneering work on algorithmic bias as a graduate student at MIT opened the worlds eyes to the racial and gender prejudices embedded in facial recognition systems. Amazon, Microsoft and IBM each suspended their facial recognition offerings this year as a result of Buolamwinis research, acknowledging that the technology was not yet fit for public use. Buolamwinis work is powerfully profiled in the new documentary Coded Bias.

Buolamwini stands at the forefront of a burgeoning movement to identify and address the social consequences of artificial intelligence technology, a movement she advances through her nonprofit Algorithmic Justice League.

Buolamwini on the battle against algorithmic bias: When I started talking about this, in 2016, it was such a foreign concept. Today, I cant go online without seeing some news article or story about a biased AI system. People are just now waking up to the fact that there is a problem. Awareness is goodand then that awareness needs to lead to action. That is the phase that were in.

From SRI to Google to Uber to NVIDIA, Claire Delaunay has held technical leadership roles at many of Silicon Valleys most iconic organizations. She was also co-founder and engineering head at Otto, the pedigreed but ill-fated autonomous trucking startup helmed by Anthony Levandowski.

In her current role at NVIDIA, Delaunay is focused on building tools and platforms to enable the deployment of autonomous machines at scale.

Delaunay on the tradeoffs between working at a big company and a startup: Some kinds of breakthroughs can only be accomplished at a big company, and other kinds of breakthroughs can only be accomplished at a startup. Startups are very good at deconstructing things and generating discontinuous big leaps forward. Big companies are very good at consolidating breakthroughs and building out robust technology foundations that enable future innovation.

Rana el Kaliouby has dedicated her career to making AI more emotionally intelligent.

Kaliouby is credited with pioneering the field of Emotion AI. In 2009, she co-founded the startup Affectiva as a spinout from MIT to develop machine learning systems capable of understanding human emotions. Today, the companys technology is used by 25% of the Fortune 500, including for media analytics, consumer behavioral research and automotive use cases.

Kaliouby on her big-picture vision: My lifes work is about humanizing technology before it dehumanizes us.

Daphne Kollers wide-ranging career illustrates the symbiosis between academia and industry that is a defining characteristic of the field of artificial intelligence.

Koller has been a professor at Stanford since 1995, focused on machine learning. In 2012 she co-founded education technology startup Coursera with fellow Stanford professor and AI leader Andrew Ng. Coursera is today a $2.6 billion ed tech juggernaut.

Kollers most recent undertaking may be her most ambitious yet. She is the founding CEO at insitro, a startup applying machine learning to transform pharmaceutical drug discovery and development. Insitro has raised roughly $250 million from Andreessen Horowitz and others and recently announced a major commercial partnership with Bristol Myers Squibb.

Koller on advice for those just starting out in the field of AI: Pick an application of AI that really matters, that is really societally worthwhilenot all AI applications areand then put in the hard work to truly understand that domain. I am able to build insitro today only because I spent 20 years learning biology. An area I might suggest to young people today is energy and the environment.

Few individuals have left more of a mark on the world of AI in the twenty-first century than Fei-Fei Li.

As a young Princeton professor in 2007, Li conceived of and spearheaded the ImageNet project, a database of millions of labeled images that has changed the entire trajectory of AI. The prescient insight behind ImageNet was that massive datasetsmore than particular algorithmswould be the key to unleashing AIs potential. When Geoff Hinton and team debuted their neural network-based model trained on ImageNet at the 2012 ImageNet competition, the modern era of deep learning was born.

Li has since become a tenured professor at Stanford, served as Chief Scientist of AI/ML at Google Cloud, headed Stanfords AI lab, joined the Board of Directors at Twitter, cofounded the prominent nonprofit AI4ALL, and launched Stanfords Human-Centered AI Institute (HAI). Across her many leadership positions, Li has tirelessly advocated for a more inclusive, equitable and human approach to AI.

Li on why diversity in AI is so important: Our technology is not independent of human values. It represents the values of the humans that are behind the design, development and application of the technology. So, if were worried about killer robots, we should really be worried about the creators of the technology. We want the creators of this technology to represent our values and represent our shared humanity.

Anna Patterson has led a distinguished career developing and deploying AI products, both at large technology companies and at startups.

A long-time executive at Google, which she first joined in 2004, Patterson led artificial intelligence efforts for years as the companys VP of Engineering. In 2017 she launched Googles AI venture capital fund Gradient Ventures, where today she invests in early-stage AI startups.

Patterson serves on the board of a number of promising AI startups including Algorithmia, Labelbox and test.ai. She is also a board director at publicly-traded Square.

Patterson on one question she asks herself before investing in any AI startup: Do I find myself constantly thinking about their vision and mission?

Daniela Rus is one of the worlds leading roboticists.

She is an MIT professor and the first female head of MITs Computer Science and Artificial Intelligence Lab (CSAIL), one of the largest and most prestigious AI research labs in the world. This makes her part of a storied lineage: previous directors of CSAIL (and its predecessor labs) over the decades have included AI legends Marvin Minsky, J.C.R. Licklider and Rodney Brooks.

Rus groundbreaking research has advanced the state of the art in networked collaborative robots (robots that can work together and communicate with one another), self-reconfigurable robots (robots that can autonomously change their structure to adapt to their environment), and soft robots (robots without rigid bodies).

Rus on a common misconception about AI: It is important for people to understand that AI is nothing more than a tool. Like any other tool, it is neither intrinsically good nor bad. It is solely what we choose to do with it. I believe that we can do extraordinarily positive things with AIbut it is not a given that that will happen.

Shivon Zilis has spent time on the leadership teams of several companies at AIs bleeding edge: OpenAI, Neuralink, Tesla, Bloomberg Beta.

She is the youngest board member at OpenAI, the influential research lab behind breakthroughs like GPT-3. At NeuralinkElon Musks mind-bending effort to meld the human brain with digital machinesZilis works on high-priority strategic initiatives in the office of the CEO.

Zilis on her attitude toward new technology development: Im astounded by how often the concept of building moats comes up. If you think the technology youre building is good for the world, why not laser focus on expanding your tech tree as quickly as possible versus slowing down and dividing resources to impede the progress of others?

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8 Leading Women In The Field Of AI - Forbes

The test automation effort will continue to accelerate. Surprisingly, a lot of businesses do have manual checks in their distribution pipeline, but you can't deliver quickly if you have humans on the vital path of the supply chain, slowing things down.

FREMONT, CA: Over the last decade, there has been an unwavering drive to deliver applications faster. Automated testing has emerged as one of the most relevant technologies for scaling DevOps, businesses are spending a lot of time and effort to develop end-to-end software delivery pipelines, and containers and their ecosystem are keeping up with their early promise.

Testing is one of the top DevOps monitors that companies can use to ensure that their consumers have a delightful brand experience. Others include access management, logging, traceability and disaster recovery.

Quality and access control are preventive controls, while others are reactive. In the future, there will be a growing emphasis on consistency because it prevents consumers from having a bad experience. So delivering value quicklyor better still delivering the right value quickly at the right quality levelis the main theme that everyone will see this year and beyond.

Here are the five key trends in 2021:

Automation of exams

The test automation effort will continue to accelerate. Surprisingly, a lot of businesses do have manual checks in their distribution pipeline, but you can't deliver quickly if you have humans on the vital path of the supply chain, slowing things down.

Automation of manual tests is a long process that takes dedicated engineering time. While many companies have at least some kind of test automation, much needs to be done. That's why automated testing will remain one of the top trends in the future.

DevOps-driven data

Over the past six to eight years, the industry has concentrated on linking various resources through the development of robust distribution pipelines. Each of these tools produces a significant amount of data, but the data is used minimally, if at all.

The next stage is to add the smarts to the tooling. Expect to see an increased focus on data-driven decision-making by practitioners.

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Machine Learning To Bring A Transformation In Software Testing - CIO Applications

Switchtec PFX PCIe Gen 5 high performance switches double the data rate of PCIe Gen 4.0 solutions while delivering ultra-low latency and advanced diagnostics

CHANDLER, Ariz., Feb. 02, 2021 (GLOBE NEWSWIRE) Applications such as data analytics, autonomous-driving and medical diagnostics are driving extraordinary demands for machine learning and hyperscale compute infrastructure. To meet these demands, Microchip Technology Inc.(Nasdaq: MCHP)today announced the worlds first PCI Express (PCIe) 5.0 switch solutions theSwitchtec PFX PCIe 5.0 family doubling the interconnect performance for dense compute, high speed networking and NVM Express(NVMe) storage. Together with the XpressConnectretimers, Microchip is the industrys only supplier of both PCIe Gen 5 switches and PCIe Gen 5 retimer products, delivering a complete portfolio of PCIe Gen 5 infrastructure solutions with proven interoperability.

Accelerators, graphic processing units (GPUs), central processing units (CPUs) and high-speed network adapters continue to drive the need for higher performance PCIe infrastructure. Microchips introduction of the worlds first PCIe 5.0 switch doubles the PCIe Gen 4 interconnect link rates to 32 GT/s to support the most demanding next-generation machine learning platforms, said Andrew Dieckmann, associate vice president of marketing and applications engineering for Microchips data center solutions business unit. Coupled with our XpressConnect family of PCIe 5.0 and Compute Express Link(CXL) 1.1/2.0 retimers, Microchip offers the industrys broadest portfolio of PCIe Gen 5 infrastructure solutions with the lowest latency and end-to-end interoperability.

The Switchtec PFX PCIe 5.0 switch family comprises high density, high reliability switches supporting 28 lanes to 100 lanes and up to 48 non-transparent bridges (NTBs). The Switchtec technology devices support high reliability capabilities, including hot-and surprise-plug as well as secure boot authentication. With PCIe 5.0 data rates of 32 GT/s, signal integrity and complex system topologies pose significant development and debug challenges. To accelerate time-to-market, the Switchtec PFX PCIe 5.0 switch provides a comprehensive suite of debug and diagnostic features including sophisticated internal PCIe analyzers supporting Transaction Layer Packet (TLP) generation and analysis and on-chip non-obtrusive SerDes eye capture capabilities. Rapid system bring-up and debug is further supported with ChipLink an intuitive graphical user interface (GUI) based device configuration and topology viewer that provides full access to the PFX PCIe switchs registers, counters, diagnostics and forensic capture capabilities.

Intels upcoming Sapphire Rapids Xeon processors will implement PCI Express 5.0 and Compute Express Link running up to 32.0 GT/s to deliver the low-latency and high-bandwidth I/O solutions our customers need to deploy, said Dr. Debendra Das Sharma, Intel fellow and director of I/O technology and standards. We are pleased to see Microchips PCIe 5.0 switch and retimer investment strengthen the ecosystem and drive broader deployment of PCIe 5.0 and CXL enabled solutions.

Development ToolsMicrochip has released a full set of design-in collateral, reference designs, evaluation boards and tools to support customers building systems that take advantage of the high-bandwidth of PCIe 5.0.

In addition to PCIe technology, Microchip also provides data center infrastructure builders worldwide with total system solutions including RAID over NVMe, storage, memory, timing and synchronization systems, stand-alone secure boot, secure firmware and authentication, wireless products, touch-enabled displays to configure and monitor data center equipment and predictive fan controls.

AvailabilityThe Switchtec PFX PCIe 5.0 family of switches are sampling now to qualified customers. For additional information, contact a Microchip sales representative.

ResourcesHigh-res image available through Flickr or editorial contact (feel free to publish):

About Microchip TechnologyMicrochip Technology Inc. is a leading provider of smart, connected and secure embedded control solutions. Its easy-to-use development tools and comprehensive product portfolio enable customers to create optimal designs which reduce risk while lowering total system cost and time to market. The companys solutions serve more than 120,000 customers across the industrial, automotive, consumer, aerospace and defense, communications and computing markets. Headquartered in Chandler, Arizona, Microchip offers outstanding technical support along with dependable delivery and quality. For more information, visit the Microchip website atwww.microchip.com.

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Microchip Accelerates Machine Learning and Hyperscale Computing Infrastructure with the World's First PCI Express 5.0 Switches - EE Journal

Metallic alloys for aerospace components are expected to be made faster and more cheaply with the application of machine learning in Project MEDAL.

This is the aim of Project MEDAL: Machine Learning for Additive Manufacturing Experimental Design,which is being led by Intellegens, a Cambridge University spin-out specialising in artificial intelligence, the Sheffield University AMRC North West, and Boeing. It aims to accelerate the product development lifecycle of aerospace components by using a machine learning model to optimise additive manufacturing (AM) for new metal alloys.

How collaboration is driving advances in additive manufacturing

Project MEDALs research will concentrate on metal laser powder bed fusion and will focus on so-called parameter variables required to manufacture high density, high strength parts.

The project is part of the National Aerospace Technology Exploitation Programme (NATEP), a 10m initiative for UK SMEs to develop innovative aerospace technologies funded by the Department for Business, Energy and Industrial Strategy and delivered in partnership with the Aerospace Technology Institute (ATI) and Innovate UK.

In a statement, Ben Pellegrini, CEO of Intellegens, said: The intersection of machine learning, design of experiments and additive manufacturing holds enormous potential to rapidly develop and deploy custom parts not only in aerospace, as proven by the involvement of Boeing, but in medical, transport and consumer product applications.

There are many barriers to the adoption of metallic AM but by providing users, and maybe more importantly new users, with the tools they need to process a required material should not be one of them, added James Hughes, research director for Sheffield University AMRC North West. With the AMRCs knowledge in AM, and Intellegens AI tools, all the required experience and expertise is in place in order to deliver a rapid, data-driven software toolset for developing parameters for metallic AM processes to make them cheaper and faster.

Aerospace components must withstand certain loads and temperature resistances, and some materials are limited in what they can offer. There is also simultaneous push for lower weight and higher temperature resistance for better fuel efficiency, bringing new or previously impractical-to-machine metals into the aerospace sector.

One of the main drawbacks of AM is the limited material selection currently available and the design of new materials, particularly in the aerospace industry, requires expensive and extensive testing and certification cycles which can take longer than a year to complete and cost as much as 1m. Project MEDAL aims to accelerate this process.

The machine learning solution in this project can significantly reduce the need for many experimental cycles by around 80 per cent, Pellegrini said: The software platform will be able to suggest the most important experiments needed to optimise AM processing parameters, in order to manufacture parts that meet specific target properties. The platform will make the development process for AM metal alloys more time and cost-efficient. This will in turn accelerate the production of more lightweight and integrated aerospace components, leading to more efficient aircraft and improved environmental impact.

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Project MEDAL to apply machine learning to aero innovation - The Engineer