The global Machine Learning in Finance Market is carefully researched in the report while largely concentrating on top players and their business tactics, geographical expansion, market segments, competitive landscape, manufacturing, and pricing and cost structures. Each section of the research study is specially prepared to explore key aspects of the global Machine Learning in Finance Market. For instance, the market dynamics section digs deep into the drivers, restraints, trends, and opportunities of the global Machine Learning in Finance Market. With qualitative and quantitative analysis, we help you with thorough and comprehensive research on the global Machine Learning in Finance Market. We have also focused on SWOT, PESTLE, and Porters Five Forces analyses of the global Machine Learning in Finance Market.

Leading players of the global Machine Learning in Finance Market are analyzed taking into account their market share, recent developments, new product launches, partnerships, mergers or acquisitions, and markets served. We also provide an exhaustive analysis of their product portfolios to explore the products and applications they concentrate on when operating in the global Machine Learning in Finance Market. Furthermore, the report offers two separate market forecasts one for the production side and another for the consumption side of the global Machine Learning in Finance Market. It also provides useful recommendations for new as well as established players of the global Machine Learning in Finance Market.

Final Machine Learning in Finance Report will add the analysis of the impact of COVID-19 on this Market.

Machine Learning in Finance Market competition by top manufacturers/Key player Profiled:

Ignite LtdYodleeTrill A.I.MindTitanAccentureZestFinance

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With the slowdown in world economic growth, the Machine Learning in Finance industry has also suffered a certain impact, but still maintained a relatively optimistic growth, the past four years, Machine Learning in Finance market size to maintain the average annual growth rate of 15 from XXX million $ in 2014 to XXX million $ in 2019, This Report analysts believe that in the next few years, Machine Learning in Finance market size will be further expanded, we expect that by 2024, The market size of the Machine Learning in Finance will reach XXX million $.

Segmentation by Product:

Supervised LearningUnsupervised LearningSemi Supervised LearningReinforced Leaning

Segmentation by Application:

BanksSecurities Company

Competitive Analysis:

Global Machine Learning in Finance Market is highly fragmented and the major players have used various strategies such as new product launches, expansions, agreements, joint ventures, partnerships, acquisitions, and others to increase their footprints in this market. The report includes market shares of Machine Learning in Finance Market for Global, Europe, North America, Asia-Pacific, South America and Middle East & Africa.

Scope of the Report:The all-encompassing research weighs up on various aspects including but not limited to important industry definition, product applications, and product types. The pro-active approach towards analysis of investment feasibility, significant return on investment, supply chain management, import and export status, consumption volume and end-use offers more value to the overall statistics on the Machine Learning in Finance Market. All factors that help business owners identify the next leg for growth are presented through self-explanatory resources such as charts, tables, and graphic images.

Key Questions Answered:

Our industry professionals are working reluctantly to understand, assemble and timely deliver assessment on impact of COVID-19 disaster on many corporations and their clients to help them in taking excellent business decisions. We acknowledge everyone who is doing their part in this financial and healthcare crisis.

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Table of Contents

Report Overview:It includes major players of the global Machine Learning in Finance Market covered in the research study, research scope, and Market segments by type, market segments by application, years considered for the research study, and objectives of the report.

Global Growth Trends:This section focuses on industry trends where market drivers and top market trends are shed light upon. It also provides growth rates of key producers operating in the global Machine Learning in Finance Market. Furthermore, it offers production and capacity analysis where marketing pricing trends, capacity, production, and production value of the global Machine Learning in Finance Market are discussed.

Market Share by Manufacturers:Here, the report provides details about revenue by manufacturers, production and capacity by manufacturers, price by manufacturers, expansion plans, mergers and acquisitions, and products, market entry dates, distribution, and market areas of key manufacturers.

Market Size by Type:This section concentrates on product type segments where production value market share, price, and production market share by product type are discussed.

Market Size by Application:Besides an overview of the global Machine Learning in Finance Market by application, it gives a study on the consumption in the global Machine Learning in Finance Market by application.

Production by Region:Here, the production value growth rate, production growth rate, import and export, and key players of each regional market are provided.

Consumption by Region:This section provides information on the consumption in each regional market studied in the report. The consumption is discussed on the basis of country, application, and product type.

Company Profiles:Almost all leading players of the global Machine Learning in Finance Market are profiled in this section. The analysts have provided information about their recent developments in the global Machine Learning in Finance Market, products, revenue, production, business, and company.

Market Forecast by Production:The production and production value forecasts included in this section are for the global Machine Learning in Finance Market as well as for key regional markets.

Market Forecast by Consumption:The consumption and consumption value forecasts included in this section are for the global Machine Learning in Finance Market as well as for key regional markets.

Value Chain and Sales Analysis:It deeply analyzes customers, distributors, sales channels, and value chain of the global Machine Learning in Finance Market.

Key Findings: This section gives a quick look at important findings of the research study.

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Trending News Machine Learning in Finance Market Key Drivers, Key Countries, Regional Landscape and Share Analysis by 2025|Ignite Ltd,Yodlee,Trill...

Originally published in Chip Huyen Blog, June 22, 2020

To better understand the landscape of available tools for machine learning production, I decided to look up every AI/ML tool I could find. The resources I used include:

After filtering out applications companies (e.g. companies that use ML to provide business analytics), tools that arent being actively developed, and tools that nobody uses, I got 202 tools. See the full list. Please let me know if there are tools you think I should include but arent on the list yet!

Disclaimer

This post consists of 6 parts:

I. OverviewII. The landscape over timeIII. The landscape is under-developedIV. Problems facing MLOpsV. Open source and open-coreVI. Conclusion

I. OVERVIEW

In one way to generalize the ML production flow that I agreed with, it consists of 4 steps:

I categorize the tools based on which step of the workflow that it supports. I dont include Project setup since it requires project management tools, not ML tools. This isnt always straightforward since one tool might help with more than one step. Their ambiguous descriptions dont make it any easier: we push the limits of data science, transforming AI projects into real-world business outcomes, allows data to move freely, like the air you breathe, and my personal favorite: we lived and breathed data science.

I put the tools that cover more than one step of the pipeline into the category that they are best known for. If theyre known for multiple categories, I put them in the All-in-one category. I also include the Infrastructure category to include companies that provide infrastructure for training and storage. Most of these are Cloud providers.

To continue reading this article click here.

Go here to see the original:
What I Learned From Looking at 200 Machine Learning Tools - Machine Learning Times - machine learning & data science news - The Predictive...

The latest report on Data Science and Machine Learning Service Industry market now available at MarketStudyReport.com, delivers facts and numbers regarding the market size, geographical landscape and profit forecast of the Data Science and Machine Learning Service Industry market. In addition, the report focuses on major obstacles and the latest growth plans adopted by leading companies in this business.

The Data Science and Machine Learning Service Industry market report presents a comprehensive assessment of this industry vertical and comprises of significant insights pertaining to the current as well as anticipated situation of the marketplace over the forecast period. Key industry trends which are impacting the Data Science and Machine Learning Service Industry market are also mentioned in the report. The document delivers information about industry policies, regional spectrum and other parameters including the impact of the current industry scenario on investors.

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The report on Data Science and Machine Learning Service Industry market evaluates the advantages and the disadvantages of company products as well as provides with an overview of the competitive scenario. Significant data regarding the raw material and the downstream buyers is provided in the report.

Revealing information concerning the Data Science and Machine Learning Service Industry market competitive terrain:

Important data regarding the Data Science and Machine Learning Service Industry market regional landscape:

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Data Science and Machine Learning Service Market Size 2020 Application, Trends, Growth, Opportunities and Worldwide Forecast to 2025 - 3rd Watch News

A new project called Keen is launching today from Googles in-house incubator for new ideas, Area 120, to help users track their interests. The app is like a modern rethinking of the Google Alerts service, which allows users to monitor the web for specific content. Except instead of sending emails about new Google Search results, Keen leverages a combination of machine learning techniques and human collaboration to help users curate content around a topic.

Each individual area of interest is called a keen a word often used to reference someone with an intellectual quickness.

The idea for the project came about after co-founder C.J. Adams realized he was spending too much time on his phone mindlessly browsing feeds and images to fill his downtime. He realized that time could be better spent learning more about a topic he was interested in perhaps something he always wanted to research more or a skill he wanted to learn.

To explore this idea, he and four colleagues at Google worked in collaboration with the companys People and AI Research (PAIR) team, which focuses on human-centered machine learning, to create what has now become Keen.

To use Keen, which is available both on the web and on Android, you first sign in with your Google account and enter in a topic you want to research. This could be something like learning to bake bread, bird watching or learning about typography, suggests Adams in an announcement about the new project.

Keen may suggest additional topics related to your interest. For example, type in dog training and Keen could suggest dog training classes, dog training books, dog training tricks, dog training videos and so on. Click on the suggestions you want to track and your keen is created.

When you return to the keen, youll find a pinboard of images linking to web content that matches your interests. In the dog training example, Keen found articles and YouTube videos, blog posts featuring curated lists of resources, an Amazon link to dog training treats and more.

For every collection, the service uses Google Search and machine learning to help discover more content related to the given interest. The more you add to a keen and organize it, the better these recommendations become.

Its like an automated version of Pinterest, in fact.

Once a keen is created, you can then optionally add to the collection, remove items you dont want and share the Keen with others to allow them to also add content. The resulting collection can be either public or private. Keen can also email you alerts when new content is available.

Google, to some extent, already uses similar techniques to power its news feed in the Google app. The feed, in that case, uses a combination of items from your Google Search history and topics you explicitly follow to find news and information it can deliver to you directly on the Google apps home screen. Keen, however, isnt tapping into your search history. Its only pulling content based on interests you directly input.

And unlike the news feed, a keen isnt necessarily focused only on recent items. Any sort of informative, helpful information about the topic can be returned. This can include relevant websites, events, videos and even products.

But as a Google project and one that asks you to authenticate with your Google login the data it collects is shared with Google. Keen, like anything else at Google, is governed by the companys privacy policy.

Though Keen today is a small project inside a big company, it represents another step toward the continued personalization of the web. Tech companies long since realized that connecting users with more of the content that interests them increases their engagement, session length, retention and their positive sentiment for the service in question.

But personalization, unchecked, limits users exposure to new information or dissenting opinions. It narrows a persons worldview. It creates filter bubbles and echo chambers. Algorithmic-based recommendations can send users searching for fringe content further down dangerous rabbit holes, even radicalizing them over time. And in extreme cases, radicalized individuals become terrorists.

Keen would be a better idea if it were pairing machine-learning with topical experts. But it doesnt add a layer of human expertise on top of its tech, beyond those friends and family you specifically invite to collaborate, if you even choose to. That leaves the system wanting for better human editorial curation, and perhaps the need for a narrower focus to start.

Read more:
Googles latest experiment is Keen, an automated, machine-learning based version of Pinterest - TechCrunch

With healthcare organizations around the world leveraging cloud technologies for key clinical and operational systems, the industry is building toward digitally enhanced, data-driven healthcare.

And unstructured healthcare data, within clinical documents and summaries, continues to remain an important source of insights to support clinical and operational excellence.

But there are countless nuggets of important unstructured data something that does not lend itself to manual search and manipulation by clinicians. This is where automation comes in.

Arun Ravi, senior product leader at Amazon Web Services is copresenting a HIMSS20 Digital presentation on unstructured healthcare data and machine learning, Accelerating Insights from Unstructured Data, Cloud Capabilities to Support Healthcare.

There is a huge shift from volume- to value-based care: 54% of hospital CEOs see the transition from volume to value as their biggest financial challenge, and two-thirds of the IT budget goes toward keeping the lights on, Ravi explained.

Machine learning has this really interesting role to play where were not necessarily looking to replace the workflows, but give essentially a superpower to people in healthcare and allow them to do their jobs a lot more efficiently.

In terms of how this affects health IT leaders, with value-based care there is a lot of data being created. When a patient goes through the various stages of care, there is a lot of documentationa lot of datacreated.

But how do you apply the resources that are available to make it much more streamlined, to create that perfect longitudinal view of the patient? Ravi asked. A lot of the current IT models lack that agility to keep pace with technology. And again, its about giving the people in this space a superpower to help them bring the right data forward and use that in order to make really good clinical decisions.

This requires responding to a very new model that has come into play. And this model requires focus on differentiating a healthcare organizations ability to do this work in real time and do it at scale.

How [do] you incorporate these new technologies into care delivery in a way that not only is scalable but actually reaches your patients and also makes sure your internal stakeholders are happy with it? Ravi asked. And again, you want to reduce the risk, but overall, how do you manage this data well in a way that is easy for you to scale and easy for you to deploy into new areas as the care model continues to shift?

So why is machine learning important in healthcare?

If you look at the amount of unstructured data that is created, it is increasing exponentially, said Ravi. And a lot of that remains untapped. There are 1.2 billion unstructured clinical documents that are actually created every year. How do you extract the insights that are valuable for your application without applying manual approaches to it?

Automating all of this really helps a healthcare organization reduce the expense and the time that is spent trying to extract these insights, he said. And this creates a unique opportunity, not just to innovate, but also to build new products, he added.

Ravi and his copresenter, Paul Zhao, senior product leader at AWS, offer an in-depth look into gathering insights from all of this unstructured healthcare data via machine learning and cloud capabilities in their HIMSS20 Digital session. To attend the session, click here.

Twitter:@SiwickiHealthITEmail the writer:bill.siwicki@himss.orgHealthcare IT News is a HIMSS Media publication.

Read this article:
Machine learning can give healthcare workers a 'superpower' - Healthcare IT News

At Pew Research Center, we collect and analyze data in a variety of ways. Besides asking people what they think through surveys, we also regularly study things like images, videos and even the text of religious sermons.

In a digital world full of ever-expanding datasets like these, its not always possible for humans to analyze such vast troves of information themselves. Thats why our researchers have increasingly made use of a method called machine learning. Broadly speaking, machine learning uses computer programs to identify patterns across thousands or even millions of data points. In many ways, these techniques automate tasks that researchers have done by hand for years.

Our latest video explainer part of our Methods 101 series explains the basics of machine learning and how it allows researchers at the Center to analyze data on a large scale. To learn more about how weve used machine learning and other computational methods in our research, including the analysis mentioned in this video, you can explore recent reports from our Data Labs team.

The rest is here:
What is machine learning, and how does it work? - Pew Research Center

The Machine Learning as a Service Market research report enhanced worldwide Coronavirus COVID19 impact analysis on the market size (Value, Production and Consumption), splits the breakdown (Data Status 2014-2019 and 6 Year Forecast From 2020 to 2026), by region, manufacturers, type and End User/application. This Machine Learning as a Service market report covers the worldwide top manufacturers like (Amazon, Oracle Corporation, IBM, Microsoft Corporation, Google Inc., Salesforce.Com, Tencent, Alibaba, UCloud, Baidu, Rackspace, SAP AG, Century Link Inc., CSC (Computer Science Corporation), Heroku, Clustrix, Xeround) which including information such as: Capacity, Production, Price, Sales, Revenue, Shipment, Gross, Gross Profit, Import, Export, Interview Record, Business Distribution etc., these data help the consumer know about the Machine Learning as a Service market competitors better. It covers Regional Segment Analysis, Type, Application, Major Manufactures, Machine Learning as a Service Industry Chain Analysis, Competitive Insights and Macroeconomic Analysis.

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Machine Learning as a Service Market report offers comprehensive assessment of 1) Executive Summary, 2) Market Overview, 3) Key Market Trends, 4) Key Success Factors, 5) Machine Learning as a Service Market Demand/Consumption (Value or Size in US$ Mn) Analysis, 6) Machine Learning as a Service Market Background, 7) Machine Learning as a Service industry Analysis & Forecast 20182023 by Type, Application and Region, 8) Machine Learning as a Service Market Structure Analysis, 9) Competition Landscape, 10) Company Share and Company Profiles, 11) Assumptions and Acronyms and, 12) Research Methodology etc.

Scope of Machine Learning as a Service Market:Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to learn (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed.

On the basis on the end users/applications,this report focuses on the status and outlook for major applications/end users, shipments, revenue (Million USD), price, and market share and growth rate foreach application.

Personal Business

On the basis of product type, this report displays the shipments, revenue (Million USD), price, and market share and growth rate of each type.

Private clouds Public clouds Hybrid cloud

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Geographically, the report includes the research on production, consumption, revenue, Machine Learning as a Service market share and growth rate, and forecast (2017-2022) of the following regions:

Important Machine Learning as a Service Market Data Available In This Report:

Strategic Recommendations, Forecast Growth Areasof the Machine Learning as a Service Market.

Challengesfor the New Entrants,TrendsMarketDrivers.

Emerging Opportunities,Competitive Landscape,Revenue Shareof Main Manufacturers.

This Report Discusses the Machine Learning as a Service MarketSummary; MarketScopeGives A BriefOutlineof theMachine Learning as a Service Market.

Key Performing Regions (APAC, EMEA, Americas) Along With Their Major Countries Are Detailed In This Report.

Company Profiles, Product Analysis,Marketing Strategies, Emerging Market Segments and Comprehensive Analysis of Machine Learning as a Service Market.

Machine Learning as a Service Market ShareYear-Over-Year Growthof Key Players in Promising Regions.

What is the (North America, South America, Europe, Africa, Middle East, Asia, China, Japan)production, production value, consumption, consumption value, import and exportof Machine Learning as a Service market?

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Machine Learning in EducationMarket 2020: Inclusive Insight

Los Angeles, United States, May 2020:The report titled Global Machine Learning in Education Market is one of the most comprehensive and important additions to Alexareports archive of market research studies. It offers detailed research and analysis of key aspects of the global Machine Learning in Education market. The market analysts authoring this report have provided in-depth information on leading growth drivers, restraints, challenges, trends, and opportunities to offer a complete analysis of the global Machine Learning in Education market. Market participants can use the analysis on market dynamics to plan effective growth strategies and prepare for future challenges beforehand. Each trend of the global Machine Learning in Education market is carefully analyzed and researched about by the market analysts.

Machine Learning in Education Market competition by top manufacturers/ Key player Profiled: IBM, Microsoft, Google, Amazon, Cognizan, Pearson, Bridge-U, DreamBox Learning, Fishtree, Jellynote, Quantum Adaptive Learning

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Global Machine Learning in Education Market is estimated to reach xxx million USD in 2020 and projected to grow at the CAGR of xx% during 2020-2026. According to the latest report added to the online repository of Alexareports the Machine Learning in Education market has witnessed an unprecedented growth till 2020. The extrapolated future growth is expected to continue at higher rates by 2026.

Machine Learning in Education Market Segment by Type covers: Cloud-Based, On-Premise

Machine Learning in Education Market Segment by Application covers:Intelligent Tutoring Systems, Virtual Facilitators, Content Delivery Systems, Interactive Websites

After reading the Machine Learning in Education market report, readers get insight into:

*Major drivers and restraining factors, opportunities and challenges, and the competitive landscape*New, promising avenues in key regions*New revenue streams for all players in emerging markets*Focus and changing role of various regulatory agencies in bolstering new opportunities in various regions*Demand and uptake patterns in key industries of the Machine Learning in Education market*New research and development projects in new technologies in key regional markets*Changing revenue share and size of key product segments during the forecast period*Technologies and business models with disruptive potential

Based on region, the globalMachine Learning in Education market has been segmented into Americas (North America ((the U.S. and Canada),) and Latin Americas), Europe (Western Europe (Germany, France, Italy, Spain, UK and Rest of Europe) and Eastern Europe), Asia Pacific (Japan, India, China, Australia & South Korea, and Rest of Asia Pacific), and Middle East & Africa (Saudi Arabia, UAE, Kuwait, Qatar, South Africa, and Rest of Middle East & Africa).

Key questions answered in the report:

What will the market growth rate of Machine Learning in Education market?What are the key factors driving the global Machine Learning in Education market size?Who are the key manufacturers in Machine Learning in Education market space?What are the market opportunities, market risk and market overview of the Machine Learning in Education market?What are sales, revenue, and price analysis of top manufacturers of Machine Learning in Education market?Who are the distributors, traders, and dealers of Machine Learning in Education market?What are the Machine Learning in Education market opportunities and threats faced by the vendors in the global Machine Learning in Education industries?What are sales, revenue, and price analysis by types and applications of Machine Learning in Education market?What are sales, revenue, and price analysis by regions of Machine Learning in Education industries?

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Table of ContentsSection 1 Machine Learning in Education Product DefinitionSection 2 Global Machine Learning in Education Market Manufacturer Share and Market Overview2.1 Global Manufacturer Machine Learning in Education Shipments2.2 Global Manufacturer Machine Learning in Education Business Revenue2.3 Global Machine Learning in Education Market Overview2.4 COVID-19 Impact on Machine Learning in Education IndustrySection 3 Manufacturer Machine Learning in Education Business Introduction3.1 IBM Machine Learning in Education Business Introduction3.1.1 IBM Machine Learning in Education Shipments, Price, Revenue and Gross profit 2014-20193.1.2 IBM Machine Learning in Education Business Distribution by Region3.1.3 IBM Interview Record3.1.4 IBM Machine Learning in Education Business Profile3.1.5 IBM Machine Learning in Education Product Specification3.2 Microsoft Machine Learning in Education Business Introduction3.2.1 Microsoft Machine Learning in Education Shipments, Price, Revenue and Gross profit 2014-20193.2.2 Microsoft Machine Learning in Education Business Distribution by Region3.2.3 Interview Record3.2.4 Microsoft Machine Learning in Education Business Overview3.2.5 Microsoft Machine Learning in Education Product Specification3.3 Google Machine Learning in Education Business Introduction3.3.1 Google Machine Learning in Education Shipments, Price, Revenue and Gross profit 2014-20193.3.2 Google Machine Learning in Education Business Distribution by Region3.3.3 Interview Record3.3.4 Google Machine Learning in Education Business Overview3.3.5 Google Machine Learning in Education Product Specification3.4 Amazon Machine Learning in Education Business Introduction3.5 Cognizan Machine Learning in Education Business Introduction3.6 Pearson Machine Learning in Education Business IntroductionSection 4 Global Machine Learning in Education Market Segmentation (Region Level)4.1 North America Country4.1.1 United States Machine Learning in Education Market Size and Price Analysis 2014-20194.1.2 Canada Machine Learning in Education Market Size and Price Analysis 2014-20194.2 South America Country4.2.1 South America Machine Learning in Education Market Size and Price Analysis 2014-20194.3 Asia Country4.3.1 China Machine Learning in Education Market Size and Price Analysis 2014-20194.3.2 Japan Machine Learning in Education Market Size and Price Analysis 2014-20194.3.3 India Machine Learning in Education Market Size and Price Analysis 2014-20194.3.4 Korea Machine Learning in Education Market Size and Price Analysis 2014-20194.4 Europe Country4.4.1 Germany Machine Learning in Education Market Size and Price Analysis 2014-20194.4.2 UK Machine Learning in Education Market Size and Price Analysis 2014-20194.4.3 France Machine Learning in Education Market Size and Price Analysis 2014-20194.4.4 Italy Machine Learning in Education Market Size and Price Analysis 2014-20194.4.5 Europe Machine Learning in Education Market Size and Price Analysis 2014-20194.5 Other Country and Region4.5.1 Middle East Machine Learning in Education Market Size and Price Analysis 2014-20194.5.2 Africa Machine Learning in Education Market Size and Price Analysis 2014-20194.5.3 GCC Machine Learning in Education Market Size and Price Analysis 2014-20194.6 Global Machine Learning in Education Market Segmentation (Region Level) Analysis 2014-20194.7 Global Machine Learning in Education Market Segmentation (Region Level) AnalysisSection 5 Global Machine Learning in Education Market Segmentation (Product Type Level)5.1 Global Machine Learning in Education Market Segmentation (Product Type Level) Market Size 2014-20195.2 Different Machine Learning in Education Product Type Price 2014-20195.3 Global Machine Learning in Education Market Segmentation (Product Type Level) AnalysisSection 6 Global Machine Learning in Education Market Segmentation (Industry Level)6.1 Global Machine Learning in Education Market Segmentation (Industry Level) Market Size 2014-20196.2 Different Industry Price 2014-20196.3 Global Machine Learning in Education Market Segmentation (Industry Level) AnalysisSection 7 Global Machine Learning in Education Market Segmentation (Channel Level)7.1 Global Machine Learning in Education Market Segmentation (Channel Level) Sales Volume and Share 2014-20197.2 Global Machine Learning in Education Market Segmentation (Channel Level) AnalysisSection 8 Machine Learning in Education Market Forecast 2019-20248.1 Machine Learning in Education Segmentation Market Forecast (Region Level)8.2 Machine Learning in Education Segmentation Market Forecast (Product Type Level)8.3 Machine Learning in Education Segmentation Market Forecast (Industry Level)8.4 Machine Learning in Education Segmentation Market Forecast (Channel Level)Section 9 Machine Learning in Education Segmentation Product Type9.1 Cloud-Based Product Introduction9.2 On-Premise Product IntroductionSection 10 Machine Learning in Education Segmentation Industry10.1 Intelligent Tutoring Systems Clients10.2 Virtual Facilitators Clients10.3 Content Delivery Systems Clients10.4 Interactive Websites ClientsSection 11 Machine Learning in Education Cost of Production Analysis11.1 Raw Material Cost Analysis11.2 Technology Cost Analysis11.3 Labor Cost Analysis11.4 Cost OverviewSection 12 Conclusion

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2020 Current trends in Machine Learning in Education Market Share, Growth, Demand, Trends, Region Wise Analysis of Top Players and Forecasts - Cole of...

AI artificial intelligence concept Central Computer Processors CPU concept, 3d rendering, Circuit ... [+] board, Technology background, Motherboard digital chip, Tech science background, machine learning

Machine learning is definitely a confusing term.Is it AI or something different?

Well, its actually a subset of AI (which, by the way, is a massive category). Machine learning is a method of analyzing data using an analytical model that is built automatically, or learned, from training data, said Rick Negrin, who is the VP of Product Management at MemSQL. The idea is that the model gets better as you feed it more data points.

There are two key steps with machine learning.First, you need to collect and train the data, which can be a long and tough process.Then, you will operationalize the machine learning, such as by using it to help provide insights or as part of a product.There are a myriad of tools to help with the process, such as open source platforms like TensorFlow and commercial systems, such as DataRobot.

Successful machine learning is only as good as the data available, which is why it needs new, updated data to provide the most accurate outputs or predictions for any given need, said Panagiotis Angelopoulos, who is the Chief Data Officer at Persado.And unlike what any one person can analyze, machine learning can take vast amounts of data over time and make predictions to improve the customer experience and provide real value to the end-user.

Sometimes the models are so intricate that they are nearly impossible to understand. The lack of transparency can make it so that certain industries, like healthcare and banking, may not be able to use machine learning models. Because of this, more research is being focused on the explainability of models.

Another challenge with machine learning is the need to form an experienced team. To build this team in-house, you will have to hire more than just data scientists, said Ji Li, who is the director of data science at CLARA analytics.Full deployment of a new solution requires product managers, software engineers, data engineers, operational experts to develop process and operational workflows, staff to integrate data models into operations, people to manage onboarding and training of the employees who will ultimately use the solution, and staff who can quantify value generation.

In other words, for many organizations, the best option with machine learning may be to buy an off-the-shelf solution.The good news is that there are many on the marketand they are generally affordable.

But regardless of what path you take, there needs to be a clear-cut business case for machine learning.It should not be used just because it is trendy.There also needs to be sufficient change management within the organization. One of the greatest challenges in implementing machine learning and other data science initiatives is navigating institutional changegetting a buy-in, dealing with new processes, the changing job duties, and more, said Ingo Mierswa, who is the founder and CTO of RapidMiner.

Then what are the use cases for machine learning?According to Alyssa Simpson Rochwerger, who is the VP of AI and the Data Evangelist at Appen:Machine learning can solve lots of different types of problems.But it's particularly well suited to decisions that require very simple and repetitive tasks at large scale. For example, the US Postal Service has been successfully using machine learning systems to sort the mail for decades. The task was simple:read the address on the mail (sense) and then understand the zip code (perceive) and then sort into different buckets (decide). The US Postal Service processes almost two hundred million pieces of mail per dayso sorting this by hand wouldn't work.

In fact, the examples are seemingly endless for machine learning.Here are just a few:

Machine learning is a tool and like most tools, it works best when used properly, said Matei Zaharia, who is the chief technologist and co-founder of Databricks.Machine learning can take something as simple as some images and some annotations or just drawings on those images and create a solution that can be automated efficiently and at scale. However, we are not in a technological state where a machine learning model can just work on anything that is thrown at itthat is, not without some kind of external guidance. A machine learns, a human teaches.

Tom (@ttaulli) is an advisor to startups and the author of Artificial Intelligence Basics: A Non-Technical Introduction and The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems. He also has developed various online courses, such as for the Python programming language.

Read the original here:
Machine Learning: What Is It Really Good For? - Forbes

Machine-learning algorithms are responsible for the vast majority of the artificial intelligence advancements and applications you hear about. (For more background, check out our first flowchart on "What is AI?" here.)

Machine-learning algorithms use statistics to find patterns in massive* amounts of data. And data, here, encompasses a lot of thingsnumbers, words, images, clicks, what have you. If it can be digitally stored, it can be fed into a machine-learning algorithm.

Machine learning is the process that powers many of the services we use todayrecommendation systems like thoseon Netflix, YouTube, and Spotify; search engines like Google and Baidu; social-media feeds like Facebook and Twitter; voice assistants like Siri and Alexa. The list goes on.

In all of these instances, each platform is collecting as much data about you as possiblewhat genres you like watching, what links you are clicking, which statuses you are reacting toand using machine learning to make a highly educated guess about what you might want next. Or, in the case of a voice assistant, about which words match best with the funny sounds coming out of your mouth.

Frankly, this process is quite basic: find the pattern, apply the pattern. But it pretty much runs the world. Thats in big part thanks to an invention in 1986, courtesy of Geoffrey Hinton, today known as the father of deep learning.

Deep learning is machine learning on steroids: it uses a technique that gives machines an enhanced ability to findand amplifyeven the smallest patterns. This technique is called a deep neural networkdeep because it has many, many layers of simple computational nodes that work together to munch through data and deliver a final resultin the form of the prediction.

Neural networks were vaguely inspired by the inner workings of the human brain. The nodes are sort of like neurons, and the network is sort of like the brain itself. (For the researchers among you who are cringing at this comparison: Stop pooh-poohing the analogy. Its a good analogy.) But Hinton published his breakthrough paper at a time when neural nets had fallen out of fashion. No one really knew how to train them, so they werent producing good results. It took nearly 30 years for the technique to make a comeback. And boy, did it make a comeback.

One last thing you need to know: machine (and deep) learning comes in three flavors: supervised, unsupervised, and reinforcement. In supervised learning, the most prevalent, the data is labeled to tell the machine exactly what patterns it should look for. Think of it as something like a sniffer dog that will hunt down targets once it knows the scent its after. Thats what youre doing when you press play on a Netflix showyoure telling the algorithm to find similar shows.

In unsupervised learning, the data has no labels. The machine just looks for whatever patterns it can find. This is like letting a dog smell tons of different objects and sorting them into groups with similar smells. Unsupervised techniques arent as popular because they have less obvious applications. Interestingly, they have gained traction incybersecurity.

Lastly, we have reinforcement learning, the latest frontier of machine learning. A reinforcement algorithm learns by trial and error to achieve a clear objective. It tries out lots of different things and is rewarded or penalized depending on whether its behaviors help or hinder it from reaching its objective. This is like giving and withholding treats when teaching a dog a new trick. Reinforcement learning is the basis of Googles AlphaGo, the program that famously beat the best human players in the complex game of Go.

Thats it. That's machine learning. Now check out the flowchart above for a final recap.

*Note: Okay, there are technically ways to perform machine learning on smallish amounts of data, but you typically need huge piles of it to achieve good results.

___

This originally appeared in our AI newsletter The Algorithm. To have it directly delivered to your inbox,subscribe herefor free.

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What is machine learning? | MIT Technology Review

The world has seen an unprecedented challenge and is battling this invisible enemy with all their might. The Novel coronavirus spread has left the global economies holding on to strands, businesses impacted and most people locked down. But while the physical world has come to a drastic halt or slow-down, the digital world is blooming. And in addition to understanding the possibilities of home workspaces, companies are finally understanding the scope of Machine Learning and Artificial Intelligence. A trend that was already gardening all the attention in recent years, ML & AI have particularly taken the centre-stage as more and more brands realise the possibilities of these tools. According to a research report released in February, demand for data engineers was up 50% and demand for data scientists was up 32% in 2019 compared to the prior year. Not only is machine learning being used by researchers to tackle this global pandemic, but it is also being seen as an essential tool in building a world post-COVID.

This pandemic is being fought on the basis of numbers and data. This is the key reason that has driven peoples interest in Machine Learning. It helps us in collecting, analysing and understanding a vast quantity of data. Combined with the power of Artificial Intelligence, Machine Learning has the power to help with an early understanding of problems and quick resolutions. In recent times, ML & AI are being used by doctors and medical personnel to track the virus, identify potential patients and even analyse the possible cure available. Even in the current economic crisis, jobs in data science and machine learning have been least affected. All these factors indicate that machine learning and artificial intelligence are here to stay. And this is the key reason that data science is an area you can particularly focus on, in this lockdown.

The capabilities of Machine Learning and Data Sciences One of the key reasons that a number of people have been able to shift to working from home without much hassle has to be the use of ML & AI by businesses. This shift has also motivated many businesses, both small-scale and large-scale, to re-evaluate their functioning. With companies already announcing plans to look at a more robust working mechanism, which involves less office space and more detailed and structured online working systems, the focus on Machine Learning is bound to increase considerably.

The Current PossibilitiesThe world of data science has been coming out stronger during this lockdown and the interest and importance given to the subject are on the rise. AI-powered mechanics and operations have already made it easier to manage various spaces with lower risks and this trend of turning to AI is bound to increase in the coming years. This is the reason that being educated in this field can improve your skills in this segment. If you are someone who has always been intrigued by data sciences and machine learning or are already working in this field and are looking for ways to accelerate your career, there are various courses that you can turn to. With the increased free time that staying at home has facilitated us with, beginning an additional degree to pad up your resume and also learn some cutting-edge concepts while gaining access to industry experts.

Start learning more about Machine Learning & AIIf you are wondering where to begin this journey of learning, a leading online education service provider, upGrad, has curated programs that would suit you! From Data Sciences to in-depth learnings in AI, there are multiple programs on their website that covers various domains. The PG Diploma in Machine Learning and AI, in particular, has a brilliant curriculum that will help you progress in the field of Machine Learning and Artificial Intelligence. A carefully crafted program from IIIT Bangalore which offers 450+ hours of learning with more than 10 practical hands-on capstone projects, this program has been designed to help people get a deeper understanding of the real-life problems in the field.

Understanding the PG Diploma in Machine Learning & AIThis 1-year program at upGrad has been articulated especially for working professionals who are looking for a career push. The curriculum consists of 30+ Case Studies and Assignments and 25+ Industry Mentorship Sessions, which help you to understand everything you need to know about this field. This program has the perfect balance between the practical exposure required to instil better management and problem-solving skills as well as the theoretical knowledge that will sharpen your skills in this category. The program also gives learners an IIIT Bangalore Alumni Status and Job Placement Assistance with Top Firms on successful completion.

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Rise in the demand for Machine Learning & AI skills in the post-COVID world - Times of India

Machine learning is one aspect of the AI portfolio of capability that has been with us in various forms for decades, so its hardly a product of science fiction. Its widely used as a means of processing high volumes of customer data to provide a better service and hence increase profits.

Yet things become more complex when the technology is brought into the public sector, where many decisions can greatly affect our lives. AI is often feared, particularly around removing the human touch that could lead to unfair judgements or decisions that could cause injury, death or even the complete destruction of humanity. If we think about medical diagnoses or the unfair denial of welfare for a citizen, its apparent where the first two fears arise. Hollywood can take credit for the final scenario.

Whatever the fear, we shouldnt throw the baby out with the bathwater. Local services in the UK face a 7.8 billion funding gap by 2025. With services already cut to the bone, central and local government organisations, along with the NHS, need new approaches and technologies to drive efficiency while also improving the service quality. Often this means collaboration between service providers, but collaboration between man and machine can also play a part.

Machine learning systems can help transform the public sector, driving better decisions through more accurate insights and streamlining service delivery through automation. Whats important, however, is that we dont try to replace human judgement and creativity with machine efficiency we need to combine them.

Theres a strong case to be made for greater adoption of machine learning across a diverse range of activities. The basic premise of machine learning is that a computer can derive a formula from looking at lots of historical data that enables the prediction of certain things the data describes. This formula is often termed an algorithm or a model. We use this algorithm with new data to make decisions for a specific task, or we use the additional insight that the algorithm provides to enrich our understanding and drive better decisions.

For example, machine learning can analyse patients interactions in the healthcare system and highlight which combinations of therapies in what sequence offer the highest success rates for patients; and maybe how this regime is different for different age ranges. When combined with some decisioning logic that incorporates resources (availability, effectiveness, budget, etc.) its possible to use the computers to model how scarce resources could be deployed with maximum efficiency to get the best-tailored regime for patients.

When we then automate some of this, machine learning can even identify areas for improvement in real-time and far faster than humans and it can do so without bias, ulterior motives or fatigue-driven error. So, rather than being a threat, it should perhaps be viewed as a reinforcement for human effort in creating fairer and more consistent service delivery.

Machine learning is also an iterative process; as the machine is exposed to new data and information, it adapts through a continuous feedback loop, which in turn provides continuous improvement. As a result, it produces more reliable results over time and ever more finely tuned and improved decision-making. Ultimately, its a tool for driving better outcomes.

The opportunities for AI to enhance service delivery are many. Another example in healthcare is Computer Vision (another branch of AI), which is being used in cancer screening and diagnosis. Were already at the stage where AI, trained from huge libraries of images of cancerous growths, is better at detecting cancer than human radiologists. This application of AI has numerous examples, such as work being done at Amsterdam UMC to increase the speed and accuracy of tumour evaluations.

But lets not get this picture wrong. Here, the true value is in giving the clinician more accurate insight or a second opinion that informs their diagnosis and, ultimately, the patients final decision regarding treatment. A machine is there to do the legwork, but the human decision to start a programme for cancer treatment, remains with the humans.

Acting with this enhanced insight enables doctors to become more efficient as well as effective. Combining the results of CT scans with advanced genomics using analytics, the technology can assess how patients will respond to certain treatments. This means clinicians avoid the stress, side effects and cost of putting patients through procedures with limited efficacy, while reducing waiting times for those patients whose condition would respond well. Yet, full-scale automation could run the risk of creating a lot more VOMIT.

Victims Of Modern Imaging Technology (VOMIT) is a new phenomenon where a condition such as a malignant tumour is detected by imaging and thus at first glance it would seem wise to remove it. However, medical procedures to remove it carry a morbidity risk which may be greater than the risk the tumour presents during the patients likely lifespan. Here, ignorance could be bliss for the patient and doctors would examine the patient holistically, including mental health, emotional state, family support and many other factors that remain well beyond the grasp of AI to assimilate into an ethical decision.

All decisions like these have a direct impact on peoples health and wellbeing. With cancer, the faster and more accurate these decisions are, the better. However, whenever cost and effectiveness are combined there is an imperative for ethical judgement rather than financial arithmetic.

Healthcare is a rich seam for AI but its application is far wider. For instance, machine learning could also support policymakers in planning housebuilding and social housing allocation initiatives, where they could both reduce the time for the decision but also make it more robust. Using AI in infrastructural departments could allow road surface inspections to be continuously updated via cheap sensors or cameras in all council vehicles (or cloud-sourced in some way). The AI could not only optimise repair work (human or robot) but also potentially identify causes and determine where strengthened roadways would cost less in whole-life costs versus regular repairs.

In the US, government researchers are already using machine learning to help officials make quick and informed policy decisions on housing. Using analytics, they analyse the impact of housing programmes on millions of lower-income citizens, drilling down into factors such as quality of life, education, health and employment. This instantly generates insightful, accessible reports for the government officials making the decisions. Now they can enact policy decisions as soon as possible for the benefit of residents.

While some of the fears about AI are fanciful, there is a genuine concern about the ethical deployment of such technology. In our healthcare example, allocation of resources based on gender, sexuality, race or income wouldnt be appropriate unless these specifically had an impact on the prescribed treatment or its potential side-effects. This is self-evident to a human, but a machine would need this to be explicitly defined otherwise. Logically, a machine would likely display bias to those groups whose historical data gave better resultant outcomes, thus perpetuating any human equality gap present in the training data

The recent review by the Committee on Standards in Public Life into AI and its ethical use by government and other public bodies concluded that there are serious deficiencies in regulation relating to the issue, although it stopped short of recommending the establishment of a new regulator.

SAS welcomed the review and contributed to it. We believe these concerns are best addressed proactively by organisations that use AI in a manner which is fair, accountable, transparent and explainable.

The review was chaired by crossbench peer Lord Jonathan Evans, who commented:

Explaining AI decisions will be the key to accountability but many have warned of the prevalence of Black Box AI. However, our review found that explainable AI is a realistic and attainable goal for the public sector, so long as government and private companies prioritise public standards when designing and building AI systems.

Todays increased presence of machine learning should be viewed as complementary to human decision-making within the public sector. Its an assistive tool that turns growing data volumes into positive outcomes for people, quickly and fairly. As the cost of computational power continues to fall, ever-increasing opportunities will emerge for machine learning to enhance public services and help transform lives.

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How can machine learning benefit the healthcare sector? - Open Access Government

The chaos and uncertainty surrounding the coronavirus pandemic have claimed an unlikely victim: the machine learning systems that are programmed to make sense of our online behavior.

The algorithms that recommend products on Amazon, for instance, are struggling to interpret our new lifestyles, MIT Technology Review reports.

And while machine learning tools are built to take in new data, they're typically not so robust that they can adapt as dramatically as needed.

For instance, MIT Tech reports that a company that detects credit card fraud needed to step in and tweak its algorithm to account for a surge of interest in gardening equipment and power tools.

An online retailer found that its AI was ordering stock that no longer matched with what was selling. And a firm that uses AI to recommend investments based on sentiment analysis of news stories was confused by the generally negative tone throughout the media.

"The situation is so volatile," Rael Cline, CEO of the algorithmic marketing consulting firm Nozzle, told MIT Tech.

"You're trying to optimize for toilet paper last week, and this week everyone wants to buy puzzles or gym equipment."

While some companies are dedicating more time and resources to manually steering their algorithms, others see this as an opportunity to improve.

"A pandemic like this is a perfect trigger to build better machine-learning models," Sharma said.

READ MORE: Our weird behavior during the pandemic is messing with AI models

This article was originally published by Futurism. Read the original article.

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Our Behaviour in This Pandemic Has Seriously Confused AI Machine Learning Systems - ScienceAlert

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Machine Learning in Medicine Market 2020-2024 Review and Outlook - Latest Herald

Advances in computer power, machine-learning and predictive algorithms are creating paradigm shifts in many industries. For example, when analgorithm outperformed six radiologistsin reading mammograms and accurately diagnosing breast cancer, this raised questions around the role of machine-learning in medicine and whether it will replace, or enhance, the work being done by doctors.

Similarly, when Googles AI software AlphaGo beat the worlds top Go master in what is described as humankinds most complicated board game, The New York Timesdeclared it isnt looking good for humanity when an algorithm can outperform a human in a highly complex task.

Both these examples point to narrow uses of artificial intelligence, specific types of machine-learning that are hugely effective. The medical example illustrates supervised learning, where a computer is programmed to solve a particular problem by looking for patterns. It is given labelled data sets, in this case X-rays with the diagnosis of presence or absence of breast cancer. When given a new X-ray, the computer applies an algorithm based on what it has learnt from all the previous X-rays to make a diagnosis. Unsupervised learning is a sort of self-optimisation where a computer has a set of rules, such as how to play Go, and through playing millions of games learns how to apply these rules and improve.

What is machine-learning?

Machine-learning is a phenomenal tool. To fully harness its potential it is essential to understand what machine-learning is (and isnt) and to demystify some of the hype and the fear around what it can and cant be used for. We have anthropomorphised computers; we speak about them in terms of intelligence and learning. But in essence, a machine computes it does not learn. Its algorithms are designed to mimic learning. In essence, these algorithms minimise the errors of a complicated function that maps inputs to outcomes and we interpret that as solving a problem, but the machine doesnt know what problem it is solving or that it is playing a game. The intelligence rests with the humans who design the algorithms and configure them for specific tasks.

Now, more than ever, we need intelligent and well-educated people who can apply these techniques in the correct context and interpret the results. When an algorithm fails, the consequences can be catastrophic. An obvious example is a fatal accident caused by aself-driving car. We need to build in fault tolerance. Data integrity is also an important issue what we put in is going to affect what we get out. Education is critical in making sure we get these elements right. And, of course, there are broader ethical issues to consider surrounding data collection, such as what data can be used, where it is sourced, and whether different data sets can be combined.

Machine-learning is particularly valuable in the financial sector. Many applications are already in use in banking, insurance and asset management. Financial institutions use pattern recognition successfully for fraud detection. It is also valuable for looking at trends in data sets and finding patterns that humans may not be able to identify directly, for example in profiling people who apply for credit. There are even robo-advisory applications for individual asset allocation. In financial modelling, machine-learning can be applied to pricing, calibration and hedging.

For example, valuing derivatives contracts depends on many complex factors and variables such as interest rates, exchange rates, equity values all of which fluctuate all the time. Financial mathematicians use models for this, but they are complicated and not easy to solve in a closed form. We may be able to build and apply a model to one contract, but banks have hundreds of contracts, and risk management and regulatory frameworks need to be updated all the time. Machine-learning, specifically deep learning and neural nets, provides a powerful shortcut. We can use classical numerical methods to produce financial models and then use them as labelled data sets as in the X-ray example. An algorithm can take this input to generate the output for multiple contracts.

Industries and organisations that are pulling ahead are figuring out where to replace standard methods and complex, time-consuming computations with machine-learning. They are also using it for more complex modelling approaches, adding further variables that cannot usually be factored into standard methodologies. The most obvious benefit is that it is faster machines can compute millions of times faster than humans. These techniques also have the potential to be far more accurate and allow us to make better-informed decisions.

But the human element is critical. The accuracy of potentially life-changing outcomes will depend on how we identify where we use these techniques, how we build the algorithms, how we choose and manage data and, finally, in how we interpret and act upon the results.

Prof McWalter is an applied mathematician who lectures computational finance at UCTs African Institute of Financial Markets and Risk Management. Prof Kienitz lectures at the University of Wuppertal and is an adjunct associate professor at UCT. His research interests include numerical methods in finance and machine-learning applied to financial problems and derivative instruments.

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Machine-learning is a boon, but it still needs a human hand - Business Day

Monitors display a video showing facial recognition software in use at the headquarters of the artificial intelligence company Megvii, in Beijing, May 10, 2018. Beijing is putting billions of dollars behind facial recognition and other technologies to track and control its citizens.

Gilles Sabri | The New York Times

The world is facing its biggest health crisis in decades but one of the world's most promising technologies artificial intelligence (AI) isn't playing the major role some may have hoped for.

Renowned AI labs at the likes of DeepMind, OpenAI, Facebook AI Research, and Microsoft have remained relatively quiet as the coronavirus has spread around the world.

"It's fascinating how quiet it is," said Neil Lawrence, the former director of machine learning at Amazon Cambridge.

"This (pandemic) is showing what bulls--t most AI hype is. It's great and it will be useful one day but it's not surprising in a pandemic that we fall back on tried and tested techniques."

Those techniques include good, old-fashioned statistical techniques and mathematical models. The latter is used to create epidemiological models, which predict how a disease will spread through a population. Right now, these are far more useful than fields of AI like reinforcement learning and natural-language processing.

Of course, there are a few useful AI projects happening here and there.

In March, DeepMind announced that it hadused a machine-learning technique called "free modelling" to detail the structures of six proteins associated with SARS-CoV-2, the coronavirus that causes the Covid-19 disease.Elsewhere, Israeli start-up Aidoc is using AI imaging to flag abnormalities in the lungs and a U.K. start-up founded by Viagra co-inventor David Brown is using AI to look for Covid-19 drug treatments.

Verena Rieser, a computer science professor at Heriot-Watt University, pointed out that autonomous robots can be used to help disinfect hospitals and AI tutors can support parents with the burden of home schooling. She also said "AI companions" can help with self isolation, especially for the elderly.

"At the periphery you can imagine it doing some stuff with CCTV," said Lawrence, adding that cameras could be used to collect data on what percentage of people are wearing masks.

Separately, a facial recognition system built by U.K. firm SCC has also been adapted to spot coronavirus sufferers instead of terrorists.In Oxford, England, Exscientia is screening more than 15,000 drugs to see how effective they are as coronavirus treatments. The work is being done in partnership withDiamond Light Source, the U.K.'s national "synchotron."

But AI's role in this pandemic is likely to be more nuanced than some may have anticipated. AI isn't about to get us out of the woods any time soon.

"It's kind of indicating how hyped AI was," said Lawrence, who is now a professor of machine learning at the University of Cambridge. "The maturity of techniques is equivalent to the noughties internet."

AI researchers rely on vast amounts of nicely labeled data to train their algorithms, but right now there isn't enough reliable coronavirus data to do that.

"AI learns from large amounts of data which has been manually labeled a time consuming and expensive task," said Catherine Breslin, a machine learning consultant who used to work on Amazon Alexa.

"It also takes a lot of time to build, test and deploy AI in the real world. When the world changes, as it has done, the challenges with AI are going to be collecting enough data to learn from, and being able to build and deploy the technology quickly enough to have an impact."

Breslin agrees that AI technologies have a role to play. "However, they won't be a silver bullet," she said, adding that while they might not directly bring an end to the virus, they can make people's lives easier and more fun while they're in lockdown.

The AI community is thinking long and hard about how it can make itself more useful.

Last week, Facebook AI announced a number of partnerships with academics across the U.S.

Meanwhile, DeepMind's polymath leader Demis Hassabis is helping the Royal Society, the world's oldest independent scientific academy, on a new multidisciplinary project called DELVE (Data Evaluation and Learning for Viral Epidemics). Lawrence is also contributing.

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A.I. can't solve this: The coronavirus could be highlighting just how overhyped the industry is - CNBC

by Sina Pilehchiha, Concordia University

Trail of Bits has manually curated a wealth of datayears of security assessment reportsand now were exploring how to use this data to make the smart contract auditing process more efficient with Slither-simil.

Based on accumulated knowledge embedded in previous audits, we set out to detect similar vulnerable code snippets in new clients codebases. Specifically, we explored machine learning (ML) approaches to automatically improve on the performance of Slither, our static analyzer for Solidity, and make life a bit easier for both auditors and clients.

Currently, human auditors with expert knowledge of Solidity and its security nuances scan and assess Solidity source code to discover vulnerabilities and potential threats at different granularity levels. In our experiment, we explored how much we could automate security assessments to:

Slither-simil, the statistical addition to Slither, is a code similarity measurement tool that uses state-of-the-art machine learning to detect similar Solidity functions. When it began as an experiment last year under the codename crytic-pred, it was used to vectorize Solidity source code snippets and measure the similarity between them. This year, were taking it to the next level and applying it directly to vulnerable code.

Slither-simil currently uses its own representation of Solidity code, SlithIR (Slither Intermediate Representation), to encode Solidity snippets at the granularity level of functions. We thought function-level analysis was a good place to start our research since its not too coarse (like the file level) and not too detailed (like the statement or line level.)

Figure 1: A high-level view of the process workflow of Slither-simil.

In the process workflow of Slither-simil, we first manually collected vulnerabilities from the previous archived security assessments and transferred them to a vulnerability database. Note that these are the vulnerabilities auditors had to find with no automation.

After that, we compiled previous clients codebases and matched the functions they contained with our vulnerability database via an automated function extraction and normalization script. By the end of this process, our vulnerabilities were normalized SlithIR tokens as input to our ML system.

Heres how we used Slither to transform a Solidity function to the intermediate representation SlithIR, then further tokenized and normalized it to be an input to Slither-simil:

Figure 2: A complete Solidity function from the contract TurtleToken.sol.

Figure 3: The same function with its SlithIR expressions printed out.

First, we converted every statement or expression into its SlithIR correspondent, then tokenized the SlithIR sub-expressions and further normalized them so more similar matches would occur despite superficial differences between the tokens of this function and the vulnerability database.

Figure 4: Normalized SlithIR tokens of the previous expressions.

After obtaining the final form of token representations for this function, we compared its structure to that of the vulnerable functions in our vulnerability database. Due to the modularity of Slither-simil, we used various ML architectures to measure the similarity between any number of functions.

Figure 5: Using Slither-simil to test a function from a smart contract with an array of other Solidity contracts.

Lets take a look at the function transferFrom from the ETQuality.sol smart contract to see how its structure resembled our query function:

Figure 6: Function transferFrom from the ETQuality.sol smart contract.

Comparing the statements in the two functions, we can easily see that they both contain, in the same order, a binary comparison operation (>= and <=), the same type of operand comparison, and another similar assignment operation with an internal call statement and an instance of returning a true value.

As the similarity score goes lower towards 0, these sorts of structural similarities are observed less often and in the other direction; the two functions become more identical, so the two functions with a similarity score of 1.0 are identical to each other.

Research on automatic vulnerability discovery in Solidity has taken off in the past two years, and tools like Vulcan and SmartEmbed, which use ML approaches to discovering vulnerabilities in smart contracts, are showing promising results.

However, all the current related approaches focus on vulnerabilities already detectable by static analyzers like Slither and Mythril, while our experiment focused on the vulnerabilities these tools were not able to identifyspecifically, those undetected by Slither.

Much of the academic research of the past five years has focused on taking ML concepts (usually from the field of natural language processing) and using them in a development or code analysis context, typically referred to as code intelligence. Based on previous, related work in this research area, we aim to bridge the semantic gap between the performance of a human auditor and an ML detection system to discover vulnerabilities, thus complementing the work of Trail of Bits human auditors with automated approaches (i.e., Machine Programming, or MP).

We still face the challenge of data scarcity concerning the scale of smart contracts available for analysis and the frequency of interesting vulnerabilities appearing in them. We can focus on the ML model because its sexy but it doesnt do much good for us in the case of Solidity where even the language itself is very young and we need to tread carefully in how we treat the amount of data we have at our disposal.

Archiving previous client data was a job in itself since we had to deal with the different solc versions to compile each project separately. For someone with limited experience in that area this was a challenge, and I learned a lot along the way. (The most important takeaway of my summer internship is that if youre doing machine learning, you will not realize how major a bottleneck the data collection and cleaning phases are unless you have to do them.)

Figure 7: Distribution of 89 vulnerabilities found among 10 security assessments.

The pie chart shows how 89 vulnerabilities were distributed among the 10 client security assessments we surveyed. We documented both the notable vulnerabilities and those that were not discoverable by Slither.

This past summer we resumed the development of Slither-simil and SlithIR with two goals in mind:

We implemented the baseline text-based model with FastText to be compared with an improved model with a tangibly significant difference in results; e.g., one not working on software complexity metrics, but focusing solely on graph-based models, as they are the most promising ones right now.

For this, we have proposed a slew of techniques to try out with the Solidity language at the highest abstraction level, namely, source code.

To develop ML models, we considered both supervised and unsupervised learning methods. First, we developed a baseline unsupervised model based on tokenizing source code functions and embedding them in a Euclidean space (Figure 8) to measure and quantify the distance (i.e., dissimilarity) between different tokens. Since functions are constituted from tokens, we just added up the differences to get the (dis)similarity between any two different snippets of any size.

The diagram below shows the SlithIR tokens from a set of training Solidity data spherized in a three-dimensional Euclidean space, with similar tokens closer to each other in vector distance. Each purple dot shows one token.

Figure 8: Embedding space containing SlithIR tokens from a set of training Solidity data

We are currently developing a proprietary database consisting of our previous clients and their publicly available vulnerable smart contracts, and references in papers and other audits. Together theyll form one unified comprehensive database of Solidity vulnerabilities for queries, later training, and testing newer models.

Were also working on other unsupervised and supervised models, using data labeled by static analyzers like Slither and Mythril. Were examining deep learning models that have much more expressivity we can model source code withspecifically, graph-based models, utilizing abstract syntax trees and control flow graphs.

And were looking forward to checking out Slither-simils performance on new audit tasks to see how it improves our assurance teams productivity (e.g., in triaging and finding the low-hanging fruit more quickly). Were also going to test it on Mainnet when it gets a bit more mature and automatically scalable.

You can try Slither-simil now on this Github PR. For end users, its the simplest CLI tool available:

Slither-simil is a powerful tool with potential to measure the similarity between function snippets of any size written in Solidity. We are continuing to develop it, and based on current results and recent related research, we hope to see impactful real-world results before the end of the year.

Finally, Id like to thank my supervisors Gustavo, Michael, Josselin, Stefan, Dan, and everyone else at Trail of Bits, who made this the most extraordinary internship experience Ive ever had.

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*** This is a Security Bloggers Network syndicated blog from Trail of Bits Blog authored by Nol Ponthieux. Read the original post at: https://blog.trailofbits.com/2020/10/23/efficient-audits-with-machine-learning-and-slither-simil/

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In a recent development, Amazon announced that it will make online courses by its Machine Learning University available to the public. The classes were previously only available to Amazon employees.

The company believes that machine learning has the potential to transform businesses in all industries, but theres a major limitation: demand for individuals with ML expertise far outweighs supply. Thats a challenge for Amazon, and for companies big and small across the globe.

The Machine Learning University (MLU) was founded with an aim to meet this demand in 2016. It helped ML practitioners sharpen their skills and keep them abreast with the latest developments in the field. The classes are taught by Amazon ML experts.

The tech giant now plans to make these classes available to the ML community across the globe. It will include nine more in-depth courses before the year ends. As the blog post notes, by the beginning of 2021, all MLU classes will be available via on-demand video, along with associated coding materials. It will cover topics such as natural language processing, computer vision and tabular data while addressing various business problems.

By going public with the classes, we are contributing to the scientific community on the topic of machine learning, and making machine learning more democratic, said Brent Werness, AWS research scientist and MLUs academic director.

This initiative to bring our courseware online represents a step toward lowering barriers for software developers, students and other builders who want to get started with practical machine learning, he added.

Instead of a three-class sequence that takes upwards of 18 or 20 weeks to complete, in the accelerated classes we can engage students with machine learning right up front, shared Ben Starsky, MLU program manager.

The company said that similar to other open-source initiatives, MLUs courseware will evolve to improve over time based on input from the builder community. It also looking to rebuild its curriculum to further integrate dive into deep learning into class sessions.

The company wants to include as many important things as possible while offering flexibility in the way people can take these classes.

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Srishti currently works as Associate Editor at Analytics India Magazine. When not covering the analytics news, editing and writing articles, she could be found reading or capturing thoughts into pictures.

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RIVERSIDE, Calif. The scientists of the world have yet to agree on a sole, ideal drug to treat the novel coronavirus. According to a new study, however, there certainly isnt a shortage of possibilities. Researchers at the University of California, Riverside have identified hundreds of COVID-19 drug candidates through the use of machine learning.

There is an urgent need to identify effective drugs that treat or prevent COVID-19, says research leader Anandasankar Ray, a professor ofmolecular, cell, and systems biology, in a release. We have developed a drug discovery pipeline that identified several candidates.

This drug discovery pipeline, as professor Ray calls it, is in actuality a computational AI-connected algorithm capable of teaching itself to predict and detect patterns and activity over time through trial and error.

With a vaccine still months away, identifying a drug that proves truly effective against coronaviruscould save countless lives.

As a result, drug candidate pipelines, such as the one we developed, are extremely important to pursue as a first step toward systematic discovery of new drugs for treating COVID-19, professor Ray explains. Existing FDA-approved drugs that target one or more human proteins important for viral entry and replication are currently high priority for repurposing as new COVID-19 drugs. The demand is high for additional drugs or small molecules that can interfere with both entry and replication of SARS-CoV-2 in the body. Our drug discovery pipeline can help.

What makes this complex algorithm tick? Joel Kowalewski, a graduate student in professor Rays lab, used a small grouping of ligands (molecules) associated with 65 human proteins known to come in contact with SARS-CoV-2 proteins. For each of the 65 proteins, a new machine learning model was created.

These models are trained to identify new small molecule inhibitors and activators the ligands simply from their 3-D structures, Kowalewski notes.

All that allowed researchers to develop a database of chemicals featuring structures predicted to interact with the 65 proteins.

The 65 protein targets are quite diverse and are implicated in many additional diseases as well, including cancers, Kowalewski says. Apart from drug-repurposing efforts ongoing against these targets, we were also interested in identifying novel chemicals that are currently not well studied.

In all, more than 10 million (from a database of over 200 million) commercially available small molecules were screened by the machine learning models. Then, among the molecules that hit for any one of the 65 proteins, researchers looked for compounds that have already been approved by the FDA. Any potentially toxic compounds were weeded out by the machine learning models.

This process is what allowed them to identify drug candidates with the highest potential for fighting the coronavirus. Some chemicals are even predicted to neutralize at least two of the 65 protein targets.

Compounds I am most excited to pursue are those predicted to be volatile, setting up the unusual possibility of inhaled therapeutics, professor Ray says.

Historically, disease treatments become increasingly more complex as we develop a better understanding of the disease and how individual genetic variability contributes to the progression and severity of symptoms, Kowalewski notes. Machine learning approaches like ours can play a role in anticipating the evolving treatment landscape by providing researchers with additional possibilities for further study. While the approach crucially depends on experimental data, virtual screening may help researchers ask new questions or find new insight.

This newly-developed computational strategy represents a big improvement over older ways of analyzing large assortments of chemicals simultaneously.

Our database can serve as a resource for rapidly identifying and testing novel, safe treatment strategies for COVID-19 and other diseases where the same 65 target proteins are relevant, professor Ray concludes. While the COVID-19 pandemic was what motivated us, we expect our predictions from more than 10 million chemicals will accelerate drug discovery in the fight against not only COVID-19 but also a number of other diseases.

The study is published in Heliyon.

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As companies step up the use of machine learning-enabled systems in their day-to-day operations, they become increasingly reliant on those systems to help them make critical business decisions. In some cases, the machine learning systems operate autonomously, making it especially important that the automated decision-making works as intended.

However, machine learning-based systems are only as good as the data that's used to train them. If there are inherent biases in the data used to feed a machine learning algorithm, the result could be systems that are untrustworthy and potentially harmful.

In this article, you'll learn why bias in AI systems is a cause for concern, how to identify different types of biases and six effective methods for reducing bias in machine learning.

The power of machine learning comes from its ability to learn from data and apply that learning experience to new data the systems have never seen before. However, one of the challenges data scientists have is ensuring that the data that's fed into machine learning algorithms is not only clean, accurate and -- in the case of supervised learning, well-labeled -- but also free of any inherently biased data that can skew machine learning results.

The power of supervised learning, one of the core approaches to machine learning, in particular depends heavily on the quality of the training data. So it should be no surprise that when biased training data is used to teach these systems, the results are biased AI systems. Biased AI systems that are put into implementation can cause problems, especially when used in automated decision-making systems, autonomous operation, or facial recognition software that makes predictions or renders judgment on individuals.

Some notable examples of the bad outcomes caused by algorithmic bias include: a Google image recognition system that misidentified images of minorities in an offensive way; automated credit applications from Goldman Sachs that have sparked an investigation into gender bias; and a racially biased AI program used to sentence criminals. Enterprises must be hyper-vigilant about machine learning bias: Any value delivered by AI and machine learning systems in terms of efficiency or productivity will be wiped out if the algorithms discriminate against individuals and subsets of the population.

However, AI bias is not only limited to discrimination against individuals. Biased data sets can jeopardize business processes when applied to objects and data of all types. For example, take a machine learning model that was trained to recognize wedding dresses. If the model was trained using Western data, then wedding dresses would be categorized primarily by identifying shades of white. This model would fail in non-Western countries where colorful wedding dresses are more commonly accepted. Errors also abound where data sets have bias in terms of the time of day when data was collected, the condition of the data and other factors.

All of the examples described above represent some sort of bias that was introduced by humans as part of their data selection and identification methods for training the machine learning model. Because the systems technologists build are necessarily colored by their own experiences, they must be very aware that their individual biases can jeopardize the quality of the training data. Individual bias, in turn, can easily become a systemic bias as bad predictions and unfair outcomes are automated.

Part of the challenge of identifying bias is due to the difficulty of seeing how some machine learning algorithms generalize their learning from the training data. In particular, deep learning algorithms have proven to be remarkably powerful in their capabilities. This approach to neural networks leverages large quantities of data, high performance compute power and a sophisticated approach to efficiency, resulting in machine learning models with profound abilities.

Deep learning, however, is a "black box." It's not clear how an individual decision was arrived at by the neural network predictive model. You can't simply query the system and determine with precision which inputs resulted in which outputs. This makes it hard to spot and eliminate potential biases when they arise in the results. Researchers are increasingly turning their focus on adding explainability to neural networks. Verification is the process of proving the properties of neural networks. However, because of the size of neural networks, it can be hard to check them for bias.

Until we have truly explainable systems, we must understand how to recognize and measure AI bias in machine learning models. Some of the biases in the data sets arise from the selection of training data sets. The model needs to represent the data as it exists in the real world. If your data set is artificially constrained to a subset of the population, you will get skewed results in the real world, even if it performs very well against training data. Likewise, data scientists must take care in how they select which data to include in a training data set and which features or dimensions are included in the data for machine learning training.

Companies are combating inherent data bias by implementing programs to not only broaden the diversity of their data sets, but also the diversity of their teams. More diversity on teams means that people of many perspectives and varied experiences are feeding systems the data points to learn from. Unfortunately, the tech industry today is very homogeneous; there are not many women or people of color in the field. Efforts to diversify teams should also have a positive impact on the machine learning models produced, since data science teams will be better able to understand the requirements for more representative data sets.

There are a few sources for the bias that can have an adverse impact on machine learning models. Some of these are represented in the data that is collected and others in the methods used to sample, aggregate, filter and enhance that data.

There are no doubt other types of bias that might be represented in the data set than just the ones listed above, and all those forms should be identified early in the machine learning project.

1. Identify potential sources of bias. Using the above sources of bias as a guide, one way to address and mitigate bias is to examine the data and see how the different forms of bias could impact the data being used to train the machine learning model. Have you selected the data without bias? Have you made sure there isn't any bias arising from errors in data capture or observation? Are you making sure not to use an historic data set tainted with prejudice or confirmation bias? By asking these questions you can help to identify and potentially eliminate that bias.

2. Set guidelines and rules for eliminating bias and procedures. To keep bias in check, organizations should set guidelines, rules and procedures for identifying, communicating and mitigating potential data set bias. Forward-thinking organizations are documenting cases of bias as they occur, outlining the steps taken to identify bias, and explaining the efforts taken to mitigate bias. By establishing these rules and communicating them in an open, transparent manner, organizations can put the right foot forward to address issues of machine learning model bias.

3. Identify accurate representative data. Prior to collecting and aggregating data for machine learning model training, organizations should first try to understand what a representative data set should look like. Data scientists should use their data analysis skills to understand the nature of the population that is to be modeled along with the characteristics of the data used to create the machine learning model. These two things should match in order to build a data set with as little bias as possible.

4. Document and share how data is selected and cleansed. Many forms of bias occur when selecting data from among large data sets and during data cleansing operations. In order to make sure few bias-inducing mistakes are made, organizations should document their methods of data selection and cleansing and allow others to examine when and if the models exhibit any form of bias. Transparency allows for root-cause analysis of sources of bias to be eliminated in future model iterations.

5. Evaluate model for performance and select least-biased, in addition to performance. Machine learning models are often evaluated prior to being placed into operation. Most of the time these evaluation steps focus on aspects of model accuracy and precision. Organizations should also add measures of bias detection in their model evaluation steps. Even if the model performs with certain levels of accuracy and precision for particular tasks, it could fail on measures of bias, which might point to issues with the training data.

6. Monitor and review models in operation. Finally, there is a difference between how the machine learning model performs in training and how it performs in the real world. Organizations should provide methods to monitor and continuously review the models as they perform in operation. If there are signs that certain forms of bias are showing up in the results, then the organization can take action before the bias causes irreparable harm.

When bias becomes embedded in machine learning models, it can have an adverse impact on our daily lives. The bias is exhibited in the form of exclusion, such as certain groups being denied loans or not being able to use the technology, or in the technology not working the same for everyone. As AI continues to become more a part of our lives, the risks from bias only grow larger. Companies, researchers and developers have a responsibility to minimize bias in AI systems. A lot of it comes down to ensuring that the data sets are representative and that the interpretation of data sets is correctly understood. However, just making sure that the data sets aren't biased won't actually remove bias, so having diverse teams of people working toward the development of AI remains an important goal for enterprises.

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6 ways to reduce different types of bias in machine learning - TechTarget