TLDR: The Complete 2020 Big Data and Machine Learning Bundle breaks down understanding and getting started in two of the tech eras biggest new growth sectors.

Its instructive to know just how big Big Data really is. And the reality is that its now so big that the word big doesnt even effectively do it justice anymore. Right now, humankind is creating 2.5 quintillion bytes of data every day. And its growing exponentially, with 90 percent of all data created in just the past two years. By 2023, the big data industry will be worth about $77 billion and thats despite the fact that unstructured data is identified as a problem by 95 percent of all businesses.

Meanwhile, data analysis is also the background of other emerging fields, like the explosion of machine learning projects that have companies like Apple scooping up machine learning upstarts.

The bottom is that if you understand Big Data, you can effectively right your own ticket salary-wise. You can jump into this fascinating field the right way with the training in The Complete 2020 Big Data and Machine Learning Bundle, on sale now for $39.90, over 90 percent off from TNW Deals.

This collection includes 10 courses featuring 68 hours of instruction covering the basics of big data, the tools data analysts need to know, how machines are being taught to think for themselves, and the career applications for learning all this cutting-edge technology.

Everything starts with getting a handle on how data scientists corral mountains of raw information. Six of these courses focus on big data training, including close exploration of the essential industry-leading tools that make it possible. If you dont know what Hadoop, Scala or Elasticsearch do or that Spark Streaming is a quickly developing technology for processing mass data sets in real-time, you will after these courses.

Meanwhile, the remaining four courses center on machine learning, starting with a Machine Learning for Absolute Beginners Level 1 course that helps first-timers get a grasp on the foundations of machine learning, artificial intelligence and deep learning. Students also learn about the Python coding languages role in machine learning as well as how tools like Tensorflow and Keras impact that learning.

A training package valued at almost $1,300, you can start turning Big Data and machine learning into a career with this instruction for just $39.90.

Prices are subject to change.

Read next: The 'average' Robinhood trader is no match for the S&P 500, just like Buffett

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Big data and machine learning are growing at massive rates. This training explains why - The Next Web

Even in horrible economic times, a few simple rules hold unshakably true. And one of those rules is that if you possess an in-demand skill, youll always find work and often, at a top market salary, to boot.

If you understand Big Data and how to find order from the chaos of massive stockpiles of raw information, you can land a six-figure salary. Even now. And if you know how to program machines to think and respond for themselves, youre in an even better position to make a very comfortable living.

If youre unsure about your career future or just want to change your tax bracket, the training in The Complete 2020 Big Data and Machine Learning Bundle can hand you everything you need to start down the path toward life as a Big Data analyst or machine learning engineer.

Across 10 courses hosting almost 70 hours of content, this instruction explains the ins and outs of these exploding job fields, even for those who have no experience with statistics or advanced technology.

Half of the courses here look deeply into the process of using big data, the vast amounts of structured and unstructured information that most businesses collect on a daily basis. Of course, youll never get on top of that tidal wave with your eyes and a ream of spreadsheets, so these courses examine the key analytical tools and language data experts use to organize findings and extract mining for all that unprocessed data.

The training covers industry-leading processes and software like Scala, Hadoop, Elasticsearch, MapReduce and Apache Spark, all valuable means to unlock the secrets hidden inside that mountain of numbers.

The other half of the coursework focuses on machine learning as the Machine Learning for Absolute Beginners - Level 1 course offers newbies a real understanding of what machine learning, artificial intelligence, and deep learning really mean.

Helping computers determine how to assess information and adjust their behavior on their own isnt science fiction. Training in learning the Python coding language at the heart of these fields as well as how to use tools like Tensorflow and Keras not only make it all relatable but can put you in a position to get hired as a machine learning expert with the paycheck to match.

This course package usually retails for almost $1,300, but your path to a new career in Big Data and machine learning can start now for a whole lot less, only $39.90.

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Get over 65 hours of Big Data and Machine Learning training for less than $40 - Boing Boing

This weeks Top 5 comes from Teo Susnjaka computer scientistspecialising in machine learning. He is a Senior Lecturer in Information Technology at Massey University and is the developer behind GDPLive.

As always, we welcome your additions in the comments below or via email to david.chaston@interest.co.nz.

And if you're interested in contributing the occasional Top 5yourself, contact gareth.vaughan@interest.co.nz.

1. Covid-19 broke machine learning.

As the Covid-19 crisis started to unfold, we started to change our buying patterns. All of a sudden, some of the top purchasing items became: antibacterial soap, sanitiser, face masks, yeast and of course, toilet paper. As the demand for these unexpected items exploded, retail supply chains were disrupted. But they weren't the only ones affected.

Artificial intelligence systems began to break too. The MIT Technology Review reports:

Machine-learning models that run behind the scenes in inventory management, fraud detection, and marketing rely on a cycle of normal human behavior. But what counts as normal has changed, and now some are no longer working.

How bad the situation is depends on whom you talk to. According to Pactera Edge, a global AI consultancy, automation is in tailspin. Others say they are keeping a cautious eye on automated systems that are just about holding up, stepping in with a manual correction when needed.

Whats clear is that the pandemic has revealed how intertwined our lives are with AI, exposing a delicate codependence in which changes to our behavior change how AI works, and changes to how AI works change our behavior. This is also a reminder that human involvement in automated systems remains key. You can never sit and forget when youre in such extraordinary circumstances, says Cline.

Image source: MIT Technology Review

The extreme data capturing a previously unseen collapse in consumer spending that feeds the real-time GDP predictor at GDPLive.net, also broke our machine learning algorithms.

2. Extreme data patterns.

The eminent economics and finance historian, Niall Ferguson (not to be confused with Neil Ferguson who also likes to create predictive models) recently remarked that the first month of the lockdown created conditions which took a full year to materialise during the Great Depression.

The chart below shows the consumption data falling off the cliff, generating inputs that broke econometrics and machine learning models.

What we want to see is a rapid V-shaped recovery in consumer spending. The chart below shows the most up-to-date consumer spending trends. Consumer spending has now largely recovered, but is still lower than that of the same period in 2019. One of the key questions will be whether or not this partial rebound will be temporary until the full economic impacts of the 'Great Lockdown' take effect.

Paymark tracks consumer spending on their new public dashboard. Check it out here.

3. Wealth and income inequality.

As the current economic crisis unfolds, GDP will take centre-stage again and all other measures which attempt to quantify wellbeing and social inequalities will likely be relegated until economic stability returns.

When the conversation does return to this topic, AI might have something to contribute.

Effectively addressing income inequality is a key challenge in economics with taxation being the most useful tool. Although taxation can lead to greater equalities, over-taxation discourages from working and entrepreneurship, and motivates tax avoidance. Ultimately this leaves less resources to redistribute. Striking an optimal balance is not straightforward.

The MIT Technology Reviewreports thatAI researchers at the US business technology company Salesforce implemented machine learning techniques that identify optimal tax policies for a simulated economy.

In one early result, the system found a policy thatin terms of maximising both productivity and income equalitywas 16% fairer than a state-of-the-art progressive tax framework studied by academic economists. The improvement over current US policy was even greater.

Image source: MIT Technology Review

It is unlikely that AI will have anything meaningful to contribute towards tackling wealth inequality though. If Walter Scheidel, author of The Great Leveller and professor of ancient history at Stanford is correct, then the only historically effective levellers of inequality are: wars, revolutions, state collapses and...pandemics.

4. Bots and propaganda.

Over the coming months, arguments over what has caused this crisis, whether it was the pandemic or the over-reactive lockdown policies, will occupy much of social media. According to The MIT Technology Review, bots are already being weaponised to fight these battles.

Nearly half of Twitter accounts pushing to reopen America may be bots. Bot activity has become an expected part of Twitter discourse for any politicized event. Across US and foreign elections and natural disasters, their involvement is normally between 10 and 20%. But in a new study, researchers from Carnegie Mellon University have found that bots may account for between 45 and 60% of Twitter accounts discussing covid-19.

To perform their analysis, the researchers studied more than 200 million tweets discussing coronavirus or covid-19 since January. They used machine-learning and network analysis techniques to identify which accounts were spreading disinformation and which were most likely bots or cyborgs (accounts run jointly by bots and humans).

They discovered more than 100 types of inaccurate Covid-19-19 stories and found that not only were bots gaining traction and accumulating followers, but they accounted for 82% of the top 50 and 62% of the top 1,000 influential retweeters.

Image source: MIT Technology Review

How confident are you that you can tell the difference between a human and a bot? You can test yourself out here. BTW, I failed.

5. Primed to believe bad predictions.

This has been a particularly uncertain time. We humans don't like uncertainty especially once it reaches a given threshold. We have an amazing brain that is able to perform complex pattern recognition that enables us to predict what's around the corner. When we do this, we resolve uncertainty and our brain releases dopamine, making us feel good. When we cannot make sense of the data and the uncertainty remains unresolved, then stress kicks in.

Writing on this in Forbes, John Jennings points out:

Research shows we dislike uncertainty so much that if we have to choose between a scenario in which we know we will receive electric shocks versus a situation in which the shocks will occur randomly, well select the more painful option of certain shocks.

The article goes on to highlight how we tend to react in uncertain times. Aversion to uncertainty drives some of us to try to resolve it immediately through simple answers that align with our existing worldviews. For others, there will be a greater tendency to cluster around like-minded people with similar worldviews as this is comforting. There are some amongst us who are information junkies and their hunt for new data to fill in the knowledge gaps will go into overdrive - with each new nugget of information generating a dopamine hit. Lastly, a number of us will rely on experts who will use their crystal balls to find for us the elusive signal in all the noise, and ultimately tell us what will happen.

The last one is perhaps the most pertinent right now. Since we have a built-in drive that seeks to avoid ambiguity, in stressful times such as this, our biology makes us susceptible to accepting bad predictions about the future as gospel especially if they are generated by experts.

Experts at predicting the future do not have a strong track record considering how much weight is given to them. Their predictive models failed to see the Global Financial Crisis coming, they overstated the economic fallout of Brexit, the climate change models and their forecasts are consistently off-track, and now we have the pandemic models.

Image source:drroyspencer.com

The author suggests that this time "presents the mother of all opportunities to practice learning to live with uncertainty". I would also add that a good dose of humility on the side of the experts, and a good dose of scepticism in their ability to accurately predict the future both from the public and decision makers, would also serve us well.

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Massey University's Teo Susnjak on how Covid-19 broke machine learning, extreme data patterns, wealth and income inequality, bots and propaganda and...

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.

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

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*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...

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I am a skeptic of machine learning. There, I've said it. I say this not because I don't think that machine learning is a poor technology - it's actually quite powerful for what it does - but because machine-learning by itself is only half a solution.

To explain this (and the relationship that graphs have to machine learning and AI), it's worth spending a bit of time exploring what exactly machine learning does, how it works. Machine learning isn't actually one particular algorithm or piece of software, but rather the use of statistical algorithms to analyze large amounts of data and from that construct a model that can, at a minimum, classify the data consistently. If it's done right, the reasoning goes, it should then be possible to use that model to classify new information so that it's consistent with what's already known.

Many such systems make use of clustering algorithms - they take a look at data as vectors that can be described in an n-dimensional space. That is to say, there are n different facets that describe a particular thing, such as a thing's color, shape (morphology), size, texture, and so forth. Some of these attributes can be identified by a single binary (does the thing have a tail or not), but in most cases the attributes usually range along a spectrum, such as "does the thing have an an exclusively protein-based diet (an obligate carnivore) or does its does consist of a certain percentage of grains or other plants?". In either case, this means that it is possible to use the attribute as a means to create a number between zero and one (what mathematicians would refer to as a normal orthogonal vector).

Orthogonality is an interesting concept. In mathematics, two vectors are considered orthogonal if there exists some coordinate system in which you cannot express any information about one vector using the other. For instance, if two vectors are at right angles to one another, then there is one coordinate system where one vector aligns with the x-axis and the other with the y-axis. I cannot express any part of the length of a vector along the y axis by multiplying the length of the vector on the x-axis. In this case they are independent of one another.

This independence is important. Mathematically, there is no correlation between the two vectors - they represent different things, and changing one vector tells me nothing about any other vector. When vectors are not orthogonal, one bleeds a bit (or more than a bit) into another. One two vectors are parallel to one another, they are fully correlated - one vector can be expressed as a multiple of the other. A vector in two dimensions can always be expressed as the "sum" of two orthogonal vectors, a vector in three dimensions, can always be expressed as the "sum" of three orthogonal vectors and so forth.

If you can express a thing as a vector consisting of weighted values, this creates a space where related things will generally be near one another in an n-dimensional space. Cats, dogs, and bears are all carnivores, so in a model describing animals, they will tend to be clustered in a different group than rabbits, voles, and squirrels based upon their dietary habits. At the same time cats,, dogs and bears will each tend to cluster in different groups based upon size as even a small adult bear will always be larger than the largest cat and almost all dogs. In a two dimensional space, it becomes possible to carve out a region where you have large carnivores, medium-sized carnivores, small carnivores, large herbivores and so forth.

Machine learning (at its simplest) would recognize that when you have a large carnivore, given a minimal dataset, you're likely to classify that as a bear, because based upon the two vectors size and diet every time you are at the upper end of the vectors for those two values, everything you've already seen (your training set) is a bear, while no vectors outside of this range are classified in this way.

A predictive model with only two independent vectors is going to be pretty useless as a classifier for more than a small set of items. A fox and a dog will be indistinguishable in this model, and for that matter, a small dog such as a Shitsu vs. a Maine Coon cat will confuse the heck out of such a classifier. On the flip side, the more variables that you add, the harder it is to ensure orthogonality, and the more difficult it then becomes determine what exactly is the determining factor(s) for classification, and consequently increasing the chances of misclassification. A panda bear is, anatomically and genetically, a bear. Yet because of a chance genetic mutation it is only able to reasonably digest bamboo, making it a herbivore.

You'd need to go to a very fine-grained classifier, one capable of identifying genomic structures, to identify a panda as a bear. The problem here is not in the mathematics but in the categorization itself. Categorizations are ultimately linguistic structures. Normalization functions are themselves arbitrary, and how you normalize will ultimately impact the kind of clustering that forms. When the number of dimensions in the model (even assuming that they are independent, which gets harder to determine with more variables) gets too large, then the size of hulls for clustering becomes too small, and interpreting what those hulls actually significant become too complex.

This is one reason that I'm always dubious when I hear about machine learning models that have thousands or even millions of dimensions. As with attempting to do linear regressions on curves, there are typically only a handful of parameters that typically drive most of the significant curve fitting, which is ultimately just looking for adequate clustering to identify meaningful patterns - and typically once these patterns are identified, then they are encoded and indexed.

Facial recognition, for instance, is considered a branch of machine learning, but for the most part it works because human faces exist within a skeletal structure that limits the variations of light and dark patterns of the face. This makes it easy to identify the ratios involved between eyes, nose, and mouth, chin and cheekbones, hairlines and other clues, and from that reduce this information to a graph in which the edges reflect relative distances between those parts. This can, in turn, be hashed as a unique number, in essence encoding a face as a graph in a database. Note this pattern. Because the geometry is consistent, rotating a set of vectors to present a consistent pattern is relatively simple (especially for modern GPUs).

Facial recognition then works primarily due to the ability to hash (and consequently compare) graphs in databases. This is the same way that most biometric scans work, taking a large enough sample of datapoints from unique images to encode ratios, then using the corresponding key to retrieve previously encoded graphs. Significantly, there's usually very little actual classification going on here, save perhaps in using courser meshes to reduce the overall dataset being queried. Indeed, the real speed ultimately is a function of indexing.

This is where the world of machine learning collides with that of graphs. I'm going to make an assertion here, one that might get me into trouble with some readers. Right now there's a lot of argument about the benefits and drawbacks of property graphs vs. knowledge graphs. I contend that this argument is moot - it's a discussion about optimization strategies, and the sooner that we get past that argument, the sooner that graphs will make their way into the mainstream.

Ultimately, we need to recognize that the principal value of a graph is to index information so that it does not need to be recalculated. One way to do this is to use machine learning to classify, and semantics to bind that classification to the corresponding resource (as well as to the classifier as an additional resource). If I have a phrase that describes a drink as being nutty or fruity, then these should be identified as classifications that apply to drinks (specifically to coffees, teas or wines). If I come across flavors such as hazelnut, cashew or almond, then these should be correlated with nuttiness, and again stored in a semantic graph.

The reason for this is simple - machine learning without memory is pointless and expensive. Machine learning is fast facing a crisis in that it requires a lot of cycles to train, classify and report. Tie machine learning into a knowledge graph, and you don't have to relearn all the time, and you can also reduce the overall computational costs dramatically. Furthermore, you can make use of inferencing, which are rules that can make use of generalization and faceting in ways that are difficult to pull off in a relational data system. Something is bear-like if it is large, has thick fur, does not have opposable thumbs, has a muzzle, is capable of extended bipedal movement and is omnivorous.

What's more, the heuristic itself is a graph, and as such is a resource that can be referenced. This is something that most people fail to understand about both SPARQL and SHACL. They are each essentially syntactic sugar on top of graph templates. They can be analyzed, encoded and referenced. When a new resource is added into a graph, the ingestion process can and should run against such templates to see if they match, then insert or delete corresponding additional metadata as the data is folded in.

Additionally, one of those pieces of metadata may very well end up being an identifier for the heuristic itself, creating what's often termed a reverse query. Reverse queries are significant because they make it possible to determine which family of classifiers was used to make decisions about how an entity is classified, and from that ascertain the reasons why a given entity was classified a certain way in the first place.

This gets back to one of the biggest challenges seen in both AI and machine learning - understanding why a given resource was classified. When you have potentially thousands of facets that may have potentially been responsible for a given classification, the ability to see causal chains can go a long way towards making such a classification system repeatable and determining whether the reason for a given classification was legitimate or an artifact of the data collection process. This is not something that AI by itself is very good at, because it's a contextual problem. In effect, semantic graphs (and graphs in general) provide a way of making recommendations self-documenting, and hence making it easier to trust the results of AI algorithms.

One of the next major innovations that I see in graph technology is actually a mathematical change. Most graphs that exist right now can be thought of as collections of fixed vectors, entities connected by properties with fixed values. However, it is possible (especially when using property graphs) to create properties that are essentially parameterized over time (or other variables) or that may be passed as functional results from inbound edges. This is, in fact, an alternative approach to describing neural networks (both physical and artificial), and it has the effect of being able to make inferences based upon changing conditions over time.

This approach can be seen as one form of modeling everything from the likelihood of events happening given other events (Bayesian trees) or modeling complex cost-benefit relationships. This can be facilitated even today with some work, but the real value will come with standardization, as such graphs (especially when they are closed network circuits) can in fact act as trainable neuron circuits.

It is also likely that graphs will play a central role in Smart Contracts, "documents" that not only specify partners and conditions but also can update themselves transactional, can trigger events and can spawn other contracts and actions. These do not specifically fall within the mandate of "artificial intelligence" per se, but the impact that smart contracts play in business and society, in general, will be transformative at the very least.

It's unlikely that this is the last chapter on graphs, either (though it is the last in the series about the State of the Graph). Graphs, ultimately, are about connections and context. How do things relate to one another? How are they connected? What do people know, and how do they know them. They underlie contracts and news, research and entertainment, history and how the future is shaped. Graphs promise a means of generating knowledge, creating new models, and even learning. They remind us that, even as forces try to push us apart, we are all ultimately only a few hops from one another in many, many ways.

I'm working on a book calledContext, hopefully out by Summer 2020. Until then, stay connected.

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Artificial Intelligence, Machine Learning and the Future of Graphs - BBN Times

Machine Learning in Communication Market 2025:The latest research report published by Alexa Reports presents an analytical study titled as global Machine Learning in Communication Market 2020. The report is a brief study on the performance of both historical records along with the recent trends. This report studies the Machine Learning in Communication industry based on the type, application, and region. The report also analyzes factors such as drivers, restraints, opportunities, and trends affecting the market growth. It evaluates the opportunities and challenges in the market for stakeholders and provides particulars of the competitive landscape for market leaders.

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This study considers the Machine Learning in Communication value generated from the sales of the following segments:

The key manufacturers covered in this report: Breakdown data in in Chapter:- Amazon, IBM, Microsoft, Google, Nextiva, Nexmo, Twilio, Dialpad, Cisco, RingCentral

Segmentation by Type: Cloud-Based, On-Premise

Segmentation by Application: Network Optimization, Predictive Maintenance, Virtual Assistants, Robotic Process Automation (RPA)

The report studies micro-markets concerning their growth trends, prospects, and contributions to the total Machine Learning in Communication market. The report forecasts the revenue of the market segments concerning four major regions, namely, Americas, Europe, Asia-Pacific, and Middle East & Africa.

The report studies Machine Learning in Communication Industry sections and the current market portions will help the readers in arranging their business systems to design better products, enhance the user experience, and craft a marketing plan that attracts quality leads, and enhances conversion rates. It likewise demonstrates future opportunities for the forecast years 2019-2025.

The report is designed to comprise both qualitative and quantitative aspects of the global industry concerning every region and country basis.

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The report has been prepared based on the synthesis, analysis, and interpretation of information about the Machine Learning in Communication market 2020 collected from specialized sources. The competitive landscape chapter of the report provides a comprehensible insight into the market share analysis of key market players. Company overview, SWOT analysis, financial overview, product portfolio, new project launched, recent market development analysis are the parameters included in the profile.

Some of the key questions answered by the report are:

What was the size of the market in 2014-2019?What will be the market growth rate and market size in the forecast period 2020-2025?What are the market dynamics and market trends?Which segment and region will dominate the market in the forecast period?Which are the key market players, competitive landscape and key development strategies of them?

The last part investigates the ecosystem of the consumer market which consists of established manufacturers, their market share, strategies, and break-even analysis. Also, the demand and supply side is portrayed with the help of new product launches and diverse application industries. Various primary sources from both, the supply and demand sides of the market were examined to obtain qualitative and quantitative information.

Table of ContentsSection 1 Machine Learning in Communication Product DefinitionSection 2 Global Machine Learning in Communication Market Manufacturer Share and Market Overview2.1 Global Manufacturer Machine Learning in Communication Shipments2.2 Global Manufacturer Machine Learning in Communication Business Revenue2.3 Global Machine Learning in Communication Market Overview2.4 COVID-19 Impact on Machine Learning in Communication IndustrySection 3 Manufacturer Machine Learning in Communication Business Introduction3.1 Amazon Machine Learning in Communication Business Introduction3.1.1 Amazon Machine Learning in Communication Shipments, Price, Revenue and Gross profit 2014-20193.1.2 Amazon Machine Learning in Communication Business Distribution by Region3.1.3 Amazon Interview Record3.1.4 Amazon Machine Learning in Communication Business Profile3.1.5 Amazon Machine Learning in Communication Product Specification3.2 IBM Machine Learning in Communication Business Introduction3.2.1 IBM Machine Learning in Communication Shipments, Price, Revenue and Gross profit 2014-20193.2.2 IBM Machine Learning in Communication Business Distribution by Region3.2.3 Interview Record3.2.4 IBM Machine Learning in Communication Business Overview3.2.5 IBM Machine Learning in Communication Product Specification3.3 Microsoft Machine Learning in Communication Business Introduction3.3.1 Microsoft Machine Learning in Communication Shipments, Price, Revenue and Gross profit 2014-20193.3.2 Microsoft Machine Learning in Communication Business Distribution by Region3.3.3 Interview Record3.3.4 Microsoft Machine Learning in Communication Business Overview3.3.5 Microsoft Machine Learning in Communication Product Specification3.4 Google Machine Learning in Communication Business Introduction3.5 Nextiva Machine Learning in Communication Business Introduction3.6 Nexmo Machine Learning in Communication Business IntroductionSection 4 Global Machine Learning in Communication Market Segmentation (Region Level)4.1 North America Country4.1.1 United States Machine Learning in Communication Market Size and Price Analysis 2014-20194.1.2 Canada Machine Learning in Communication Market Size and Price Analysis 2014-20194.2 South America Country4.2.1 South America Machine Learning in Communication Market Size and Price Analysis 2014-20194.3 Asia Country4.3.1 China Machine Learning in Communication Market Size and Price Analysis 2014-20194.3.2 Japan Machine Learning in Communication Market Size and Price Analysis 2014-20194.3.3 India Machine Learning in Communication Market Size and Price Analysis 2014-20194.3.4 Korea Machine Learning in Communication Market Size and Price Analysis 2014-20194.4 Europe Country4.4.1 Germany Machine Learning in Communication Market Size and Price Analysis 2014-20194.4.2 UK Machine Learning in Communication Market Size and Price Analysis 2014-20194.4.3 France Machine Learning in Communication Market Size and Price Analysis 2014-20194.4.4 Italy Machine Learning in Communication Market Size and Price Analysis 2014-20194.4.5 Europe Machine Learning in Communication Market Size and Price Analysis 2014-20194.5 Other Country and Region4.5.1 Middle East Machine Learning in Communication Market Size and Price Analysis 2014-20194.5.2 Africa Machine Learning in Communication Market Size and Price Analysis 2014-20194.5.3 GCC Machine Learning in Communication Market Size and Price Analysis 2014-20194.6 Global Machine Learning in Communication Market Segmentation (Region Level) Analysis 2014-20194.7 Global Machine Learning in Communication Market Segmentation (Region Level) AnalysisSection 5 Global Machine Learning in Communication Market Segmentation (Product Type Level)5.1 Global Machine Learning in Communication Market Segmentation (Product Type Level) Market Size 2014-20195.2 Different Machine Learning in Communication Product Type Price 2014-20195.3 Global Machine Learning in Communication Market Segmentation (Product Type Level) AnalysisSection 6 Global Machine Learning in Communication Market Segmentation (Industry Level)6.1 Global Machine Learning in Communication Market Segmentation (Industry Level) Market Size 2014-20196.2 Different Industry Price 2014-20196.3 Global Machine Learning in Communication Market Segmentation (Industry Level) AnalysisSection 7 Global Machine Learning in Communication Market Segmentation (Channel Level)7.1 Global Machine Learning in Communication Market Segmentation (Channel Level) Sales Volume and Share 2014-20197.2 Global Machine Learning in Communication Market Segmentation (Channel Level) AnalysisSection 8 Machine Learning in Communication Market Forecast 2019-20248.1 Machine Learning in Communication Segmentation Market Forecast (Region Level)8.2 Machine Learning in Communication Segmentation Market Forecast (Product Type Level)8.3 Machine Learning in Communication Segmentation Market Forecast (Industry Level)8.4 Machine Learning in Communication Segmentation Market Forecast (Channel Level)Section 9 Machine Learning in Communication Segmentation Product Type9.1 Cloud-Based Product Introduction9.2 On-Premise Product IntroductionSection 10 Machine Learning in Communication Segmentation Industry10.1 Network Optimization Clients10.2 Predictive Maintenance Clients10.3 Virtual Assistants Clients10.4 Robotic Process Automation (RPA) ClientsSection 11 Machine Learning in Communication 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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Thus, Machine Learning in Communication Market serves as a valuable material for all industry competitors and individuals having a keen interest in the study.

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Trending now: Machine Learning in Communication Market Size, Share, Industry Trends, Growth Insight, Share, Competitive Analysis, Statistics,...

Global Data Science and Machine-Learning Platforms Market Size, Status and Forecast 2020-2026

This report studies the Data Science and Machine-Learning Platforms market with many aspects of the industry like the market size, market status, market trends and forecast, the report also provides brief information of the competitors and the specific growth opportunities with key market drivers. Find the complete Data Science and Machine-Learning Platforms market analysis segmented by companies, region, type and applications in the report.

New vendors in the market are facing tough competition from established international vendors as they struggle with technological innovations, reliability and quality issues. The report will answer questions about the current market developments and the scope of competition, opportunity cost and more.

The major players covered in Data Science and Machine-Learning Platforms Market: SAS, Alteryx, IBM, RapidMiner, KNIME, Microsoft, Dataiku, Databricks, TIBCO Software, MathWorks, H20.ai, Anaconda, SAP, Google, Domino Data Lab, Angoss, Lexalytics, Rapid Insight, etc.

The final report will add the analysis of the Impact of Covid-19 in this report Data Science and Machine-Learning Platforms industry.

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Market Overview:-

Data Science and Machine-Learning Platforms market is segmented by Type, and by Application. Players, stakeholders, and other participants in the global Data Science and Machine-Learning Platforms market will be able to gain the upper hand as they use the report as a powerful resource. The segmental analysis focuses on revenue and forecast by Type and by Application in terms of revenue and forecast for the period 2015-2026.

Data Science and Machine-Learning Platforms Market in its database, which provides an expert and in-depth analysis of key business trends and future market development prospects, key drivers and restraints, profiles of major market players, segmentation and forecasting. An Data Science and Machine-Learning Platforms Market provides an extensive view of size; trends and shape have been developed in this report to identify factors that will exhibit a significant impact in boosting the sales of Data Science and Machine-Learning Platforms Market in the near future.

This report focuses on the global Data Science and Machine-Learning Platforms status, future forecast, growth opportunity, key market and key players. The study objectives are to present the Data Science and Machine-Learning Platforms development in United States, Europe, China, Japan, Southeast Asia, India, and Central & South America.

Market segment by Type, the product can be split into

Market segment by Application, split into

The Data Science and Machine-Learning Platforms market is a comprehensive report which offers a meticulous overview of the market share, size, trends, demand, product analysis, application analysis, regional outlook, competitive strategies, forecasts, and strategies impacting the Data Science and Machine-Learning Platforms Industry. The report includes a detailed analysis of the market competitive landscape, with the help of detailed business profiles, SWOT analysis, project feasibility analysis, and several other details about the key companies operating in the market.

The study objectives of this report are:

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The Data Science and Machine-Learning Platforms market research report completely covers the vital statistics of the capacity, production, value, cost/profit, supply/demand import/export, further divided by company and country, and by application/type for best possible updated data representation in the figures, tables, pie chart, and graphs. These data representations provide predictive data regarding the future estimations for convincing market growth. The detailed and comprehensive knowledge about our publishers makes us out of the box in case of market analysis.

Key questions answered in this report

Table of Contents

Chapter 1: Global Data Science and Machine-Learning Platforms Market Overview

Chapter 2: Data Science and Machine-Learning Platforms Market Data Analysis

Chapter 3: Data Science and Machine-Learning Platforms Technical Data Analysis

Chapter 4: Data Science and Machine-Learning Platforms Government Policy and News

Chapter 5: Global Data Science and Machine-Learning Platforms Market Manufacturing Process and Cost Structure

Chapter 6: Data Science and Machine-Learning Platforms Productions Supply Sales Demand Market Status and Forecast

Chapter 7: Data Science and Machine-Learning Platforms Key Manufacturers

Chapter 8: Up and Down Stream Industry Analysis

Chapter 9: Marketing Strategy -Data Science and Machine-Learning Platforms Analysis

Chapter 10: Data Science and Machine-Learning Platforms Development Trend Analysis

Chapter 11: Global Data Science and Machine-Learning Platforms Market New Project Investment Feasibility Analysis

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Data Science and Machine-Learning Platforms Market (impact of COVID-19) to See Massive Growth by 2026| SAS, Alteryx, IBM, RapidMiner, KNIME,...

The global COVID-19 Impact on Global Artificial Intelligence and Machine Learning Market report is based on comprehensive analysis conducted by experienced and professional experts. The report mentions, factors that are influencing growth such as drivers, restrains of the market. The report offers in-depth analysis of trends and opportunities in the COVID-19 Impact on Global Artificial Intelligence and Machine Learning Market. The report offers figurative estimations and predicts future for upcoming years on the basis of the recent developments and historic data. For the gathering information and estimating revenue for all segments, researchers have used top-down and bottom-up approach. On the basis of data collected from primary and secondary research and trusted data sources the report offers future predictions of revenue and market share.

The Leading Market Players Covered in this Report are : AIBrain,Amazon,Anki,CloudMinds,Deepmind,Google,Facebook,IBM,Iris AI,Apple,Luminoso,Qualcomm .

For Better Understanding, Download FREE Sample Copy of COVID-19 Impact on Global Artificial Intelligence and Machine Learning Report in Just One Single Step @ https://www.researchmoz.us/enquiry.php?type=S&repid2691666

Key Questions Answered in This Report:

Impact of Covid-19 in COVID-19 Impact on Global Artificial Intelligence and Machine Learning Market:The utility-owned segment is mainly being driven by increasing financial incentives and regulatory supports from the governments globally. The current utility-owned COVID-19 Impact on Global Artificial Intelligence and Machine Learning are affected primarily by the COVID-19 pandemic. Most of the projects in China, the US, Germany, and South Korea are delayed, and the companies are facing short-term operational issues due to supply chain constraints and lack of site access due to the COVID-19 outbreak. Asia-Pacific is anticipated to get highly affected by the spread of the COVID-19 due to the effect of the pandemic in China, Japan, and India. China is the epic center of this lethal disease. China is a major country in terms of the chemical industry.

Key Businesses Segmentation of COVID-19 Impact on Global Artificial Intelligence and Machine Learning MarketOn the basis on the end users/applications,this report focuses on the status and outlook for major applications/end users, sales volume, COVID-19 Impact on Global Artificial Intelligence and Machine Learning market share and growth rate of COVID-19 Impact on Global Artificial Intelligence and Machine Learning foreach application, including-

On the basis of product,this report displays the sales volume, revenue (Million USD), product price, COVID-19 Impact on Global Artificial Intelligence and Machine Learning market share and growth rate ofeach type, primarily split into-

COVID-19 Impact on Global Artificial Intelligence and Machine Learning Market Regional Analysis Includes: Asia-Pacific(Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia) Europe(Turkey, Germany, Russia UK, Italy, France, etc.) North America(the United States, Mexico, and Canada.) South America(Brazil etc.) The Middle East and Africa(GCC Countries and Egypt.)

Key Insights that Study is going to provide: The 360-degree COVID-19 Impact on Global Artificial Intelligence and Machine Learning market overview based on a global and regional level Market Share & Sales Revenue by Key Players & Emerging Regional Players Competitors In this section, various COVID-19 Impact on Global Artificial Intelligence and Machine Learning industry leading players are studied with respect to their company profile, product portfolio, capacity, price, cost, and revenue. A separate chapter on COVID-19 Impact on Global Artificial Intelligence and Machine Learning market Entropy to gain insights on Leaders aggressiveness towards market [Merger & Acquisition / Recent Investment and Key Developments] Patent Analysis** No of patents / Trademark filed in recent years.

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Table of Content:Global COVID-19 Impact on Global Artificial Intelligence and Machine Learning Market Size, Status and Forecast 20261. Report Overview2. Market Analysis by Types3. Product Application Market4. Manufacturers Profiles/Analysis5. Market Performance for Manufacturers6. Regions Market Performance for Manufacturers7. Global COVID-19 Impact on Global Artificial Intelligence and Machine Learning Market Performance (Sales Point)8. Development Trend for Regions (Sales Point)9. Upstream Source, Technology and Cost10. Channel Analysis11. Consumer Analysis12. Market Forecast 2020-202613. Conclusion

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COVID-19 Impact on Global Artificial Intelligence and Machine Learning Market Research Methodology: Business Plans, Inventive Technology, Growth...

Friday, June 26, 9am-10:30am Pacific time

Special Topic: Machine Learning & COVID-19: Time is of the Essence

Danilo Tomanovic will cover events including the 2003 SARS Epidemic & aspects of Machine Learning that can be applied to anticipate pandemic risk going forward. This presentation will offer a practical review of key dates & events during this COVID-19 pandemic and offer a fresh perspective on how we may collectively prevent this from happening again on this scale. For the audience this is an opportunity to become engaged as to what can work presently for them in preparation and anticipation of future concerns as they relate to viruses incorporating Machine Learning into their product/service designs.

Danilo Tomanovic's career includes sales, marketing, product development, global transaction banking, investment banking & risk management. He is the President, Founder of Machine Learning Deep Dive focused on creating ML Projects as proof of concepts pointed to market forces/demands.

This briefing will be followed by Q&A and our regular roundtable discussion.

At a Bootstrappers Breakfast(R) we have serious conversations about growing a business based on internal cashflow and organic profit: this is for founders who are actively bootstrapping a startup. We meet in the back room at several Silicon Valley restaurants so space is limited - Please RSVP. Join us upstairs for a little caffeine and sharing among the startup community.

Join other entrepreneurs who eat problems for breakfast.

* Compare Notes

* Exchange Ideas

* Learn from Others Mistakes

* Brainstorm with Peers

* Find Partners

* Small Group Atmosphere

* Serious Conversation

No Charge.

Presented by Bootstrappers Breakfast.

https://www.meetup.com/Bootstr...

events@bootstrappersbreakfast.com

408-252-9676

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Bootstrapper Breakfast: ML & COVID-19 -- Time is of the Essence - Patch.com

A new market report by Market Research Intellect on the Cloud Machine Learning Market has been released with reliable information and accurate forecasts for a better understanding of the current and future market scenarios. The report offers an in-depth analysis of the global market, including qualitative and quantitative insights, historical data, and estimated projections about the market size and share in the forecast period. The forecasts mentioned in the report have been acquired by using proven research assumptions and methodologies. Hence, this research study serves as an important depository of the information for every market landscape. The report is segmented on the basis of types, end-users, applications, and regional markets.

The research study includes the latest updates about the COVID-19 impact on the Cloud Machine Learning sector. The outbreak has broadly influenced the global economic landscape. The report contains a complete breakdown of the current situation in the ever-evolving business sector and estimates the aftereffects of the outbreak on the overall economy.

Get Sample Copy with TOC of the Report to understand the structure of the complete report @ https://www.marketresearchintellect.com/download-sample/?rid=194333&utm_source=COD&utm_medium=888

The report also emphasizes the initiatives undertaken by the companies operating in the market including product innovation, product launches, and technological development to help their organization offer more effective products in the market. It also studies notable business events, including corporate deals, mergers and acquisitions, joint ventures, partnerships, product launches, and brand promotions.

Leading Cloud Machine Learning manufacturers/companies operating at both regional and global levels:

Sales and sales broken down by Product:

Sales and sales divided by Applications:

The report also inspects the financial standing of the leading companies, which includes gross profit, revenue generation, sales volume, sales revenue, manufacturing cost, individual growth rate, and other financial ratios.

The report also focuses on the global industry trends, development patterns of industries, governing factors, growth rate, and competitive analysis of the market, growth opportunities, challenges, investment strategies, and forecasts till 2026. The Cloud Machine Learning Market was estimated at USD XX Million/Billion in 2016 and is estimated to reach USD XX Million/Billion by 2026, expanding at a rate of XX% over the forecast period. To calculate the market size, the report provides a thorough analysis of the market by accumulating, studying, and synthesizing primary and secondary data from multiple sources.

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The market is predicted to witness significant growth over the forecast period, owing to the growing consumer awareness about the benefits of Cloud Machine Learning. The increase in disposable income across the key geographies has also impacted the market positively. Moreover, factors like urbanization, high population growth, and a growing middle-class population with higher disposable income are also forecasted to drive market growth.

According to the research report, one of the key challenges that might hinder the market growth is the presence of counter fit products. The market is witnessing the entry of a surging number of alternative products that use inferior ingredients.

Key factors influencing market growth:

Reasons for purchasing this Report from Market Research Intellect

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Customization of the Report:

Market Research Intellect also provides customization options to tailor the reports as per client requirements. This report can be personalized to cater to your research needs. Feel free to get in touch with our sales team, who will ensure that you get a report as per your needs.

Thank you for reading this article. You can also get chapter-wise sections or region-wise report coverage for North America, Europe, Asia Pacific, Latin America, and Middle East & Africa.

To summarize, the Cloud Machine Learning market report studies the contemporary market to forecast the growth prospects, challenges, opportunities, risks, threats, and the trends observed in the market that can either propel or curtail the growth rate of the industry. The market factors impacting the global sector also include provincial trade policies, international trade disputes, entry barriers, and other regulatory restrictions.

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Cloud Machine Learning Market 2019 Break Down by Top Companies, Countries, Applications, Challenges, Opportunities and Forecast 2026 - Cole of Duty

Through countless all-nighters studying for tests, soccer games and science fairs, William Tobin made the most of his four years in high school.

While Tobin earned recognition for his work on an individual basis, possibly his biggest achievement yet ties all of his work together as one. On May 27, it was announced Tobin has been named one of two U.S. Presidential Scholars for the entire state of West Virginia.

What it means to me to get it is really just its a culmination of everything Ive done in high school, said Tobin, who just graduated from Fairmont Senior High. The different national science fairs, countless hours of studying, the all-nighters Ive pulled, different competitions Ive went to, it really just brings all those together to just one award that recognizes it.

The Presidential Scholar program aims to recognize and reward high school seniors for achievements in test scores and extracurricular activities. Tobins extra-curricular resume is impressive having served as president of the West Virginia Association of Student Councils, vice president of the National Honor Society, vice president of Math Honors and captain of his schools math team.

Though this honor typically includes a trip to Washington, D.C., this year because of the coronavirus, the scholars will be awarded the Presidential Scholars Medallion, sponsored by the White House, and honored for their accomplishments during an online recognition event to ensure the health and safety of the award recipients.

Tobin plans to continue learning at Washington and Lee University, in Lexington City, Virginia, which will be aided by his full ride scholarship he earned as a U.S. Presidential Scholar.

I hope to study computer science and business at Washington & Lee University, Tobin said. After college, I hope to work a couple years in the industry, maybe with machine learning, then I hope to finally start a company with machine learning that combats issues.

Tobin thanks everyone he came in contact with at Fairmont Senior for their role in his high school career. He said he has good relationships with the faculty and administration at the school, having made his mark through his achievements.

He is a good student and an all around good kid, said James Greene, assistant principal at Fairmont Senior. I have dealt with Billy a number of times, and he is definitely worthy of the award, and I also think it goes to support the idea that Fairmont Senior is a top academic institution, and I think the teachers and students that we have here winning these kinds of awards reinforces that.

Greene said that Tobin is the first student at the school to get this award, at least in a while.

This is my sixth year here and I dont recall anyone else winning, Greene said. We have definitely had some high end academic students, but I think part of what separated Billy is his test scores. Getting a perfect ACT is very rare.

Along with tremendous academic success, Tobin played four years on the Polar Bears soccer team and contributed to the teams 2019 state championship in which they defeated Robert C. Byrd High 2-1 in sudden-death overtime. But head coach Darrin Paul said Tobins contributions to the team were not only on the field, but in the classroom with his teammates.

Billy was a great student and a great player, Paul said. He was always willing to help his teammates whether it was to become better players or help them with their homework after school.

Tobin was part of the team that took home the championship last year, which he also said was one of his biggest accomplishments.

Tobin said that even through the coronavirus pandemic, his motivation to pursue machine learning was not hindered. He said the isolation actually drove him to further expand his knowledge in the field.

I was pretty motivated to go into machine learning before this, Tobin said. Especially during this pandemic, Ive had a lot of free time, and I really tried to hone my interests into different types of machine learning.

Tobin also said he hopes to make a difference in situations like this pandemic, because machine learning can be used to study data to predict future events.

I think there will be a lot of different PhDs and dissertations done on this, especially in machine learning, Tobin said. Right now were collecting a bunch of data, but we dont really know what it means... Thats exactly what machine learning does, it looks at a bunch of data and tries to analyze trends.

We are making critical coverage of the coronavirus available for free. Please consider subscribing so we can continue to bring you the latest news and information on this developing story.

Continued here:
On the soccer field, or the classroom, William Tobin is a winner - Times-West Virginian

Footy is back - thankfully - but it's hard to ignore the fact that stadium seats are more or less completely empty for each game.

But, along with the cardboard cut-outs making headlines around the world, one company went above and beyond toreplicate the crowd sounds to fans at home

Tim ONeill is a soundengineer with aFX Global, a company put together almost exclusively totry andmake it sound like a stadium was just as full of noise as it normally would be.

Speaking to Triple M Riverina, O'Neill explained that, when watching Round 1 of the AFL, he felt that the silence behind the play was not going to be good enough as a fan.

So he took the logical next step to... invent new technology to provide the next best thing.

Listen below:

You get the flavour of the stadium but every time we reinterpret it for that match, its unique so you never get that kind of fatigue of something youve heard before, so every game will be different," O'Neill explained.

Hear the full chat below:

Don't miss a minute ofthe action; download theTriple M NRL appnow to listen to the call live or to Catch-Up at anytime.

Link:
Machine Learning, AI, And One Guy Operating The Entire System: How An Aussie Invented The Technology Responsible For The NRL's Crowd Noise - Triple M

Since the evolution of social media platforms like Instagram, Snapchat, and Facebook, we have seen a lot of beauty apps pop-up in the market. Even most of the budget phones of recent times come with beauty filters baked in the default camera app. All these apps apply a ton of filters to your pictures and that can be very annoying. Well, now theres an app that can not only detect, but also remove edits from an image.

Created by Redditor, Akshat Jagga (u/chancemehmu), Mirage is an app that can detect and remove edits by image-editing software on an existing image. According to the Haryana-based developer, the app uses machine learning to perform the tasks.

As shown in a video (below) posted by the developer on Reddit, the app works even on screenshots of pictures.

I recently made an app that uses Machine Learning to detect & undo photoshopped/edited images! Looking for feedback on Mirage. from iphone

You can feed the app an image or a screenshot. It will then analyze it for a few seconds, and show two versions of the image. These will show the original input image, and the image with highlights around areas that have been edited. This can be seen in the video as well.

The next screen shows a similar view. Only in this one, the picture on the right shows the original image, before all the effects and filters. This is the one the app makes after removing the edits from the highlighted areas.

Now, we do not have any idea what kind of machine learning algorithm the app is using to detect and remove the edits of the images. However, it definitely looks interesting.

The app is available on both the App Store and the Play Store. While the App Store version costs $1.99, on the Play Store you can get it for free. However, bear in mind that you will need to subscribe to the app before you are able to use it, which is really annoying.

Download Mirage (Android, iOS)

Original post:
This App Uses Machine Learning to Detect and Remove Edits from Images - Beebom

Recently, a team of researchers from MIT CSAIL recommended that researchers should focus on three key areas that prioritise to deliver computing speed-ups, which are new algorithms, higher-performance software and more specialised hardware, and the need for moving away from focusing on creating only smaller hardware.

The researchers stated that semiconductor miniaturisation is running out of steam as a viable way to grow computer performance, and industries will soon face challenges in their productivity. However, the opportunities for growth in computing performance will still be available if the researchers focus more on software, algorithms, including hardware architecture.

Transistors have brought a plethora of advances and growth in computer performance over the past few decades. These improvements in computer performance come from decades of miniaturisation of computer components, for instance, from a room-sized computer to a cellphone. For decades, programmers have been able to prioritise writing code quickly rather than writing it so that it runs quickly since smaller, faster computer chips have always been able to pick up the slack.

In 1975, Intel founder Gordon Moore predicted the regularity of this miniaturisation trend, which is now called Moores law the number of transistors on computer chips would double every 24 months.

The researchers broke down their recommendations into the categories, they are software, algorithms, and hardware architecture as mentioned below.

According to the researchers, software can be made more efficient by performance engineering such as restructuring the software to make it run faster. Performance engineering can remove inefficiencies in programs, also known as software bloat. Software bloating is an issue that arises from traditional software-development strategies that aim to minimise applications development time rather than the time it takes to run. Performance engineering can also tailor the software to the hardware on which it runs, for example, to take advantage of parallel processors and vector units.

Algorithms offer more-efficient ways to solve problems. The researchers stated that the biggest benefits coming from algorithms are for new problem domains. For instance, machine learning and new theoretical machine models that better reflect emerging hardware.

According to the researchers, hardware architectures can be streamlined through processor simplification, where a complex processing core is replaced with a simpler core that requires fewer transistors. Then, the freed-up transistor budget can be redeployed in other ways. For example, by increasing the number of processor cores running in parallel, which can lead to large efficiency gains for problems that can exploit parallelism.

Also, another form of streamlining is domain specialisation, where hardware is customised for a particular application domain. This type of specialisation discards processor functionality that is not needed for the domain and can allow more customisation to the specific characteristics of the domain by decreasing floating-point precision for artificial intelligence and machine-learning applications.

Researchers have been following Moores law for a few decades now, i.e the overall processing power for computers will double every two years. Software development in the Moore era has generally focused on minimising the time it takes to develop an application, rather than the time it takes to run that application once it is deployed.

The researchers stated that as miniaturisation wanes, the silicon-fabrication improvements at the Bottom will no longer provide the predictable, broad-based gains in computer performance that society has enjoyed for more than 50 years.

In the post-Moore era, performance engineering, development of algorithms, and hardware streamlining will be most effective within big system components. From engineering-management and economic points of view, these changes will be easier to implement if they occur within big system components that include reusable software with typically more than a million lines of code or hardware of comparable complexity or a similarly large software-hardware hybrid.

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Semiconductor Miniaturisation Is Running Out Of Steam. Time To Focus On Smarter Algorithms - Analytics India Magazine

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19 Impact on Global Machine Learning Artificial intelligence Market to Grow at a Stayed CAGR from 2020 to 2026 - Cole of Duty

Telecommuters: tired of the constant embarrassment of showing up to video conferences wearing nothing but your underwear? Save the humiliation and all those pesky trips down to HR with Safe Meeting, the new system that uses the power of artificial intelligence to turn off your camera if you forget that casual Friday isnt supposed to be that casual.

The following infomercial is brought to you by [Nick Bild], who says the whole thing is tongue-in-cheek but we sense a certain degree of necessity is the mother of invention here. Its true that the sudden throng of remote-work newbies certainly increases the chance of videoconference mishaps and the resulting mortification, so whatever the impetus, Safe Meeting seems like a great idea. It uses a Pi cam connected to a Jetson Nano to capture images of you during videoconferences, which are conducted over another camera. The stream is classified by a convolutional neural net (CNN) that determines whether it can see your underwear. If it can, it makes a REST API call to the conferencing app to turn off the camera. The video below shows it in action, and that it douses the camera quickly enough to spare your modesty.

We shudder to think about how [Nick] developed an underwear-specific training set, but we applaud him for doing so and coming up with a neat application for machine learning. Hes been doing some fun work in this space lately, from monitoring where surfaces have been touched to a 6502-based gesture recognition system.

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Machine Learning Takes The Embarrassment Out Of Videoconference Wardrobe Malfunctions - Hackaday

The Global Machine Learning Chip Market Size Is Expected To Reach $8,272 Million In 2022 From $4,495 Million In 2015, Growing At A Cagr Of 9.4% From 2016 To 2022. The Global Machine Learning Chip Market report draws precise insights by examining the latest and prospective industry trends and helping readers recognize the products and services that are boosting revenue growth and profitability. The study performs a detailed analysis of all the significant factors, including drivers, constraints, threats, challenges, prospects, and industry-specific trends, impacting the market on a global and regional scale. Additionally, the report cites worldwide market scenario along with competitive landscape of leading participants.

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1 Report Overview1.1 Study Scope1.2 Key Market Segments1.3 Players Covered1.4 Market Analysis by Type1.5 Market by Application1.6 Study Objectives1.7 Years Considered

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Machine Learning Chip Market Is Thriving Worldwide to reach $8,272 Million by 2022 | Advanced Micro Devices, Inc., Google Inc., Graphcore, Intel...

London, June 03, 2020 (GLOBE NEWSWIRE) -- Artificial intelligence has emerged as the most potent technologies over the past few years, that is transitioning the landscape of almost all industry verticals. Although enterprise applications based on AI and machine learning (ML) are still in the nascent stages of development, they are gradually beginning to drive innovation strategies of the business.

In the supply chain and logistics industry, artificial intelligence is gaining rapid traction among industry stakeholders. Players operating in the supply chain and logistics industry are increasingly realizing the potential of AI to solve the complexities of running a global logistics network. Adoption of artificial intelligence in the supply chain is routing a new era or industrial transformation, allowing the companies to track their operations, enhance supply chain management productivity, augment business strategies, and engage with customers in digital world.

Theartificial intelligence in supply chain market is expected to grow at a CAGR of 45.3% from 2019 to 2027 to reach $21.8 billion by 2027. The growth in this market is mainly driven by rising awareness of artificial intelligence and big data & analytics and widening implementation of computer vision in both autonomous & semi-autonomous applications. In addition, consistent technological advancements in the supply chain industry, rising demand for AI-based business automation solutions, and evolving supply chain complementing growing industrial automation are further offering opportunities for vendors providing AI solutions in the supply chain industry. However, high deployment and operating costs and lack of infrastructure hinder the growth of the artificial intelligence in supply chain market.

In this study, the globalAI in supply chain market is segmented on the basis of component, application, technology, end user, and geography.

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Based on component, AI in supply chain market is broadly segmented into hardware, software, and services. The software segment commanded the largest share of the overall AI in supply chain market in 2019. This can be attributed to the increasing demand for AI-based platforms and solutions, as they offer supply chain visibility through software, which include inventory control, warehouse management, order procurement, and reverse logistics & tracking.

Based on technology, AI in supply chain market is broadly segmented into machine learning, computer vision, natural language processing, and context-aware computing. In 2019, the machine learning segment commanded the largest share of the overall AI in supply chain market. This growth can be attributed to the growing demand for AI-based intelligent solutions; increasing government initiatives; and the ability of AI solutions to efficiently handle and analyze big data and quickly scan, parse, and react to anomalies

Based on application, AI in supply chain market is broadly segmented into supply chain planning, warehouse management, fleet management, virtual assistant, risk management, inventory management, and planning & logistics. In 2019, the supply chain planning segment commanded the largest share of the overall AI in supply chain market. The growth of this segment can be attributed to the increasing demand for enhancing factory scheduling & production planning and the evolving agility and optimization of supply chain decision-making. In addition, digitizing existing processes and workflows to reinvent the supply chain planning model is also contributing to the growth of this segment.

Based on end user, artificial intelligence in supply chain market is broadly segmented into manufacturing, food & beverage, healthcare, automotive, aerospace, retail, and consumer packaged goods sectors. The retail sector commanded the largest share of the overall AI in supply chain market in 2019. This can be attributed to the increase in demand for consumer retail products.

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Based on geography, the global artificial intelligence in supply chain market is categorized into five major geographies, namely, North America, Europe, Asia Pacific, Latin America, and Middle East & Africa. In 2019, North America commanded for the largest share of the global artificial intelligence in supply chain market, followed by Europe, Asia-Pacific, Latin America, and the Middle East & Africa. The large share of the North American region is attributed to the presence of developed economies focusing on enhancing the existing solutions in the supply chain space, and the existence of major players in this market along with a high willingness to adopt advanced technologies.

On the other hand, the Asia-Pacific region is projected to grow at the fastest CAGR during the forecast period. The high growth rate is attributed to rapidly developing economies in the region; presence of young and tech-savvy population in this region; and growing proliferation of internet of things (IoT); rising disposable income; increasing acceptance of modern technologies across several industries including automotive, manufacturing, and retail; and broadening implementation of computer vision technology in numerous applications. Furthermore, the growing adoption of AI-based solutions and services among supply chain operations, increasing digitalization in the region, and improving connectivity infrastructure are also playing a significant role in the growth of this market in the region.

The globalAI in supply chain market is fragmented in nature and is characterized by the presence of several companies competing for the market share. Some of the leading companies in the artificial intelligence in supply chain market are from the core technology background. These include IBM Corporation (U.S.), Microsoft Corporation (U.S.), Google LLC (U.S.), and Amazon.com, Inc. (U.S.). These companies are leading the market owing to their strong brand recognition, diverse product portfolio, strong distribution & sales network, and strong organic & inorganic growth strategies. The other key players in the global artificial intelligence in supply chain market are Intel Corporation (U.S.), Nvidia Corporation (U.S.), Oracle Corporation (U.S.), Samsung (South Korea), LLamasoft, Inc. (U.S.), SAP SE (Germany), General Electric (U.S.), Deutsche Post DHL Group (Germany), Xilinx, Inc. (U.S.), Micron Technology, Inc. (U.S.), FedEx Corporation (U.S.), ClearMetal, Inc. (U.S.), Dassault Systmes (France), and JDA Software Group, Inc. (U.S.), among others.

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Artificial Intelligence (AI) in Supply Chain Market is projected to reach $21.8 billion by 2027, Growing at a CAGR of 45.3% from 2019- Meticulous...

Special Operations Commands Gen. Richard Clarke with students at the Special Forces Qualification Course.

WASHINGTON: Special Operations Command is in a war for influence with adversaires from non-state groups to state-funded information operations, the commands top general said recently, and is rushing to fund artificial intelligence and machine learning programs to find an edge.

Were going to have to have artificial intelligence and machine learning tools, specifically for information ops that hit a very broad portfolio, SOCOM commander Gen. Richard Clarke said recently, because were going to have to understand how the adversary is thinking, how the population is thinking, and work in these spaces.

Special Operations have cultivated an image in popular culture over two decades of constant war in the Middle East as almost superhuman door kickers dropping from the sky to blast their way quickly through an objective, disappearing as quickly as they had arrived. That view has in part led policymakers and the public to look to these troops as a solution to almost any problem, placing an enormous burden on a force of about 70,000 troops.

Clarke said that kinetic mission wont change any time soon, but other missions the various tribes of SOCOM and SOF have always performed intelligence gathering, training and advising, and influence operations need to be reprioritized.

We need coders, he told the virtual Special Operations Forces Industry Conference last month. Weve been having discussions internally that the most important person on the mission is no longer the operator kicking down the door, but the cyber operator who the team has to actually get to the environment so he or she can work their cyber tools into the fight.

SOCOM has started using AI in developing information operations in places like Afghanistan, but the commands interest is hardly limited to that space.

Acquisition chief Jim Smith told the conference his team is looking at a wide range of applications for employing AI, including intel gathering and fusion, surveillance and reconnaissance, precision fires, and health and training efforts. All of these functions are time and manpower-intensive, requiring long hours and entire teams to collect, understand, analyze, and move data, sometimes forcing troops to react as opposed to seizing initiative.

Those tasks are becoming more critical as defense budgets tighten and adversaries catch up and even surpass US capabilities across a wide range of technologies and capabilities.

So how do we use artificial intelligence and machine learning to get those sensors to interoperate autonomously and provide feedback to a single operator to enable that force to maneuver on the objective? Smith asked, noting that this is one of the biggest issues his office is coping with/.

Think of those small UAVs or your small ground vehicles and give them enough artificial intelligence and machine learning to be able to be autonomous, so that they can clear a building or they can clear a tunnel, which then allows the maneuver force to focus on other tasks.

These technologies could also help operators in the field launch countermeasures to intercept and disrupt enemy communications, which right now can be a slow process.

Today the way we do that is we have a library of threat radar signatures Smith said, and if you see one of those threat radars in our library we counter it. So SOCOM is looking for ways to use machine learning to identify anomalies in this space so it wasnt just the threat radars we had loaded into the library, that were already known, but maybe its a new radar that we havent seen before or a radar that we didnt realize was operating in that theater that we could identify.

Smith said his approach is to bake in AI and machine learning requirements with every program that SOCOM develops from here on out.

What were starting to see is our industry partners coming in on proposals and theyre baking in artificial intelligence and machine learning, he said. Thats exactly where we want to be.

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SOCOM Looking To Bake In AI Requirements On Every New Program - Breaking Defense

WILMINGTON, Del., June 03, 2020 (GLOBE NEWSWIRE) -- InterDigital, Inc. (NASDAQ:IDCC), a mobile and video technology research and development company, today introduced the worlds first cloud gaming solution with an AI and machine learning-enabled user interface, presented in collaborative partnership with cloud gaming trailblazer Blacknut and in cooperation with GPU pioneer Nvidia. The tripartite collaboration represents the first time that an AI and machine learning-driven user interface is utilized, wearable-free, with a live cloud gaming solution. The technology demonstrates the incredible potential of integrating localized and far-Edge enabled AI capabilities into home gaming experiences.

The AI and machine learning-enabled user interface is connected to a cloud gaming solution that operates without joysticks or wearable accessories. The demonstration leverages unique technologies, including real-time video analysis on home and local edge devices, dynamic adaptation to available compute resources, and shared AI models managed through an in-home AI hub, to implement a cutting-edge gaming experience.

In the demonstration, users play a first-person view snowboarding game streamed by Blacknut and displayed on a commercial television. Users do not require a joystick or handheld controller to play the game; instead, their movements and interactions are tracked by AI processing of the live video capture of the users movements. The users presence is detected using an AI model and his or her body movements are matched with the snowboarder in the game, in real time, using InterDigitals low latency Edge AI running on a local AI accelerator. The groundbreaking demo addresses the challenges of ensuring the lowest possible end-to-end latency from gesture capture to game action, while accelerating inference of concurrent AI models serving multiple applications to deliver an interactive and more seamless gaming experience. This demonstration enables AI and machine learning tasks to be completed locally, revolutionizing our current implementation of cloud gaming solutions.

We are so proud of the work of this demonstration, as it displays the real potential of AI and edge computing, highlights the power of industry collaboration, and helps blaze a trail for new cloud gaming capabilities. Of course, such a success would not have been possible without the utmost implication of all the teams from Interdigital, Blacknut, and Nvidia, and I would like to take the opportunity to credit and thank their outstanding work, said Laurent Depersin, Director of the Home Experience Lab at InterDigital.

The far-Edge AI and machine learning technologies put forth by InterDigital bring a plethora of new capabilities to the cloud gaming experience. Far-Edge AI enables low-latency analysis to deliver an interactive and entertaining experience, reduces cloud computing costs by leveraging available computing resources, and saves significant bandwidth by prioritizing up-linking. In addition, far-Edge AI in edge cloud architecture offers an important solution for privacy concerns by localizing computing and supports a variety of new and emerging vertical applications beyond gaming, including smart home and security, remote healthcare, and robotics.

Cloud gaming with far-Edge AI leverages artificial intelligence and localized Edge computing to showcase the ways an interactive television or gaming experience can be enhanced by the localized AI analysis of a cameras video stream. Ongoing research in the real-time processing of user generated data will drive new innovations and vertical applications in the home, from cloud gaming to remote medical care, and those innovations will be enhanced by the ability to execute artificial intelligence models under low latency conditions.

Blacknuts mission is to bring to our customers unlimited hours of gaming fun in the simplest manner, said Pascal Manchon, CTO at Blacknut. Our unique cloud gaming solution allows to free games from dedicated consoles or hardware. Using AI and machine learning to transform the human body itself in a full-fledge game controller was challenging but Blacknuts close collaboration with Interdigital and NVidia led to outstanding performances. And yes, it is addictive and fun to play this way!

Cloud gaming is an exciting industry use case that leverages innovations in network architecture, video streaming and content delivery to shape the future of interactive gaming and entertainment. This worlds first cloud gaming solution, and the broader exploration of AI-enabled cloud solutions, would not be possible without a commitment to collaboration with industry leaders and partners.

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InterDigital, Blacknut, and Nvidia unveil worlds first Cloud gaming solution with AI-enabled user interface - TelecomTV