Letters to the editor – The Economist

Jul 4th 2020

Artificial intelligence is an oxymoron (Technology quarterly, June 13th). Intelligence is an attribute of living things, and can best be defined as the use of information to further survival and reproduction. When a computer resists being switched off, or a robot worries about the future for its children, then, and only then, may intelligence flow.

I acknowledge Richard Suttons bitter lesson, that attempts to build human understanding into computers rarely work, although there is nothing new here. I was aware of the folly of anthropomorphism as an AI researcher in the mid-1980s. We learned to fly when we stopped emulating birds and studied lift. Meaning and knowledge dont result from symbolic representation; they relate directly to the visceral motives of survival and reproduction.

Great strides have been made in widening the applicability of algorithms, but as Mr Sutton says, this progress has been fuelled by Moores law. What we call AI is simply pattern discovery. Brilliant, transformative, and powerful, but just pattern discovery. Further progress is dependent on recognising this simple fact, and abandoning the fancy that intelligence can be disembodied from a living host.

ROB MACDONALDRichmond, North Yorkshire

I agree that machine learning is overhyped. Indeed, your claim that such techniques are loosely based on the structure of neurons in the brain is true of neural networks, but these are just one type among a wide array of different machine- learning methods. In fact, machine learning in some cases is no more than a rebranding of existing processes. If by machine learning we simply mean building a model using large amounts of data, then good old ordinary least squares (line of best fit) is a form of machine learning.

TOM ARMSTRONGToronto

The scope of your research into green investing was too narrow to condemn all financial services for their woolly thinking (Hotting up, June 20th). You restricted your analysis to microeconomic factors and to the ability of investors to engage with companies. It overlooked the bigger picture: investors can also shape the macro environment by structured engagement with the system itself.

For example, the data you used largely originated from the investor-led Carbon Disclosure Project (for which we hosted the first ever meeting, nearly two decades ago). In addition, investors have also helped shape sustainable-finance plans in Britain, the EU and UN. Investors also sit on the industry-led Taskforce on Climate-related Financial Disclosure, convened by the Financial Stability Board, which has proved effective.

It is critical that governments apply a meaningful carbon price. But if we are to move money at the pace and scale required to deal with climate risk, governments need to reconsider the entire architecture of markets. This means focusing a wide-angled climate lens on prudential regulation, listing rules, accounting standards, investor disclosure standards, valuation conventions and stewardship codes, as well as building on new interpretations of legal fiduciary duty. This work is done most effectively in partnership with market participants. Green-thinking investors can help.

STEVE WAYGOODChief responsible investment officerAviva InvestorsLondon

Estimating indirectly observable GDP in real time is indeed a hard job for macro-econometricians, or wonks, as you call us (Crisis measures, May 30th). Most of the components are either highly lagged, as your article mentioned, or altogether unobservable. But the textbook definition of GDP and its components wont be changing any time soon, as the reader is led to believe. Instead what has always and will continue to change are the proxy indicators used to estimate the estimate of GDP.

MICHAEL BOERMANWashington, DC

Reading Lexingtons account of his garden adventures (June 20th) brought back memories of my own experience with neighbours in Twinsburg, Ohio, in the late 1970s. They also objected to vegetables growing in our front yard (the only available space). We were doing it for the same reasons as Lexington: pleasure, fresh food to eat, and a learning experience for our young children. The neighbours, recently arrived into the suburban middle class, saw it as an affront. They no longer had to grow food for their table. They could buy it at the store and keep it in the deep freeze. Our garden, in their face every day, reminded them of their roots in Appalachian poverty. They called us hillbillies.

Arthur C. Clarke once wrote: Any sufficiently advanced technology is indistinguishable from magic. Our version read, Any sufficiently advanced lifestyle is indistinguishable from hillbillies.

PHILIP RAKITAPhiladelphia

Bartleby (May 30th) thinks the benefits of working from home will mean that employees will not want to return to the office. I am not sure that is the case for many people. My husband is lucky. He works for a company that already expected its staff to work remotely, so had the systems and habits in place. He has a spacious room to work in, with an adjustable chair, large monitor and a nice view. I do not work so he is not responsible for child care or home schooling.

Many people are working at makeshift workspaces which would make an occupational therapist cringe. Few will have a dedicated room for their home office, so their work invades their mental and physical space.

My husband has noticed that meetings are being set up both earlier and later in the day because there is an assumption that, as people are not commuting, it is fine to extend their work day. Colleagues book a half-hour meeting instead of dropping by someones desk to ask a quick question. Any benefit of not commuting is lost. My husband still struggles to finish in time to have dinner with our children. People with especially long commutes now have more time, but even that was a change of scenery and offered some incidental exercise.

JENNIFER ALLENLondon

As Bartleby pointed out, the impact of pandemic working conditions wont be limited to the current generation. By exacerbating these divides, will covid-19 completely guarantee a future dominated by the baby-Zoomers?

MALCOLM BEGGTokyo

The transition away from the physical office engenders a lackadaisical approach to the work day for many workers. It brings to mind Ignatius Reillys reasoning for his late start at the office from A Confederacy of Dunces:

I avoid that bleak first hour of the working day during which my still sluggish senses and body make every chore a penance. I find that in arriving later, the work which I do perform is of a much higher quality.

ROBERT MOGIELNICKIArlington, Virginia

This article appeared in the Letters section of the print edition under the headline "On artificial intelligence, green investing, GDP, gardens, working from home"

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Letters to the editor - The Economist

What is machine learning? | IBM

Machine learning follows a process of preparing data, training an algorithm and generating a machine learning model, and then making and refining predictions.

Preparing the data

Machine learning requires data that is analyzed, formatted and conditioned to build a machine learning model. Judith Hurwitz and Daniel Kirsch, authors of Machine Learning For Dummies, advise that machine learning requires the right set of data that can be applied to a learning process. Data preparation typically involves these tasks:

Training the algorithm

Machine learning uses the prepared data to train a machine learning algorithm. An algorithm is a computerized procedure or recipe. When the algorithm is trained on the data, a machine learning model is generated. Selecting the right algorithm is essential to applying machine learning successfully. Selection is largely influenced by the application and the data available. But there are some commonly used algorithms and applications:

Predicting and refining

Once the data is prepared and the algorithm trained, the machine learning model can make determinations or predictions about the data on its own. For example:

Consider a data set that has two basic values for cars: weight and speed. Values can be plotted on a graph that shows light cars tend to be fast and heavy cars tend to be slow.

When the machine learning model is provided with data about cars, it uses the algorithm to determine or predict whether a car will tend to be fast or slow, or light or heavy. It does this without explicit human intervention. And the more data provided, the more the model learns and improves the accuracy of its predictions.

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What is machine learning? | IBM

How Does AIOps Integrate AI and Machine Learning into IT Operations? – Analytics Insight

Data is everywhere growing across variety and velocity in both structured and unstructured formats. Leveraging this chaotic data generated at ever-increasing speeds is often a mammoth task. Even powerful AI and machine learning capabilities lose their accuracy if they dont have the right data to support them. The rise in data complexity, makes it challenging for IT operations to get the best from Artificial Intelligence and ML algorithms for digital transformation.

The secret lies in acknowledging this data, to use its explosion as an opportunity to drive intelligence, automation, effectiveness and productivity with Artificial intelligence for IT operations (AIOps). In simple words, AIOps refers to the automation of IT operations artificial intelligence (AI), freeing enterprise IT operations by inputs of operational data to achieve the ultimate data automation goals.

AIOps of any enterprise stands firmly on four pillars, collectively referred to as the key dimensions of IT operations monitoring:

Data Selection & Filtering

Modern IT environments create noisy IT data, collating this data and filtering for Excel, AI and ML models is a tedious task. Taking massive amounts of redundant data selecting data elements of interest often means filtering out up to 99% of data.

Discovering Data Patterns

Unearthing data patterns implies to collate filtered data to establish meaningful relationships between the selected data groups for further analysis.

Data Collaboration

Data analysis fosters collaboration among interdisciplinary teams across global enterprises, besides preserving valuable data intelligence that can accelerate future synergies within the enterprise.

Solution Automation

This dimension relates to automating data responses and remediation, in a bid to more precise solutions achieved at a quicker TAT.

A responsible AIOps platform combines AI, machine learning and big data with a mature understanding of IT operations. It makes way to assimilate real-time and historical data from any source for cutting edge AI and ML capabilities. This makes it possible for enterprises to get a hold of problems before they even happen by leveraging on clustering, anomaly detection, prediction, statistical thresholding, predictive analytics, forecasting, and more.

IT environments have broken silos and currently exceeding the realms of the manual human scale of operations. Traditional approaches to managing IT find redundancy over the dynamic environments governed by technology.

1. Data pipelines that ITOps need to retain is exponentially increasing encompassing a larger number of events and alerts. With the introduction of APIs, digital or machine users, mobile applications, and IoT devices, modern enterprises receive higher service ticket volumes. A trend that is becoming too complex for manual reporting and analysis.

2. As organizations walk on the digital transformation path, seamless ITOps becomes indispensable. The accessibility of technology has changed user expectations across industries and vertices. This calls for an immediate reaction to IT events especially when an issue impacts user experience.

3. The introduction of edge computing and cloud infrastructure empowers the line of business (LOB) functions to build and host their own IT solutions and applications over the cloud to be accessed anytime anywhere. This calls for an increase in budgetary allocation increase and more computing power (that can be leveraged) to be added from outside core IT.

AIOps bridges the gap between service management, performance management, and automation within the IT eco-system to accomplish the continuous goal of IT operation improvements. AIOps creates a game plan that delivers within the new accelerated IT environments, to identify patterns in monitoring, service desk, capacity addition and data automation across hybrid on-premises and multi-cloud environments.

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Kamalika Some is an NCFM level 1 certified professional with previous professional stints at Axis Bank and ICICI Bank. An MBA (Finance) and PGP Analytics by Education, Kamalika is passionate to write about Analytics driving technological change.

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How Does AIOps Integrate AI and Machine Learning into IT Operations? - Analytics Insight

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

The 19 Impact on Global Machine Learning Artificial intelligence market research report added by Market Study Report, LLC, is a thorough analysis of the latest trends prevalent in this business. The report also dispenses valuable statistics about market size, participant share, and consumption data in terms of key regions, along with an insightful gist of the behemoths in the 19 Impact on Global Machine Learning Artificial intelligence market.

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CVPR 2020 Convenes Thousands from the Global AI, Machine Learning and Computer Vision Community in Virtual Event Beginning Sunday – PRNewswire

LOS ALAMITOS, Calif., June 12, 2020 /PRNewswire/ --The Computer Vision and Pattern Recognition (CVPR) Conference, one of the largest events exploring artificial intelligence, machine learning, computer vision, deep learning, and more, will take place 14-19 June as a fully virtual event. Over the course of six days, the event will feature 45 sessions delivered by 1467 leading authors, academics, and experts to more than 6500 attendees, who have already registered for the event.

"The excitement, enthusiasm, and support for CVPR from the global community has never been more apparent," said Professor of Computer Science at Cornell University and Co-Chair of the CVPR 2020 Committee Ramin Zabih. "With large attendance, state of the art research, and insights delivered by some of the leading authorities in computer vision, AI, and machine learning, our first-ever fully virtual event is shaping up to be an exciting experience for everyone involved."

As a fully virtual event, attendees will have access to all CVPR program components, including fireside chats, workshops, tutorials, and oral and poster presentations via a robust, fully searchable, password-protected portal. Credentials to access the portal are provided to attendees shortly upon registration.

CVPR fireside chats, workshops, and tutorials will be conducted via live video with live Q&A between presenters and participants. Oral and poster presentations, which will be repeated, will include a pre-recorded video from the presenter(s), followed by a live Q&A session. Attendees will also be able to access presentations/papers and the pre-recorded videos at their convenience to help ensure maximum access given the diverse time zones in which conference participants live. Additionally, CVPR participants can leverage complementary video chat features and threaded question and answer commenting associated with each session and each sponsor to support further knowledge sharing and understanding. Multiple online networking events with video and text chat elements are also included.

"The CVPR Committee has gone to great lengths to deliver a first-in-class virtual conference experience that all attendees can enjoy," said IEEE Computer Society Executive Director Melissa Russell, co-sponsor of the event. "We are thrilled to be part of this endeavor and are excited to deliver and witness in the coming days the 'what's next' in AI, computer vision and machine learning."

Details on the full virtual CVPR 2020 schedule can be found on the conference website at http://cvpr2020.thecvf.com/program. All times are Pacific Daylight Time (Seattle Time).

Interested individuals can still register for CVPR at http://cvpr2020.thecvf.com/attend/registration. Accredited members of the media can register for the CVPR virtual conference by emailing [emailprotected].

About CVPR 2020CVPR is the premier annual computer vision and pattern recognition conference. With first-in-class technical content, a main program, tutorials, workshops, a leading-edge expo, and attended by more than 9,000 people annually, CVPR creates a one-of-a-kind opportunity for networking, recruiting, inspiration, and motivation. CVPR 2020, originally scheduled to take place 14-19 June 2020 at the Washington State Convention Center in Seattle Washington, will now be a fully virtual event. Authors and presenters will virtually deliver presentations and engage in live Q&A with attendees. For more information about CVPR 2020, the program, and how to participate virtually, visit http://cvpr2020.thecvf.com/.

About the Computer Vision FoundationThe Computer Vision Foundation is a non-profit organization whose purpose is to foster and support research on all aspects of computer vision. Together with the IEEE Computer Society, it co-sponsors the two largest computer vision conferences, CVPR and the International Conference on Computer Vision (ICCV).

About the IEEE Computer SocietyThe IEEE Computer Society is the world's home for computer science, engineering, and technology. A global leader in providing access to computer science research, analysis, and information, the IEEE Computer Society offers a comprehensive array of unmatched products, services, and opportunities for individuals at all stages of their professional career. Known as the premier organization that empowers the people who drive technology, the IEEE Computer Society offers international conferences, peer-reviewed publications, a unique digital library, and training programs. Visit http://www.computer.org for more information.

SOURCE IEEE Computer Society

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CVPR 2020 Convenes Thousands from the Global AI, Machine Learning and Computer Vision Community in Virtual Event Beginning Sunday - PRNewswire

InterDigital, Blacknut, and Nvidia Unveil World’s First Cloud Gaming Solution With AI-Enabled User Interface – GlobeNewswire

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.

To learn more about the demonstration of the worlds first cloud gaming solution with AI-enabled user interface, please click here.

About InterDigital

InterDigital develops mobile and video technologies that are at the core of devices, networks, and services worldwide. We solve many of the industrys most critical and complex technical challenges, inventing solutions for more efficient broadband networks, better video delivery, and richer multimedia experiences years ahead of market deployment. InterDigital has licenses and strategic relationships with many of the worlds leading technology companies. Founded in 1972, InterDigital is listed on NASDAQ and is included in the S&P MidCap 400 index.

InterDigital is a registered trademark of InterDigital, Inc.

For more information, visit: http://www.interdigital.com.

About Blacknut

Blacknut was founded in 2016 by Olivier Avaro (CEO) and is headquartered in Rennes, France, with offices in Paris and San Francisco. Blacknut designs, develops and commercializes a cloud gaming service. Blacknut first launched in France in 2018, for PC, Mac, and Linux. The service allows to play more than 400 premium games for a monthly subscription fee. Blacknut is now available across Europe & North America on a wider range of devices, including mobiles, set-top-boxes and Smart TVs. Blacknut is also distributed through major ISPs, device manufacturers, OTT services & Media companies.

For more information, visit: http://www.blacknut.com

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InterDigital, Blacknut, and Nvidia Unveil World's First Cloud Gaming Solution With AI-Enabled User Interface - GlobeNewswire

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

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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Leading Players in the Machine Learning Chip Market:

The Machine Learning Chip market analysis is intended to provide all participants and vendors with pertinent specifics about growth aspects, roadblocks, threats, and lucrative business opportunities that the market is anticipated to reveal in the coming years. This intelligence study also encompasses the revenue share, market size, market potential, and rate of consumption to draw insights pertaining to the rivalry to gain control of a large portion of the market share.

By Type

By Application

Competitive landscape

The Machine Learning Chip Industry is extremely competitive and consolidated because of the existence of several established companies that are adopting different marketing strategies to increase their market share. The vendors engaged in the sector are outlined based on their geographic reach, financial performance, strategic moves, and product portfolio. The vendors are gradually widening their strategic moves, along with customer interaction.

Machine Learning Chip Market Segmented by Region/Country: US, Europe, China, Japan, Middle East & Africa, India, Central & South America

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Points Covered in the Report:

Fundamentals of Table of Content:

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

2 Global Growth Trends2.1 Machine Learning Chip Market Size2.2 Machine Learning Chip Growth Trends by Regions2.3 Industry Trends

3 Market Share by Key Players3.1 Machine Learning Chip Market Size by Manufacturers3.2 Machine Learning Chip Key Players Head office and Area Served3.3 Key Players Machine Learning Chip Product/Solution/Service3.4 Date of Enter into Machine Learning Chip Market3.5 Mergers & Acquisitions, Expansion Plans

4 Breakdown Data by Product4.1 Global Machine Learning Chip Sales by Product4.2 Global Machine Learning Chip Revenue by Product4.3 Machine Learning Chip Price by Product

5 Breakdown Data by End User5.1 Overview5.2 Global Machine Learning Chip Breakdown Data by End User

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Q&A on the Book Hands-On Genetic Algorithms with Python – InfoQ.com

Key Takeaways

Hands-On Genetic Algorithms with Python by Eyal Wirsansky is a new book which explores the world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models. InfoQ interviewed Eyal Wirsansky about how genetic algorithms work and what they can be used for.

In addition to our interview, InfoQ was able to obtain a sample chapter which can be downloaded here.

InfoQ: How do genetic algorithms work?

Eyal Wirsansky: Genetic algorithms are a family of search algorithms inspired by the principles of evolution in nature. They imitate the process of natural selection and reproduction, by starting with a set of random solutions, evaluating each one of them, then selecting the better ones to create the next generation of solutions. As generations go by, the solutions we have get better at solving the problem. This way, genetic algorithms can produce high-quality solutions for various problems involving search, optimization, and learning. At the same time, their analogy to natural evolution allows genetic algorithms to overcome some of the hurdles encountered by traditional search and optimization algorithms, especially for problems with a large number of parameters and complex mathematical representations.

InfoQ: What type of problems do genetic algorithms solve?

Wirsansky: Genetic algorithms can be used for solving almost any type of problem, but they particularly shine where traditional algorithms cannot be used, or fail to produce usable results within a practical amount of time. For example, problems with very complex or non-existing mathematical representation, problems where the number of variables involved is large, and problems with noisy or inconsistent input data. In addition, genetic algorithms are better equipped to handle deceptive problems, where traditional algorithms may get trapped in a suboptimal solution.

Genetic algorithms can even deal with cases where there is no way to evaluate an individual solution by itself, as long as there is a way to compare two solutions and determine which of them is better. An example can be a machine learning-based agent that drives a car in a simulated race. A genetic algorithm can optimize and tune the agent by having different versions of it compete against each other to determine which version is better.

InfoQ: What are the best use cases for genetic algorithms?

Wirsansky: The most common use case is where we need to assemble a solution using a combination of many different available parts; we want to select the best combination, but the number of possible combinations is too large to try them all. Genetic algorithms can usually find a good combination within a reasonable amount of time. Examples can be scheduling personnel, planning of delivery routes, designing bridge structures, and also constructing the best machine learning model from many available building blocks, or finding the best architecture for a deep learning model.

Another interesting use case is where the evaluation is based on peoples opinion or response. For example, you can use the genetic algorithm approach to determine the design parameters for a web sitesuch as color palette, font size, and location of components on the pagethat will achieve the best response from customers, such as conversion or retention. This idea can also be used for genetic art artificially created paintings or music that prove pleasant to the human eye (or ear).

Genetic algorithms can also be used for ongoing optimizationcases where the best solution may change over time. The algorithm can run continuously within the changing environment and respond dynamically to these changes by updating the best solution based on the current generation.

InfoQ: How can genetic algorithms select the best subset of features for supervised learning?

Wirsansky: In many cases, reducing the number of featuresused as inputs for a model in supervised learningcan increase the models accuracy, as some of the features may be irrelevant or redundant. This will also result in a simpler, better generalizing model. But we need to figure out which are the features that we want to keep. As this comes down to finding the best combination of features out of a potentially immense number of possible combinations, genetic algorithms provide a very practical approach. Each potential solution is represented by a list of booleans, one for each feature.

The value of the boolean (0 or 1) represents the absence or presence of the corresponding feature. These lists of boolean values are used as genetic material, that can be exchanged between solutions when we mate them, or even mutated by flipping values randomly. Using these mating and mutation operations, we create new generations out of preceding ones, while giving an advantage to solutions that yielded better performing models. After a while, we can have some good solutions, each representing a subset of the features. This is demonstrated in Chapter 7 of the book (our sample chapter) with the UCI Zoo dataset using python code, where the best performance was achieved by selecting six particular features out of the original sixteen.

InfoQ: What are the benefits that we can get from using genetic algorithms with machine learning for hyperparameter tuning?

Wirsansky: Every machine learning model utilizes a set of hyperparametersvalues that are set before the training takes place and affect the way the learning is done. The combined effect of hyperparameters on the performance of the model can be significant. Unfortunately, finding the best combination of the hyperparameter valuesalso known as hyperparameter tuningcan be as difficult as finding a needle in a haystack.

Two common approaches are grid search and random search, each with its own disadvantages. Genetic algorithms can be used in two ways to improve upon these methods. One way is by optimizing the grid search, so instead of trying out every combination on the grid, we can search only a subset of combinations but still get a good combination. The other way is to conduct a full search over the hyperparameter space, as genetic algorithms are capable of handling a large number of parameters as well as different parameter types continuous, discrete and categorical. These two approaches are demonstrated in Chapter 8 of the book with the UCI Wine dataset using python code.

InfoQ: How can genetic algorithms be used in Reinforcement Learning?

Wirsansky: Reinforcement Learning (RL) is a very exciting and promising branch of machine learning, with the potential to handle complex, everyday-life-like tasks. Unlike supervised learning, RL does not present an immediate 'right/wrong' feedback, but instead provides an environment where a longer-term, cumulative reward is sought after. This kind of setting can be viewed as an optimization problem, another area where genetic algorithms excel.

As a result, genetic algorithms can be utilized for reinforcement learning in several different ways. One example can be determining the weights and biases of a neural network that interacts with its environment by mapping input values to output values. Chapter 10 of the book includes two examples of applying genetic algorithms to RL tasks, using the OpenAI Gym environments mountain-car and cart-pole.

InfoQ: What is bio-inspired computing?

Wirsansky: Genetic algorithms are just one branch within a larger family of algorithms called Evolutionary Computation, all inspired by Darwinian evolution. One particularly interesting member of this family is Genetic Programming, that evolves computer programs aiming to solve a specific problem. More broadly, as evolutionary computation techniques are based on various biological systems or behaviors, they can be considered part of the algorithm family known as Bio-inspired Computing.

Among the many fascinating members of this family are Ant Colony Optimizationimitating the way certain species of ants locate food and mark the paths to it, giving advantage to closer and richer locations of food; Artificial Immune Systems, capable of identifying and learning new threats, as well as applying the acquired knowledge and respond faster the next time a similar threat is detected; and Particle Swarm Optimization, based on the behavior of flocks of birds or schools of fish, where individuals within the group work together towards a common goal without central supervision.

Another related, broad field of computation is Artificial Life, involving systems and processes imitating natural life in different ways, such as computer simulations and robotic systems. Chapter 12 of the book includes two relevant Python-written examples, one solving a problem using genetic programming, and the otherusing particle swarm optimization.

Eyal Wirsansky is a senior software engineer, a technology community leader, and an artificial intelligence researcher and consultant. Eyal started his software engineering career as a pioneer in the field of voice over IP, and he now has over 20 years' experience of creating a variety of high-performing enterprise solutions. While in graduate school, he focused his research on genetic algorithms and neural networks. One outcome of his research is a novel supervised machine learning algorithm that combines the two. Eyal leads the Jacksonville (FL) Java user group, hosts the Artificial Intelligence for Enterprise virtual user group, and writes the developer-oriented artificial intelligence blog, ai4java.

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Patent Analytics Market to Reach USD 1,668.4 Million by 2027; Integration of Machine Learning and Artificial Intelligence to Spur Business…

Pune, May 18, 2020 (GLOBE NEWSWIRE) -- The global patent analytics market size is predicted to USD 1,668.4 million by 2027, exhibiting a CAGR of 12.4% during the forecast period. The increasing advancement and integration of machine learning, artificial intelligence, and the neural network by enterprises will have a positive impact on the market during the forecast period. Moreover, the growing needs of companies to protect intellectual assets will bolster healthy growth of the market in the forthcoming years, states Fortune Business Insights in a report, titled Patent Analytics Market Size, Share and Industry Analysis, By Component (Solutions and Services), By Services (Patent Landscapes/White Space Analysis, Patent Strategy and Management, Patent Valuation, Patent Support, Patent Analytics, and Others), By Enterprise Size (Large Enterprises, Small & Medium Enterprises), By Industry (IT and Telecommunications, Healthcare, Banking, Financial Services and Insurance (BFSI), Automotive, Media and Entertainment, Food and Beverages and, Others), and Regional Forecast, 2020-2027 the market size stood at USD 657.9 million in 2019. The rapid adoption of the Intellectual Property (IP) system to retain an innovation-based advantage in business will aid the expansion of the market.

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An Overview of the Impact of COVID-19 on this Market:

The emergence of COVID-19 has brought the world to a standstill. We understand that this health crisis has brought an unprecedented impact on businesses across industries. However, this too shall pass. Rising support from governments and several companies can help in the fight against this highly contagious disease. There are some industries that are struggling and some are thriving. Overall, almost every sector is anticipated to be impacted by the pandemic.

We are taking continuous efforts to help your business sustain and grow during COVID-19 pandemics. Based on our experience and expertise, we will offer you an impact analysis of coronavirus outbreak across industries to help you prepare for the future.

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Market Driver:

Integration of Artificial Intelligence to Improve Market Prospects

The implementation of artificial intelligence technology for analyzing patent data will support the expansion of the market. AI-based semantic search uses an artificial neural network to enhance patent discovery by improving accuracy and efficiency. For instance, in February 2018, PatSeer announced the unveiling of ReleSense, an AI-driven NLP engine. The engine utilizes 12 million+ semantic rules to gain from publically available patents, scientific journals, clinical trials, and associated data sources. ReleSense with its wide range of AI-driven capabilities offers search from classification, via APIs and predictive-analytics for apt IP solutions. The growing application of AI for domain-specific analytics will augur well for the market in the forthcoming years. Furthermore, the growing government initiatives to promote patent filing activities will boost the patent analytics market share during the forecast period. For instance, the Government of India introduced a new scheme named Innovative/ Creative India, to aware people of the patents and IP laws and support patent analytics. In addition, the growing preferment for language model and neural network intelligence for accurate, deep, and complete data insights will encourage the market.

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Regional Analysis:

Implementation of Advanced Technologies to Promote Growth in North America

The market in North America stood at USD 209.2 million and is expected to grow rapidly during the forecast period owing to the presence of major companies in the US such as IBM Corporation, Amazon.Com, Inc. The implementation of advanced technologies including IoT, big data, and artificial intelligence by major companies will aid growth in the region.

Considering this the U.S. is expected to showcase a higher growth in the patent filing. As per the World Intellectual Property, in 2018, the U.S. filed 230,085 patent applications across several domains. Asia Pacific is predicted to witness tremendous growth during the forecast period. The growth is attributed to China, which accounts for a major share in the global patent filings. According to WIPO, intellectual property (IP) office in China had accounted for 46.6% global share in patent registration, in 2018. The growing government initiatives concerning patents and IP laws in India will significantly enable speedy growth in Asia Pacific.

Key Development:

March 2018: Ipan GmbH announced its collaboration with Patentsight, Corsearch, and Uppdragshuset for the introduction of an open IP platform named IP-x-change platform. The platform enables prior art search, automatic data verification tools, smart docketing tools integrated in real-time to optimize IP management solution.

List of Key Companies Operating in the Patent Analytics Market are:

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Detailed Table of Content

TOC Continued..!!!

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Intellectual Property Software Market Size, Share and Global Trend By Deployment (On-premises & Cloud-based solutions), By Services (Development & Implementation Services, Consulting Services, Maintenance & Support Services), By Applications (Patent Management, Trademark Management and others), By Industry Vertical (Healthcare, Electronics and others) and Geography Forecast till 2025

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Patent Analytics Market to Reach USD 1,668.4 Million by 2027; Integration of Machine Learning and Artificial Intelligence to Spur Business...

Machine Learning in Finance Market Provides in-depth analysis of the Machine Learning in Finance Industry, with current trends and future estimations…

Market Expertz have recently published a new report on the global Machine Learning in Finance market. The study provides profound insights into updated market events and market trends. This, in turn, helps one in better comprehending the market factors, and strongly they influence the market. Also, the sections related to regions, players, dynamics, and strategies are segmented and sub-segmented to simplify the actual conditions of the industry.

The study is updated with the impacts of the coronavirus and the future analysis of the industrys trends. This is done to ensure that the resultant predictions are most accurate and genuinely calculated. The pandemic has affected all industries, and this report evaluates its impact on the global market.

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The report displays all leading market players profiles functioning in the globalMachine Learning in Financemarket with their SWOT analysis, fiscal status, present development, acquisitions, and mergers. The research report comprises of extensive study about various market segments and regions, emerging trends, major market drivers, challenges, opportunities, obstructions, and growth limiting factors in the market.

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 Machine Learning in Finance manufacturers/companies operating at both regional and global levels:

Ignite LtdYodleeTrill A.I.MindTitanAccentureZestFinance

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.

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Dominant participants of the market analyzed based on:

The competitors are segmented into the size of their individual enterprise, buyers, products, raw material usage, consumer base, etc. Additionally, the raw material chain and the supply chain are described to make the user aware of the prevailing costs in the market. Lastly, their strategies and approaches are elucidated for better comprehension. In short, the market research report classifies the competitive spectrum of this globalMachine Learning in Financeindustry in elaborate detail.

Key highlights of the report:

Market revenue splits by most promising business segments by type, by application, and any other business segment if applicable within the scope of the globalMachine Learning in Financemarket report. The country break-up will help you determine trends and opportunities. The prominent players are examined, and their strategies analyzed.

The Global Machine Learning in Finance Market is segmented:

In market segmentation by types of Machine Learning in Finance, the report covers-

Supervised LearningUnsupervised LearningSemi Supervised LearningReinforced Leaning

In market segmentation by applications of the Machine Learning in Finance, the report covers the following uses-

BanksSecurities CompanyOthers

This Machine Learning in Finance report umbrellas vital elements such as market trends, share, size, and aspects that facilitate the growth of the companies operating in the market to help readers implement profitable strategies to boost the growth of their business. This report also analyses the expansion, market size, key segments, market share, application, key drivers, and restraints.

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Insights into the Machine Learning in Finance market scenario:

Moreover, the report studies the competitive landscape that this industry offers to new entrants. Therefore, it gives a supreme edge to the user over the other competitors in the form of reliable speculations of the market. The key developments in the industry are shown with respect to the current scenario and the approaching advancements. The market report consists of prime information, which could be an efficient read such as investment return analysis, trends analysis, investment feasibility analysis and recommendations for growth.

The data in this report presented is thorough, reliable, and the result of extensive research, both primary and secondary. Moreover, the globalMachine Learning in Financemarket report presents the production, and import and export forecast by type, application, and region from 2020 to 2027.

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Market Expertz 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.

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To summarize, the global Machine Learning in Finance 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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Machine Learning in Finance Market Provides in-depth analysis of the Machine Learning in Finance Industry, with current trends and future estimations...

Tackling climate change with machine learning: Covid-19 and the energy transition – pv magazine International

The effect the coronavirus pandemic is having on energy systems and environmental policy in Europe was discussed at a recent machine learning and climate change workshop, along with the help artificial intelligence can offer to those planning electricity access in Africa.

The impact of Covid-19 on the energy system was discussed in an online climate change workshop that also considered how machine learning can help electricity planning in Africa.

This years International Conference on Learning Representations event included a workshop held by the Climate Change AI group of academics and artificial intelligence industry representatives which considered how machine learning can help tackle climate change.

Bjarne Steffen, senior researcher at the energy politics group at ETH Zrich, shared his insights at the workshop on how Covid-19 and the accompanying economic crisis are affecting recently introduced green policies. The crisis hit at a time when energy policies were experiencing increasing momentum towards climate action, especially in Europe, said Steffen, who added the coronavirus pandemic has cast into doubt the implementation of such progressive policies.

The academic said there was a risk of overreacting to the public health crisis, as far as progress towards climate change goals was concerned.

Lobbying

Many interest groups from carbon-intensive industries are pushing to remove the emissions trading system and other green policies, said Steffen. In cases where those policies are having a serious impact on carbon-emitting industries, governments should offer temporary waivers during this temporary crisis, instead of overhauling the regulatory structure.

However, the ETH Zrich researcher said any temptation to impose environmental conditions to bail-outs for carbon-intensive industries should be resisted. While it is tempting to push a green agenda in the relief packages, tying short-term environmental conditions to bail-outs is impractical, given the uncertainty in how long this crisis will last, he said. It is better to include provisions that will give more control over future decisions to decarbonize industries, such as the government taking equity shares in companies.

Steffen shared with pv magazine readers an article published in Joule which can be accessed here, and which articulates his arguments about how Covid-19 could affect the energy transition.

Covid-19 in the U.K.

The electricity system in the U.K. is also being affected by Covid-19, according to Jack Kelly, founder of London-based, not-for-profit, greenhouse gas emission reduction research laboratory Open Climate Fix.

The crisis has reduced overall electricity use in the U.K., said Kelly. Residential use has increased but this has not offset reductions in commercial and industrial loads.

Steve Wallace, a power system manager at British electricity system operator National Grid ESO recently told U.K. broadcaster the BBC electricity demand has fallen 15-20% across the U.K. The National Grid ESO blog has stated the fall-off makes managing grid functions such as voltage regulation more challenging.

Open Climate Fixs Kelly noted even events such as a nationally-coordinated round of applause for key workers was followed by a dramatic surge in demand, stating:On April 16, the National Grid saw a nearly 1 GW spike in electricity demand over 10 minutes after everyone finished clapping for healthcare workers and went about the rest of their evenings.

Read pv magazines coverage of Covid-19; and tell us how it is affecting your solar and energy storage operations. Email editors@pv-magazine.com to share your experiences.

Climate Change AI workshop panelists also discussed the impact machine learning could have on improving electricity planning in Africa. The Electricity Growth and Use in Developing Economies (e-Guide) initiative funded by fossil fuel philanthropic organization the Rockefeller Foundationaims to use data to improve the planning and operation of electricity systems in developing countries.

E-Guide members Nathan Williams, an assistant professor at the Rochester Institute of Technology (RIT) in New York state, and Simone Fobi, a PhD student at Columbia University in NYC, spoke about their work at the Climate Change AI workshop, which closed on Thursday. Williams emphasized the importance of demand prediction, saying: Uncertainty around current and future electricity consumption leads to inefficient planning. The weak link for energy planning tools is the poor quality of demand data.

Fobi said: We are trying to use machine learning to make use of lower-quality data and still be able to make strong predictions.

The market maturity of individual solar home systems and PV mini-grids in Africa mean more complex electrification plan modeling is required.

Modeling

When we are doing [electricity] access planning, we are trying to figure out where the demand will be and how much demand will exist so we can propose the right technology, added Fobi. This makes demand estimation crucial to efficient planning.

Unlike many traditional modeling approaches, machine learning is scalable and transferable. Rochesters Williams has been using data from nations such as Kenya, which are more advanced in their electrification efforts, to train machine learning models to make predictions to guide electrification efforts in countries which are not as far down the track.

Williams also discussed work being undertaken by e-Guide members at the Colorado School of Mines, which uses nighttime satellite imagery and machine learning to assess the reliability of grid infrastructure in India.

Rural power

Another e-Guide project, led by Jay Taneja at the University of Massachusetts, Amherst and co-funded by the Energy and Economic Growth program by police reform organization Oxford Policy Management uses satellite imagery to identify productive uses of electricity in rural areas by detecting pollution signals from diesel irrigation pumps.

Though good quality data is often not readily available for Africa, Williams added, it does exist.

We have spent years developing trusting relationships with utilities, said the RIT academic. Once our partners realize the value proposition we can offer, they are enthusiastic about sharing their data We cant do machine learning without high-quality data and this requires that organizations can effectively collect, organize, store and work with data. Data can transform the electricity sector but capacity building is crucial.

By Dustin Zubke

This article was amended on 06/05/20 to indicate the Energy and Economic Growth program is administered by Oxford Policy Management, rather than U.S. university Berkeley, as previously stated.

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Tackling climate change with machine learning: Covid-19 and the energy transition - pv magazine International

Global Machine Learning As A Service (Mlaas) Market : Industry Analysis And Forecast (2020-2027) – MR Invasion

Global Machine Learning as a Service (MLaaS) Marketwas valued about US$ XX Bn in 2019 and is expected to grow at a CAGR of 41.7% over the forecast period, to reach US$ 11.3 Bn in 2027.

The report study has analyzed revenue impact of covid-19 pandemic on the sales revenue of market leaders, market followers and disrupters in the report and same is reflected in our analysis.

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Market Definition:

Machine learning as a service (MLaaS) is an array of services that offer ML tools as part of cloud computing services. MLaaS helps clients profit from machine learning without the cognate cost, time and risk of establishing an in-house internal machine learning team.The report study has analyzed revenue impact of covid-19 pandemic on the sales revenue of market leaders, market followers and disrupters in the report and same is reflected in our analysis.

Machine Learning Service Providers:

Global Machine Learning as a Service (MLaaS) Market

Market Dynamics:

The scope of the report includes a detailed study of global and regional markets for Global Machine Learning as a Service (MLaaS) Market with the analysis given with variations in the growth of the industry in each regions. Large and SMEs are focusing on customer experience management to keep a complete and robust relationship with their customers by using customer data. So, ML needs to be integrated into enterprise applications to control and make optimal use of this data. Retail enterprises are shifting their focus to customer buying patterns with the rising number of e-commerce websites and the digital revolution in the retail industry. This drives the need to track and manage the inventory movement of items, which can be done using MLaaS. The use of MLaaS by retail enterprises for inventory optimization and behavioral tracking is expected to have a positive impact on global market growth.Apart from this, the growing trend of digitization is driving the growth of the MLaaS market globally. Growth in adoption of cloud-based platforms is expected to positively impact the growth of the MLaaS market. However, a lack of qualified and skilled persons is believed to be the one of the challenges before the growth of the MLaaS market. Furthermore, increasing concern toward data privacy is anticipated to restrain the development of the global market.

Market Segmentation:

The report will provide an accurate prediction of the contribution of the various segments to the growth of the Machine Learning as a Service (MLaaS) Market size. Based on organization size, SMEs segment is expected to account for the largest XX% market share by 2027. SMEs businesses are also projected to adopt machine learning service. With the help of predictive analytics ML, algorithms not only give real-time data but also predict the future. Machine learning solutions are used by SME businesses for fine-tuning their supply chain by predicting the demand for a product and by suggesting the timing and quantity of supplies vital for satisfying the customers expectations.

Regional Analysis:

The report offers a brief analysis of the major regions in the MLaaS market, namely, Asia-Pacific, Europe, North America, South America, and the Middle East & Africa.North America play an important role in MLaaS market, with a market size of US$ XX Mn in 2019 and will be US$ XX Mn in 2027, with a CAGR of XX% followed by Europe. Most of the machine learning as service market companies are based in the U.S and are contributing significantly in the growth of the market. The Asia-Pacific has been growing with the highest growth rate because of rising investment, favorable government policies and growing awareness. In 2017, Google launched the Google Neural Machine Translation for 9 Indian languages which use ML and artificial neural network to upsurges the fluency as well as accuracy in their Google Translate.

Recent Development:

The MMR research study includes the profiles of leading companies operating in the Global Machine Learning as a Service (MLaas) Market. Companies in the global market are more focused on enhancing their product and service helps through various strategic approaches. The ML providers are competing by launching new product categories, with advanced subscription-based platforms. The companies have adopted the strategy of version up gradations, mergers and acquisitions, agreements, partnerships, and strategic collaborations with regional and global players to achieve high growth in the MLaaS market.

Such as, in April 2019, Microsoft developed a platform that uses machine teaching to help deep strengthening learning algorithms tackle real-world problems. Microsoft scientists and product inventors have pioneered a complementary approach called ML. This relies on people know how to break a problem into easier tasks and give ML models important clues about how to find a solution earlier.

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The objective of the report is to present a comprehensive analysis of the Global Machine Learning as a Service (MLaaS) Market including all the stakeholders of the industry. The past and current status of the industry with forecasted market size and trends are presented in the report with the analysis of complicated data in simple language. The report covers all the aspects of the industry with a dedicated study of key players that includes market leaders, followers and new entrants by region. PORTER, SVOR, PESTEL analysis with the potential impact of micro-economic factors by region on the market has been presented in the report. External as well as internal factors that are supposed to affect the business positively or negatively have been analyzed, which will give a clear futuristic view of the industry to the decision-makers.

The report also helps in understanding Global Machine Learning as a Service (MLaaS) Market dynamics, structure by analyzing the market segments and projects the Global Machine Learning as a Service (MLaaS) Market size. Clear representation of competitive analysis of key players by Application, price, financial position, Product portfolio, growth strategies, and regional presence in the Global Machine Learning as a Service (MLaaS) Market make the report investors guide.Scope of the Global Machine Learning as a Service (MLaaS) Market

Global Machine Learning as a Service (MLaaS) Market, By Component

Software ServicesGlobal Machine Learning as a Service (MLaaS) Market, By Organization Size

Large Enterprises SMEsGlobal Machine Learning as a Service (MLaaS) Market, By End-Use Industry

Aerospace & Defense IT & Telecom Energy & Utilities Public sector Manufacturing BFSI Healthcare Retail OthersGlobal Machine Learning as a Service (MLaaS) Market, By Application

Marketing & Advertising Fraud Detection & Risk Management Predictive analytics Augmented & Virtual reality Natural Language processing Computer vision Security & surveillance OthersGlobal Machine Learning as a Service (MLaaS) Market, By Region

Asia Pacific North America Europe Latin America Middle East AfricaKey players operating in Global Machine Learning as a Service (MLaaS) Market

Ersatz Labs, Inc. BigML Yottamine Analytics Hewlett Packard Amazon Web Services IBM Microsoft Sift Science, Inc. Google AT&T Fuzzy.ai SAS Institute Inc. FICO Predictron Labs Ltd.

MAJOR TOC OF THE REPORT

Chapter One: Machine Learning as a Service Market Overview

Chapter Two: Manufacturers Profiles

Chapter Three: Global Machine Learning as a Service Market Competition, by Players

Chapter Four: Global Machine Learning as a Service Market Size by Regions

Chapter Five: North America Machine Learning as a Service Revenue by Countries

Chapter Six: Europe Machine Learning as a Service Revenue by Countries

Chapter Seven: Asia-Pacific Machine Learning as a Service Revenue by Countries

Chapter Eight: South America Machine Learning as a Service Revenue by Countries

Chapter Nine: Middle East and Africa Revenue Machine Learning as a Service by Countries

Chapter Ten: Global Machine Learning as a Service Market Segment by Type

Chapter Eleven: Global Machine Learning as a Service Market Segment by Application

Chapter Twelve: Global Machine Learning as a Service Market Size Forecast (2019-2026)

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Global Machine Learning As A Service (Mlaas) Market : Industry Analysis And Forecast (2020-2027) - MR Invasion

Yoshua Bengio: Attention is a core ingredient of conscious AI – VentureBeat

During the International Conference on Learning Representations (ICLR) 2020 this week, which as a result of the pandemic took place virtually, Turing Award winner and director of the Montreal Institute for Learning Algorithms Yoshua Bengio provided a glimpse into the future of AI and machine learning techniques. He spoke in February at the AAAI Conference on Artificial Intelligence 2020 in New York alongside fellow Turing Award recipients Geoffrey Hinton and Yann LeCun. But in a lecture published Monday, Bengio expounded upon some of his earlier themes.

One of those was attention in this context, the mechanism by which a person (or algorithm) focuses on a single element or a few elements at a time. Its central both to machine learning model architectures like Googles Transformer and to the bottleneck neuroscientific theory of consciousness, which suggests that people have limited attention resources, so information is distilled down in the brain to only its salient bits. Models with attention have already achieved state-of-the-art results in domains like natural language processing, and they could form the foundation of enterprise AI that assists employees in a range of cognitively demanding tasks.

Bengio described the cognitive systems proposed by Israeli-American psychologist and economist Daniel Kahneman in his seminal book Thinking, Fast and Slow. The first type is unconscious its intuitive and fast, non-linguistic and habitual, and it deals only with implicit types of knowledge. The second is conscious its linguistic and algorithmic, and it incorporates reasoning and planning, as well as explicit forms of knowledge. An interesting property of the conscious system is that it allows the manipulation of semantic concepts that can be recombined in novel situations, which Bengio noted is a desirable property in AI and machine learning algorithms.

Current machine learning approaches have yet to move beyond the unconscious to the fully conscious, but Bengio believes this transition is well within the realm of possibility. He pointed out that neuroscience research has revealed that the semantic variables involved in conscious thought are often causal they involve things like intentions or controllable objects. Its also now understood that a mapping between semantic variables and thoughts exists like the relationship between words and sentences, for example and that concepts can be recombined to form new and unfamiliar concepts.

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Attention is one of the core ingredients in this process, Bengio explained.

Building on this, in a recent paper he and colleagues proposed recurrent independent mechanisms (RIMs), a new model architecture in which multiple groups of cells operate independently, communicating only sparingly through attention. They showed that this leads to specialization among the RIMs, which in turn allows for improved generalization on tasks where some factors of variation differ between training and evaluation.

This allows an agent to adapt faster to changes in a distribution or inference in order to discover reasons why the change happened, said Bengio.

He outlined a few of the outstanding challenges on the road to conscious systems, including identifying ways to teach models to meta-learn (or understand causal relations embodied in data) and tightening the integration between machine learning and reinforcement learning. But hes confident that the interplay between biological and AI research will eventually unlock the key to machines that can reason like humans and even express emotions.

Consciousness has been studied in neuroscience with a lot of progress in the last couple of decades. I think its time for machine learning to consider these advances and incorporate them into machine learning models.

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Yoshua Bengio: Attention is a core ingredient of conscious AI - VentureBeat

Harnessing the power of GaN and machine learning – News – Compound Semiconductor

Military installations, especially on ships and aircraft, require robust power electronics systems to operate radar and other equipment, but there is limited space onboard. Researchers from the University of Houston will use a $2.5 million grant from the US Department of Defense to develop compact electronic power systems to address the issue.

Harish Krishnamoorthy, assistant professor of electrical and computer engineering and principal investigator for the project, said he will focus on developing power converters using GaN (GaN) devices, capable of quickly storing and discharging energy to operate the radar systems.

He is working with co-PI Kaushik Rajashekara, professor of electrical and computer engineering, and Tagore Technology, a semiconductor company based in Arlington Heights, Ill. The work has potential commercial applications, in addition to military use, he said.

Currently, radar systems require large capacitors, which store energy and provide bursts of power to operate the systems. The electrolytic capacitors also have relatively short lifespans, Krishnamoorthy said.

GaN devices can be turned on and off far more quickly - over ten times as quickly as silicon devices. The resulting higher operating frequency allows passive components in the circuit - including capacitors and inductors - to be designed at much smaller dimensions.

But there are still drawbacks to GaN devices. Noise - electromagnetic interference, or EMI - can affect the precision of radar systems, since the devices work at such high speeds. Part of Krishnamoorthy's project involves designing a system where converters can contain the noise, allowing the radar system to operate unimpeded.

He also will use machine learning to predict the lifespan of GaN devices, as well as of circuits employing these devices. The use of GaN technology in power applications is relatively new, and assessing how long they will continue to operate in a circuit remains a challenge.

"We don't know how long these GaN devices will last in practical applications, because they've only been used for a few years," Krishnamoorthy said. "That's a concern for industry."

The health and well-being of AngelTech speakers, partners, employees and the overall community is our top priority. Due to the growing concern around the coronavirus (COVID-19), and in alignment with the best practices laid out by the CDC, WHO and other relevant entities, AngelTech decided to postpone the live Brussels event to 16th - 18th November 2020.

In the interim, we believe it is still important to connect the community and we want to do this via an online summit, taking place live on Tuesday May 19th at 12:00 GMT and content available for 12 months on demand. This will not replace the live event (we believe live face to face interaction, learning and networking can never be fully replaced by a virtual summit), it will supplement the event, add value for key players and bring the community together digitally.

The event will involve 4 breakout sessions for CS International, PIC International, Sensors International and PIC Pilot Lines.

Key elements of the online summit:

Register to attend

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Harnessing the power of GaN and machine learning - News - Compound Semiconductor

Global Machine Learning in Education Market 2020 Study of Growing Trends, Future Scope, New investment, Regional Analysis, Upcoming Business…

The report entitled Machine Learning in Education Market: Global Industry Analysis 2020-2026is a comprehensive research study presenting significant data By Reportspedia.com

Global Machine Learning in Education Market 2020 Industry Research Report offers you market size, industry growth, share, investment plans and strategies, development trends, business idea and forecasts to 2026. The report highlights the exhaustive study of the major market along with present and forecast market scenario with useful business decisions.

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Top Key Manufacturers of Machine Learning in Education industry Report:-

IBMMicrosoftGoogleAmazonCognizanPearsonBridge-UDreamBox LearningFishtreeJellynoteQuantum Adaptive Learning

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Cloud-BasedOn-Premise

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Intelligent Tutoring SystemsVirtual FacilitatorsContent Delivery SystemsInteractive WebsitesOthers

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Global Machine Learning in Education market segmentation, by solution: Biological, Chemical, Mechanical, Global Machine Learning in Education market segmentation, by product: Stress Protection, Scarification, Pest Protection

Machine Learning in Education Market Regional Analysis:-North America(United States, Canada),Europe(Germany, Spain, France, UK, Russia, and Italy),Asia-Pacific(China, Japan, India, Australia, and South Korea),Latin America(Brazil, Mexico, etc.),The Middle East and Africa(GCC and South Africa).

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Report Table of Content Overview Gives Exact Idea About International Machine Learning in Education Market Report:

Chapter 1 describes Machine Learning in Education report important market inspection, product cost structure, and analysis, Machine Learning in Education market size and scope forecast from 2017 to 2026. Although, Machine Learning in Education market gesture, factors affecting the expansion of business also deep study of arise and existing market holders.

Chapter 2 display top manufacturers of Machine Learning in Education market with sales and revenue and market share. Furthermore, report analyses the import and export scenario of industry, demand and supply ratio, labor cost, raw material supply, production cost, marketing sources, and downstream consumers of market.

Chapter 3, 4, 5 analyses Machine Learning in Education report competitive analysis based on product type, their region wise depletion and import/export analysis, the composite annual growth rate of market and foretell study from 2017 to 2026.

Chapter 6 gives an in-depth study of Machine Learning in Education business channels, market sponsors, vendors, dispensers, merchants, market openings and risk.

Chapter 7 gives Machine Learning in Education market Research Discoveries and Conclusion

Chapter 8 gives Machine Learning in Education Appendix

To Analyze Details Of Table Of Content (TOC) of Machine Learning in Education Market Report, Visit Here: https://www.reportspedia.com/report/technology-and-media/global-machine-learning-in-education-market-2019-by-company,-regions,-type-and-application,-forecast-to-2024/30939 #table_of_contents

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Global Machine Learning in Education Market 2020 Study of Growing Trends, Future Scope, New investment, Regional Analysis, Upcoming Business...

Rashed Ali Almansoori emphasizes on how Artificial Intelligence and Machine Learning will turn out to be game-changers – IBG NEWS

Rashed Ali Almansoori emphasizes on how Artificial Intelligence and Machine Learning will turn out to be game-changers

To be in the race, it is important to evolve with time. Technology is booming and the new-age era has seen many changes. In the past, since the world met the internet, things changed and how. From the time of cellphones to smartphones, computers to portable laptops, things have seamlessly changed with social media taking over everyone. Earlier Facebook was considered only for chatting and now it has become a medium to make money by creating content. Besides this, there are many other platforms like YouTube, TikTok, and Instagram to earn in millions. One of the key social media players, Rashed Ali Almansoori is a digital genius with years of experience.

He is a tech blogger who believes to cope up with the latest trends. Being a digital creator, Rashed loves to create meaningful yet informative content about technology. Authenticity is the key to establish your target audience over the web, says the blogger. His other expertise includes web development, web designing, SEO building, and promoting brands over the digital domain. Rashed states that many businesses have taken the digital route considering the popular social media has given in the last decade. The coming decade will see many other innovations out of which Artificial Intelligence will be the main highlight among all.

The digital expert is currently learning the fundamentals of Artificial Intelligence (AI) and Machine Learning (ML). It would not be a surprise if machines perform tasks effectively than humans in the coming time. Upgrading yourself to stay in the game is the only solution, quoted Rashed. By learning the courses, he aims to integrate them into his works. Bringing novelty in his work is what the blogger is doing and it will benefit him in the future. The past year, the 29-year old techie built a strong image of himself on social media and his website is garnering millions of visitors from the Middle East and other countries.

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Rashed Ali Almansoori emphasizes on how Artificial Intelligence and Machine Learning will turn out to be game-changers - IBG NEWS

Machine Learning as a Service Market Benefits, Forthcoming Developments, Business Opportunities & Future Investments to 2027 – 3rd Watch News

Reports published inMarket Research Incfor the Machine Learning as a Service market are spread out over several pages and provide the latest industry data, market future trends, enabling products and end users to drive revenue growth and profitability. Industry reports list and study key competitors and provide strategic industry analysis of key factors affecting market dynamics. This report begins with an overview of the Machine Learning as a Service market and is available throughout development. It provides a comprehensive analysis of all regional and major player segments that provide insight into current market conditions and future market opportunities along with drivers, trend segments, consumer behavior, price factors and market performance and estimates over the forecast period.

Request a pdf copy of this report athttps://www.marketresearchinc.com/request-sample.php?id=16701

Key Strategic Manufacturers

:Microsoft (Washington,US), Amazon Web Services (Washington, US), Hewlett Packard Enterprises (California, US), Google, Inc

The report gives a complete insight of this industry consisting the qualitative and quantitative analysis provided for this market industry along with prime development trends, competitive analysis, and vital factors that are predominant in the Machine Learning as a Service Market.

The report also targets local markets and key players who have adopted important strategies for business development. The data in the report is presented in statistical form to help you understand the mechanics. The Machine Learning as a Service market report gathers thorough information from proven research methodologies and dedicated sources in many industries.

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Key Objectives of Machine Learning as a Service Market Report: Study of the annual revenues and market developments of the major players that supply Machine Learning as a Service Analysis of the demand for Machine Learning as a Service by component Assessment of future trends and growth of architecture in the Machine Learning as a Service market Assessment of the Machine Learning as a Service market with respect to the type of application Study of the market trends in various regions and countries, by component, of the Machine Learning as a Service market Study of contracts and developments related to the Machine Learning as a Service market by key players across different regions Finalization of overall market sizes by triangulating the supply-side data, which includes product developments, supply chain, and annual revenues of companies supplying Machine Learning as a Service across the globe.

Furthermore, the years considered for the study are as follows:

Historical year 2015-2019

Base year 2019

Forecast period 2020 to 2026

Table of Content:

Machine Learning as a Service Market Research ReportChapter 1: Industry OverviewChapter 2: Analysis of Revenue by ClassificationsChapter 3: Analysis of Revenue by Regions and ApplicationsChapter 6: Analysis of Market Revenue Market Status.Chapter 4: Analysis of Industry Key ManufacturersChapter 5: Marketing Trader or Distributor Analysis of Market.Chapter 6: Development Trend of Machine Learning as a Service market

Continue for TOC

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Market Research Inc is farsighted in its view and covers massive ground in global research. Local or global, we keep a close check on both markets. Trends and concurrent assessments sometimes overlap and influence the other. When we say market intelligence, we mean a deep and well-informed insight into your products, market, marketing, competitors, and customers. Market research companies are leading the way in nurturing global thought leadership. We help your product/service become the best they can with our informed approach.

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Machine Learning as a Service Market Benefits, Forthcoming Developments, Business Opportunities & Future Investments to 2027 - 3rd Watch News

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

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

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

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

Impetus StreamAnalytix Launches a Cloud-based Version for Self-service ETL and Machine Learning – EnterpriseTalk

Impetus TechnologiesInc., a leading software products and services company, announced the launch of its newcloud-based version of StreamAnalytixonAWS Marketplace. StreamAnalytix Cloud will also be available on other leading cloud marketplaces like Azure and Google Cloud very soon.

Customer-driven Open Source Technology Platform is Future

Leveraging an interactive data-first approach, the tool provides an intuitive drag-and-drop interface to build ETL flows on the cloud, effortlessly. Users can ingest data from multiple on-premise and cloud sources, enrich this data, and swiftly build applications for a wide range of analytics use cases.

StreamAnalytix Cloud offers a host of power-packed features, including:

We are focused on helping enterprises harness the limitless power of the cloud to build, test, and run ETL and machine learning applications faster across industries and use cases, saidPunit Shah, Director for StreamAnalytix at Impetus Technologies. As a next-generation ETL tool in the cloud,StreamAnalytix Cloud accelerates Spark application development, and empowers users with unmatched scalability and extensibility to meet their strategic business needs.

4 Cyber Security Predictions to Watch Out for in 2020

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Impetus StreamAnalytix Launches a Cloud-based Version for Self-service ETL and Machine Learning - EnterpriseTalk