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Do you have your stay-at-home essentials?Here are some you may have missed.

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Dive Into Data and Machine Learning With 30 Hours of Top-Rated Training for $40 - iMore

Executive Summary

Replacing people with AI may seem tempting, but its also likely a mistake. Amazons hands off the wheel initiative might be a model for how companies can adopt AI to automate repetitive jobs, but keep employees on the payroll by transferring them to more creative roles where they can add more value to the company. Amazons choice to eliminate jobs but retain the workers and move them into new roles allowed the company to be more nimble and find new ways to stay ahead of competitors.

At an automation conference in late 2018, a high-ranking banking official looked up from his buffet plate and stated his objective without hesitation: Im here, he told me, to eliminate full-time employees. I was at the conference becauseafter spending months researching how Amazon automates workat its headquarters,I was eager to learn how other firms thought about this powerful technology. After one short interaction, it was clear that some have it completely wrong.

For the past decade, Amazon has been pushing to automate office work under a program now known as Hands off the Wheel. The purpose was not to eliminate jobs but to automate tasks so that the company could reassign people to build new products to do more with the people on staff, rather than doing the same with fewer people. The strategy appears to have paid off: At a time when its possible to start new businesses faster and cheaper than ever before, Hands off the Wheel has kept Amazon operating nimbly, propelled it ahead of its competitors, and shownthat automating in order to fire can mean missing bigopportunities. As companies look at how to integrate increasingly powerful AI capabilities into their businesses, theyd do well to consider this example.

The animating idea behind Hands off the Wheel originated at Amazons South Lake Union office towers, where the company began automating work in the mid-2010s under an initiative some called Project Yoda. At the time, employees in Amazons retail management division spent their days making deals and working out product promotions as well as determining what items to stock in its warehouses, in what quantities, and for what price. But with two decades worth of retail data at its disposal, Amazons leadership decided to use the force (machine learning) to handle the formulaic processes involved in keeping warehouses stocked. When you have actions that can be predicted over and over again, you dont need people doing them, Neil Ackerman, an ex-Amazon general manager, told me.

The project began in 2012, when Amazon hired Ralf Herbrich as its director of machine learning and made the automation effort one of his launch projects. Getting the software to be goodat inventory management and pricing predictions took years, Herbrich told me, because his team had to account for low-volume product orders that befuddled its data-hungry machine-learning algorithms. By 2015, the teams machine-learning predictions were good enough that Amazons leadership placed them in employees software tools, turning them into a kind of copilot for human workers. But at that point the humans could override the suggestions, and many did, setting back progress.

Eventually, though, automation took hold. It took a few years to slowly roll it out, because there was training to be done, Herbrich said. If the system couldnt make its own decisions, he explained, it couldnt learn. Leadership required employees to automate a large number of tasks, though that varied across divisions. In 2016, my goals for Hands off the Wheel were 80% of all my activity, one ex-employee told me. By 2018 Hands off the Wheel was part of business as usual. Having delivered on his project, Herbrich left the company in 2020.

The transition to Hands off the Wheel wasnt easy. The retail division employees were despondent at first, recognizing that their jobs were transforming. It was a total change, the former employee mentioned above said. Something that you were incentivized to do, now youre being disincentivized to do. Yet in time, many saw the logic. When we heard that ordering was going to be automated by algorithms, on the one hand, its like, OK, whats happening to my job? another former employee, Elaine Kwon, told me. On the other hand, youre also not surprised. Youre like, OK, as a business this makes sense.

Although some companies might have seen an opportunity to reduce head count, Amazon assigned the employees new work. The companys retail division workers largely moved into product and program manager jobs fast-growing roles within Amazon that typically belong to professional inventors. Productmanagers oversee new product development, while program managers oversee groups of projects. People who were doing these mundane repeated tasks are now being freed up to do tasks that are about invention, Jeff Wilke, Amazons departing CEO of Worldwide Consumer, told me. The things that are harder for machines to do.

Had Amazon eliminated those jobs, it would have made its flagship business more profitable but most likely would have caused itself to miss its next new businesses. Instead of automating to milk a single asset, it set out to build new ones. Consider Amazon Go, the companys checkout-free convenience store. Go was founded, in part, by Dilip Kumar, an executive once in charge of the companys pricing and promotions operations. While Kumar spent two years acting as a technical adviser to CEO Jeff Bezos, Amazons machine learning engineers began automating work in his old division, so he took a new lead role in a project aimed at eliminating the most annoying part of shopping in real life: checking out. Kumar helped dream up Go, which is now a pillar of Amazons broader strategy.

If Amazon is any indication, businesses that reassign employees after automating their work will thrive. Those that dont risk falling behind.In shaky economic times, the need for cost-cutting could make it tempting to replace people with machines, but Ill offer a word of warning: Think twice before doing that. Its a message I wish I had shared with the banker.

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How Amazon Automated Work and Put Its People to Better Use - Harvard Business Review

Microchip Technologyannounced it has partnered with Cartesiam, Edge Impulse and Motion Gestures to simplify ML implementation at the edge using the companys ARM Cortex based 32-bit micro-controllers and microprocessors in its MPLAB X Integrated Development Environment (IDE). Bringing the interface to these partners software and solutions into its design environment uniquely positions Microchip to support customers through all phases of their AI/ML projects including data gathering, training the models and inference implementation.

Adoption of our 32-bit MCUs in AI-at-the-edge applications is growing rapidly and now these designs are easy for any embedded system developer to implement, said Fanie Duvenhage, vice president of Microchips human machine interface and touch function group. It is also easy to test these solutions using our ML evaluation kits such as the EV18H79A or EV45Y33A.

About the Partner Offerings

Cartesiam, founded in 2016,is a software publisher specializing in artificial intelligence development tools for microcontrollers. NanoEdge AI Studio, Cartesiams patented development environment, allows embedded developers, without any prior knowledge of AI, to rapidly develop specialized machine learning libraries for microcontrollers. Devices leveraging Cartesiamstechnology are already in production at hundreds ofsites throughout theWorld

Edge Impulse is the end-to-end developer platform for embedded machine learning, enabling enterprises in industrial, enterprise and wearable markets. The platform is free for developers, providing dataset collection, DSP and ML algorithms, testing and highly efficient inference code generation across a wide range of sensor, audio and vision applications. Get started in just minutes thanks to integrated Microchip MPLAB X and evaluation kit support.

Motion Gestures, founded in 2017, provides powerful embedded AI-based gesture recognition software for different sensors, including touch, motion (i.e. IMU) and vision. Unlike conventional solutions, the companys platform does not require any training data collection or programming and uses advanced machine learning algorithms. As a result, gesture software development time and costs are reduced by 10x while gesture recognition accuracy is increased to nearly 100 percent.

See Demonstrations During Embedded Vision Summit

The MPLAB X IDE ML implementations will be featured during the Embedded Vision Summit 2020 virtual conference, September 15-17. Attendees can see video demonstrations at the companys virtual exhibit, which will be staffed each day from 10:30 a.m. to 1 p.m. PDT. The demonstrations are also available here.

Please let us know if you would like to speak to a subject matter expert on Microchips enhanced MPLAB X IDE for ML implementations, or the use of 32-bit microcontrollers in AI-at-the-edge applications. For more information visit microchip.com/ML Customers can get a demo by contacting a Microchip sales representative.

Microchips offering of ML development kits now includes:

For more information, visitwww.microchip.com

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Microchip Partners with Machine-Learning (ML) Software Leaders to Simplify AI-at-the-Edge Design - ELE Times

BOSTON, Sept. 15, 2020 /PRNewswire/ -- Panalgo, a leading healthcare analytics company, today announced the launch of its new Data Sciencemodule for Instant Health Data (IHD), which allows data scientists and researchers to leverage machine-learning to uncover novel insights from the growing volume of healthcare data.

Panalgo's flagship IHD Analytics softwarestreamlines the analytics process by removing complex programming from the equation and allows users to focus on what matters most--turning data into insights. IHD Analytics supports the rapid analysis of a wide range of healthcare data sources, including administrative claims, electronic health records, registry data and more. The software, which is purpose-built for healthcare, includes the most extensive library of customizable algorithms and automates documentation and reporting for transparent, easy collaboration.

Panalgo's new IHD Data Science module is fully integrated with IHD Analytics, and allows for analysis of large, complex healthcare datasets using a wide variety of machine-learning techniques. The IHD Data Science module provides an environment to easily train, validate and test models against multiple datasets.

"Healthcare organizations are increasingly using machine-learning techniques as part of their everyday workflow. Developing datasets and applying machine-learning methods can be quite time-consuming," said Jordan Menzin, Chief Technology Officer of Panalgo. "We created the Data Science module as a way for users to leverage IHD for all of the work necessary to apply the latest machine-learning methods, and to do so using a single system."

"Our new IHD Data Science product release is part of our mission to leverage our deep domain knowledge to build flexible, intuitive software for the healthcare industry," said Joseph Menzin, PhD, Chief Executive Officer of Panalgo. "We are excited to empower our customers to answer their most pressing questions faster, more conveniently, and with higher quality."

The IHD Data Science module provides advanced analytics to better predict patient outcomes, uncover reasons for medication non-adherence, identify diseases earlier, and much more. The results from these analyses can be used by healthcare stakeholders to improve patient care.

Research abstracts using Panalgo's IHD Data Science module are being presented at this week's International Conference on Pharmacoepidemiology and Therapeutic Risk Management, including: "Identifying Comorbidity-based Subtypes of Type 2 Diabetes: An Unsupervised Machine Learning Approach," and "Identifying Predictors of a Composite Cardiovascular Outcome Among Diabetes Patients Using Machine Learning."

About Panalgo Panalgo, formerly BHE, provides software that streamlines healthcare data analytics by removing complex programming from the equation. Our Instant Health Data (IHD) software empowers teams to generate and share trustworthy results faster,enabling more impactful decisions. To learn more, visit us athttps://www.panalgo.com. To request a demo of our IHD software, please contact us at [emailprotected].

SOURCE Panalgo

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Panalgo Brings the Power of Machine-Learning to the Healthcare Industry Via Its Instant Health Data (IHD) Software - PRNewswire

The Global Machine Learning as a Service (MLaaS) Market is anticipated to rise at a considerable rate over the estimated period between 2016 and 2028. The Global Machine Learning as a Service (MLaaS) Market Industry Research Report is an exhaustive study and a detailed examination of the recent scenario of the Global Machine Learning as a Service (MLaaS) industry.

The market study examines the global Machine Learning as a Service (MLaaS) Market by top players/brands, area, type, and the end client. The Machine Learning as a Service (MLaaS) Market analysis likewise examines various factors that are impacting market development and market analysis and discloses insights on key players, market review, most recent patterns, size, and types, with regional analysis and figure.

Click here to get sample of the premium report: https://www.quincemarketinsights.com/request-sample-50032?utm_source= DC/hp

The Machine Learning as a Service (MLaaS) Market analysis offers an outline with an assessment of the market sizes of different segments and countries. The Machine Learning as a Service (MLaaS) Market study is designed to incorporate both quantitative aspects and qualitative analysis of the industry with respect to countries and regions involved in the study. Furthermore, the Machine Learning as a Service (MLaaS) Market analysis also provides thorough information about drivers and restraining factors and the crucial aspects which will enunciate the future growth of the Machine Learning as a Service (MLaaS) Market.

Machine Learning as a Service (MLaaS) Market

The market analysis covers the current global Machine Learning as a Service (MLaaS) Market and outlines the Key players/manufacturers: Microsoft, IBM Corporation, International Business Machine, Amazon Web Services, Google, Bigml, Fico, Hewlett-Packard Enterprise Development, At&T, Fuzzy.ai, Yottamine Analytics, Ersatz Labs, Inc., and Sift Science Inc.

The market study also concentrates on the main leading industry players in the Global Machine Learning as a Service (MLaaS) Market, offering information such as product picture, company profiles, specification, production, capacity, price, revenue, cost, and contact information. This market analysis also focuses on the global Machine Learning as a Service (MLaaS) Market volume, Trend, and value at the regional level, global level, and company level. From a global perspective, this market analysis represents the overall global Machine Learning as a Service (MLaaS) Market Size by analyzing future prospects and historical data.

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On the basis of Market Segmentation, the global Machine Learning as a Service (MLaaS) Market is segmented as By Type (Special Services and Management Services), By Organization Size (SMEs and Large Enterprises), By Application (Marketing & Advertising, Fraud Detection & Risk Analytics, Predictive Maintenance, Augmented Reality, Network Analytics, and Automated Traffic Management), By End User (BFSI, IT & Telecom, Automobile, Healthcare, Defense, Retail, Media & Entertainment, and Communication)

Further, the report provides niche insights for a decision about every possible segment, helping in the strategic decision-making process and market size estimation of the Machine Learning as a Service (MLaaS) market on a regional and global basis. Unique research designed for market size estimation and forecast is used for the identification of major companies operating in the market with related developments. The report has an exhaustive scope to cover all the possible segments, helping every stakeholder in the Machine Learning as a Service (MLaaS) market.

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Machine Learning as a Service (MLaaS) Market Industry Trends, Size, Competitive Analysis and Forecast 2028 - The Daily Chronicle

The amped-up efforts by Facebook and to tone down blatant misinformation on the campaign trail merits public support. Personally, I find myself trying with limited success to tune out the political noise while sensing that the problem goes beyond that.

The rhetoric of politics overall sounds tired and anachronistic, but then, to my ear, so does much of the dialogue on the popular streamers we binge on. Further, check out the virtual learning classes that now pass for education and you run into even lazier forms of communication. We all decided the earth was flat even before the new Netflix documentary, titled Social Dilemma, pointed up the random anti-truths directed our way.

So while misinformation is being challenged by the social media monoliths, my techno-nerd friends remind me that the demise of honest communication demands a more drastic approach. Their solution? Get ready to groan remember, theyre nerds.

Related StoryPeter Bart: Reed Hastings' Memoir Reveals The Hollywood Re-Education Of A Techie

Their solution is to alert us to the expanding tools of neuro-symbiotic AI artificial intelligence. For most of us, AI conjures an old Steven Spielberg movie in which a robotic Haley Joel Osment keeps flunking the tests of his cybertronics instructors. Little wonder the poor kid kept saying I see dead people (oops, different movie).

But a San Francisco-based software company called Open AI last month unveiled a system that could write coherent essays, design software applications and even propose recipes for breakfast burritos that is, if fed the appropriate maze of symbols. Its called deep learning but it could even lead to deep communicating.

Mike Davies, director of Intel Corps neuromorphic computing lab, contends that neuro-symbolic AI can potentially deliver our own voice assistants adjusted to user needs, analyzing problems or even, some day, writing film scripts or political speeches.

These systems are still nascent but you could imagine that as the technology progresses, entirely new fields could emerge in terms of advertising or media, Francesco Marconi, founder of Applied XI, told the Wall Street Journal. His company generates briefs on health and environmental data. They will become effective at assisting people because theyll be able to understand and communicate.

The ultimate aim is to build support for a sort of Manhattan Project, akin to the body that fostered the atom bomb. Spending on this technology could grow to $3.2 billion by 2023, according to IDC, a research firm, that looks for future support coming from health care, banking and retail. Yann LeCun, chief AI scientist at Facebook, insists we are in sight of creating a machine that can learn how the world works by watching video, listening to audio and reading text.

Given the critical results of its own self-audits, Facebook is under growing pressure to police hate speech, with AI-based censors potentially mobilized to crack down on targeted content. Thus extremists who argue that conservationists triggered the fires in Oregon could no longer aim their social media propaganda directly at any user who happens to check on fires or conservation.

But advances must come from sources even more esoteric then AI, some scientists insist. In a new book titled Livewired, David Eagleman, a practicing futurist, argues that the increasingly important field of brain science itself will nurture development of artificial neural networks.

As these networks proliferate, they will be embellished by machines that themselves can learn, and adjust to new surroundings, such as self-driving cars or power grids distributing electricity.

Argues Eagleman, The capacity to grow new neural circuits isnt just a response to trauma its with all of us every day and it forms the basis of all learning.

So heres the epiphany: Given the heightened sophistication of our neuro circuitry, political candidates may actually have to talk honestly to us. And there is nothing more intimidating to a political candidate than an intelligent audience even if its artificially intelligent.

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Peter Bart: Does Social Media Misinformation Anger You? Theres An AI For That Too - Deadline

General Motors Cruise subsidiary faces a monumental task. The company and its engineers are currently trying to develop a fully autonomous robotaxi that will, in the companies words keep us safer on the road, help make our air cleaner, transform our cities, and give us back time. A lot of smart people have tried and failed to develop a fully autonomous vehicle before, so what makes Cruise different?

One way that Cruise hopes to set itself apart from the competition is with its sophisticated machine learning prediction system, as Cruises Senior Engineering Manager, Sean Harris, explained in a recent Medium post. Like most AV companies, Cruise uses machine learning to give its self-driving prototypes the knowledge to read the road ahead and predict what other motorists, cyclists and pedestrians are going to do before it happens, but where Cruises system aims to excel is with regard to so-called long tail events.

While many AVs can predict common maneuvers such as lane changes or sudden stops in traffic, less common actions such as U-turns or a pedestrian stepping in front of the vehicle suddenly (these are what is referred to as a long tail event) are harder to accurately predict. To solve this, Cruise logs miles on its self-driving Chevrolet Bolt EV prototypes and begins to log these infrequent, outlier events. Engineers then use upsampling or interpolation to teach the machine learning prediction system more about these extremely rare driving events.

Another advantage Cruise has is with regard to data labeling. As the machine learning system evaluates a vehicle, pedestrian, or cyclists trajectory on the road, it eventually becomes familiar and a common predicted trajectory. It can then use this memory bank of predicted trajectories and can compare it against a vehicles observed trajectory. This allows it to appropriately label a trajectory, so long as it was previously recorded in its memory. This system, which Cruise calls a self-supervised learning framework, negates the need for manual human data labeling, which is time-consuming, expensive and inaccurate.

One good example of how predicted trajectory can help an AV maneuver in a city is with regard to U-turns. By learning from long tail events, a Cruise AV can almost instantly recognize when the vehicle ahead is in the beginning stages of making a U-turn and will predict that it will turn around and begin heading in the opposite direction. The auto data-labelling system, meanwhile, would automatically see this as a u-turn maneuver and label it as such, allowing the computer memory to quickly refer back to this event when observing other vehicles making u-turns in the future.

Machine learning is obviously quite complicated (Mr. Harris doesnt have a PhD for nothing) so those who want to learn more should check out his Meidum post at this link for a more in-depth explanation of how Cruises self-driving protoypes well, cruise!

The first production-ready vehicle to use this advanced machine learning stack will be the Cruise Origin robotaxi, which will enter production at GMs Detroit-Hamtramck Assembly plant in 2022.

Subscribe to GM Authority for more Cruise news, GM engineering and technology news and around-the-clock GM news coverage.

Sam McEachern

Sam loves to write and has a passion for auto racing, karting and performance driving of all types.

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How Cruise Uses Machine Learning To Predict The Unpredictable - GM Authority

This article is part of ourreviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence.

The Game of Life is a grid-based automaton that is very popular in discussions about science, computation, and artificial intelligence. It is an interesting idea that shows how very simple rules can yield very complicated results.

Despite its simplicity, however, the Game of Life remains a challenge to artificial neural networks, AI researchers at Swarthmore College and the Los Alamos National Laboratory have shown in a recent paper. Titled, Its Hard for Neural Networks To Learn the Game of Life, their research investigates how neural networks explore the Game of Life and why they often miss finding the right solution.

Their findings highlight some of the key issues with deep learning models and give some interesting hints at what could be the next direction of research for the AI community.

British mathematician John Conway invented the Game of Life in 1970. Basically, the Game of Life tracks the on or off statethe lifeof a series of cells on a grid across timesteps. At each timestep, the following simple rules define which cells come to life or stay alive, and which cells die or stay dead:

Based on these four simple rules, you can adjust the initial state of your grid to create interesting stable, oscillating, and gliding patterns.

For instance, this is whats called the glider gun.

You can also use the Game of Life to create very complex pattern, such as this one.

Interestingly, no matter how complex a grid becomes, you can predict the state of each cell in the next timestep with the same rules.

With neural networks being very good prediction machines, the researchers wanted to find out whether deep learning models could learn the underlying rules of the Game of Life.

There are a few reasons the Game of Life is an interesting experiment for neural networks. We already know a solution, Jacob Springer, a computer science student at Swarthmore College and co-author of the paper, told TechTalks. We can write down by hand a neural network that implements the Game of Life, and therefore we can compare the learned solutions to our hand-crafted one. This is not the case in.

It is also very easy to adjust the flexibility of the problem in the Game of Life by modifying the number of timesteps in the future the target deep learning model must predict.

Also, unlike domains such as computer vision or natural language processing, if a neural network has learned the rules of the Game of Life it will reach 100 percent accuracy. Theres no ambiguity. If the network fails even once, then it is has not correctly learned the rules, Springer says.

In their work, the researchers first created a small convolutional neural network and manually tuned its parameters to be able to predict the sequence of changes in the Game of Lifes grid cells. This proved that theres a minimal neural network that can represent the rule of the Game of Life.

Then, they tried to see if the same neural network could reach optimal settings when trained from scratch. They initialized the parameters to random values and trained the neural network on 1 million randomly generated examples of the Game of Life. The only way the neural network could reach 100 percent accuracy would be to converge on the hand-crafted parameter values. This would imply that the AI model had managed to parameterize the rules underlying the Game of Life.

But in most cases the trained neural network did not find the optimal solution, and the performance of the network decreased even further as the number of steps increased. The result of training the neural network was largely affected by the chosen set training examples as well as the initial parameters.

Unfortunately, you never know what the initial weights of the neural network should be. The most common practice is to pick random values from a normal distribution, therefore settling on the right initial weights becomes a game of luck. As for the training dataset, in many cases, it isnt clear which samples are the right ones, and in others, theres not much of a choice.

For many problems, you dont have a lot of choice in dataset; you get the data that you can collect, so if there is a problem with your dataset, you may have trouble training the neural network, Springer says.

In machine learning, one of the popular ways to improve the accuracy of a model that is underperforming is to increase its complexity. And this technique worked with the Game of Life. As the researchers added more layers and parameters to the neural network, the results improved and the training process eventually yielded a solution that reached near-perfect accuracy.

But a larger neural network also means an increase in the cost of training and running the deep learning model.

On the one hand, this shows the flexibility of large neural networks. Although a huge deep learning model might not be the most optimal architecture to address your problem, it has a greater chance of finding a good solution. But on the other, it proves that there is likely to be a smaller deep learning model that can provide the same or better resultsif you can find it.

These findings are in line with The Lottery Ticket Hypothesis, presented at the ICLR 2019 conference by AI researchers at MIT CSAIL. The hypothesis suggested that for each large neural network, there are smaller sub-networks that can converge on a solution if their parameters have been initialized on lucky, winning values, thus the lottery ticket nomenclature.

The lottery ticket hypothesis proposes that when training a convolutional neural network, small lucky subnetworks quickly converge on a solution, the authors of the Game of Life paper write. This suggests that rather than searching extensively through weight-space for an optimal solution, gradient-descent optimization may rely on lucky initializations of weights that happen to position a subnetwork close to a reasonable local minima to which the network converges.

While Conways Game of Life itself is a toy problem and has few direct applications, the results we report here have implications for similar tasks in which a neural network is trained to predict an outcome which requires the network to follow a set of local rules with multiple hidden steps, the AI researchers write in their paper.

These findings can apply to machine learning models used logic or math solvers, weather and fluid dynamics simulations, and logical deduction in language or image processing.

Given the difficulty that we have found for small neural networks to learn the Game of Life, which can be expressed with relatively simple symbolic rules, I would expect that most sophisticated symbol manipulation would be even more difficult for neural networks to learn, and would require even larger neural networks, Springer said. Our result does not necessarily suggest that neural networks cannot learn and execute symbolic rules to make decisions, however, it suggests that these types of systems may be very difficult to learn, especially as the complexity of the problem increases.

The researchers further believe that their findings apply to other fields of machine learning that do not necessarily rely on clear-cut logical rules, such as image and audio classification.

For the moment, we know that, in some cases, increasing the size and complexity of our neural networks can solve the problem of poorly performing deep learning models. But we should also consider the negative impact of using larger neural networks as the go-to method to overcome impasses in machine learning research. One outcome can be greater energy consumption and carbon emissions caused from the compute resources required to train large neural networks. On the other hand, it can result in the collection of larger training datasets instead of relying on finding ideal distribution strategies across smaller datasets, which might not be feasible in domains where data is subject to ethical considerations and privacy laws. And finally, the general trend toward endorsing overcomplete and very large deep learning models can consolidate AI power in large tech companies and make it harder for smaller players to enter the deep learning research space.

We hope that this paper will promote research into the limitations of neural networks so that we can better understand the flaws that necessitate overcomplete networks for learning. We hope that our result will drive development into better learning algorithms that do not face the drawbacks of gradient-based learning, the authors of the paper write.

I think the results certainly motivate research into improved search algorithms, or for methods to improve the efficiency of large networks, Springer said.

Read more here:
Why neural networks struggle with the Game of Life - TechTalks

Despite the pandemic, data scientists remain to be one of the most in-demand jobs. Here we list down 50 latest job openings for data science and analyst positions in cities such as Bangalore, Mumbai, Hyderabad, Pune and more, from last week.

(The jobs are sorted according to the years of experience required).

Location: Hyderabad

Skills Required: Machine learning and statistical models, big data processing technologies such as Hadoop, Hive, Pig and Spark, SQL, etc.

Apply here.

Location: Bangalore

Skills Required: Mathematical modelling using biological datasets, statistical and advanced data analytics preferably using R, Python and/or JMP, hands-on experience in data modelling, data analysis and visualisation, database systems like Postgres, MySQL, SQLServer, etc.

Apply here.

Location: Bangalore

Skills Required: Quantitative analytics or data modelling, predictive modelling, machine learning, clustering and classification techniques, Python, C, C++, Java, SQL, Big Data frameworks and visualisation tools like Cassandra, Hadoop, Spark, Tableau, etc.

Apply here.

Location: Bangalore

Skills Required: Advanced analytics, machine learning, AI techniques, cloud-based Big Data technology, Python, R, SQL, etc.

Apply here.

Location: Thiruvananthapuram, Kerala

Skills Required: Data mining techniques, statistical analysis, building high-quality prediction systems, etc.

Apply here.

Location: Bangalore

Skills Required: Advanced ML, DL, AI, and mathematical modelling and optimisation techniques, Python, NLP, TensorFlow, PyTorch, Keras, etc.

Apply here.

Location: Bangalore

Skills Required: Java, Python, R, C++, machine learning, data mining, mathematical optimisation, simulations, experience in e-commerce or supply chain, computational, programming, data management skills, etc.

Apply here.

Location: Bangalore

Skills Required: Statistics, Machine Learning, programming skills in various languages such as R, Python, etc., NLP, Matlab, linear algebra, optimisation, probability theory, etc.

Apply here.

Location: Bangalore

Skills Required: Knowledge of industry trends, R&D areas and computationally intensive processes (e.g. optimisation), Qiskit, classical approaches to machine learning, etc.

Apply here.

Location: Bangalore

Skills Required: Java, C++, Python, natural language processing systems, C/C++, Java, Perl or Python, statistical language modelling, etc.

Apply here.

Location: Khed, Maharashtra

Skills Required: Statistical computer languages like R, Python, SQL, machine learning techniques, advanced statistical techniques and concepts, etc.

Apply here.

Location: Bangalore

Skills Required: Foundational algorithms in either machine learning, computer vision or deep learning, NLP, Python, etc.

Apply here.

Location: Hyderabad

Skills Required: SQL CQL, MQL, Hive, NoSQL database concepts & applications, data modelling techniques (3NF, Dimensional), Python or R or Java, statistical models and machine learning algorithms, etc.

Apply here.

Location: Anekal, Karnataka

Skills Required: Machine Learning, deep learning-based techniques, OpenCV, DLib, Computer Vision techniques, TensorFlow, Caffe, Pytorch, Keras, MXNet, Theano, etc.

Apply here.

Location: Vadodara, Gujarat

Skills Required: Large and complex data assets, design and build explorative, predictive- or prescriptive models, Python, Spark, SQL, etc.

Apply here.

Location: Remote

Skills Required: Machine Learning & AI, data science Python, R, design and develop training programs, etc.

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Location: Bangalore

Skills Required: Integrating applications and platforms with cloud technologies (i.e. AWS), GPU acceleration (i.e. CUDA and cuDNN), Docker containers, etc.

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Skills Required: ETL developer, SQL or Python developer, Netezza, etc.

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Skills Required: Statistical techniques, statistical analysis tools (e.g., SAS, SPSS, R), statistical analysis tools (e.g., SAS, SPSS, R), etc.

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Skills Required: Probability, statistics, machine learning, data mining, artificial intelligence, big data platforms like Hadoop, spark, hive etc

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Location: Thiruvananthapuram, Kerala

Skills Required: ML and DL approach, advanced Data/Text Mining/NLP/Computer Vision, Python, MLOps concepts, relational (MySQL) and non-relational / document databases (MongoDB/CouchDB), Microsoft Azure/AWS, etc.

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Skills Required: Data structures and algorithms, SQL, regex, HTTP, REST, JSON, XML, Maven, Git, JUnit, IntelliJ IDEA/Eclipse, etc.

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Skills Required: Python, R, GA, Clevertap, Power BI, ML/DL algorithms, SQL, Advanced Excel, etc.

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Skills Required: Data-gathering, pre-processing data, model building, coding languages, including Python and Pyspark, big data technology stack, etc.

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50 Latest Data Science And Analytics Jobs That Opened Last Week - Analytics India Magazine

The Association of Data Scientists (ADaSci) recently announced Deep Learning DEVCON or DLDC 2020, a two-day virtual conference that aims to bring machine learning and deep learning practitioners and experts from the industry on a single platform to share and discuss recent developments in the field.

Scheduled for 29th and 30th October, the conference comes at a time when deep learning, a subset of machine learning, has become one of the most advancing technologies in the world. From being used in the fields of natural language processing to making self-driving cars, it has come a long way. As a matter of fact, reports suggest that by 2024, the deep learning market is expected to grow at a CAGR of 25%. Thus, it can easily be established that the advancements in the field of deep learning have just initiated and got a long road ahead.

Also Read: Top 7 Upcoming Deep Learning Conferences To Watch Out For

Being a crucial subset of artificial intelligence and machine learning, the advancements in deep learning have increased over the last few years. Thus, it has been explored in various industries, starting from healthcare and eCommerce to advertising and finance, by many leading firms as well as startups across the globe.

While companies like Waymo and Google are using deep learning for their self-driving vehicles, Apple is using the technology for its voice assistant Siri. Alongside many are using deep learning automatic text generation, handwriting recognition, relevant caption generation, image colourisation, predicting earthquakes as well as for detecting brain cancers.

In recent news, Microsoft has introduced new advancements in their deep learning optimisation library DeepSpeed to enable next-gen AI capabilities at scale. It can now be used to train language models with one trillion parameters with fewer GPUs.

With that being said, in future, it is expected to see an increased adoption machine translation, customer experience, content creation, image data augmentation, 3D printing and more. A lot of it could be attributed to the significant advancements in hardware space as well as the democratisation of technology, which helped the field in gaining traction.

Also Read: Free Online Resources To Get Hands-On Deep Learning

Many researchers and scientists across the globe have been working with deep learning technology to leverage it in fighting the deadly pandemic COVID-19. In fact, in recent news, some researchers have proposed deep learning-based automated CT image analysis tools that can differentiate COVID patients from the ones which arent infected. In another research, scientists have proposed a fully automatic deep learning system for diagnosing the disease as well as prognostic analysis. Many are also using deep neural networks for analysing X-ray images to diagnose COVID-19 among patients.

Along with these, startups like Zeotap, SilverSparro and Brainalyzed are leveraging the technology to either drive growth in customer intelligence or power industrial automation and AI solutions. With such solutions, these startups are making deep learning technology more accessible to enterprises and individuals.

Also Read: 3 Common Challenges That Deep Learning Faces In Medical Imaging

Companies like Shell, Lenskart, Snaphunt, Baker Hughes, McAfee, Lowes, L&T and Microsoft are looking for data scientists who are equipped with deep learning knowledge. With significant advancements in this field, it has now become the hottest skill that companies are looking for in their data scientists.

Consequently looking at these requirements, many edtech companies have started coming up with free online resources as well as paid certification on deep learning to provide industry-relevant knowledge to enthusiasts and professionals. These courses and accreditation, in turn, bridges the major talent gap that emerging technologies typically face during its maturation.

Also Read: How To Switch Careers To Deep Learning

With such major advancements in the field and its increasing use cases, the area of deep learning has witnessed an upsurge in popularity as well as demand. Thus it is critical, now more than ever, to understand this complex subject in-depth for better research purposes and application. For that matter, one needs to have a thorough understanding of the field to build a career in this ever-evolving field.

And, for this reason, the Deep Learning DEVCON couldnt have come at a better time than this. Not only it will help amateurs as well as professionals to get a better understanding of the field but will also provide them opportunities to network with leading developers and experts of the field.

Further, the talks and the workshops included in the event will provide a hands-on experience for deep learning practitioners on various tools and techniques. Starting with machine learning vs deep learning, followed by feed-forward neural networks and deep neural networks, the workshops will cover topics like GANs, recurrent neural networks, sequence modelling, Autoencoders, and real-time object detection. The two-day workshop will also provide an overview of deep learning as a broad topic, which will further be accredited with a certificate for all the attendees of the workshop.

The workshops will help participants have a strong understanding of deep learning, from basics to advanced, along with in-depth knowledge of artificial neural networks. With that, it will also clear concepts about tuning, regularising and improving the models as well as an understanding of various building blocks with their practical implementations. Alongside, it will also provide practical knowledge of applying deep learning in computer vision and NLP.

Considering the conference is virtual, it will also provide convenience for participants to join the talks and workshops from the comfort of their homes. Thus, a perfect opportunity to get a first-hand experience into the complex world of deep learning along with leading experts and best minds of the field, who will share their relevant experience to encourage enthusiasts and amateurs.

To register for Deep Learning DevCon 2020, visit here.

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Why Deep Learning DevCon Comes At The Right Time - Analytics India Magazine