Page 16«..10..14151617

Archive for the ‘Machine Learning’ Category

Can machine learning take over the role of investors? – TechHQ

Posted: December 31, 2019 at 11:46 pm

without comments

As we dive deeper into the Fourth Industrial Revolution, there is no disputing how technology serves as a catalyst for growth and innovation for many businesses across a range of functions and industries.

But one technology that is steadily gaining prominence across organizations includes machine learning (ML).

In the simplest terms, ML is the science of getting computers to learn and act like humans do without being programmed. It is a form of artificial intelligence (AI) and entails feeding machine data, enabling the computer program to learn autonomously and enhance its accuracy in analyzing data.

The proliferation of technology means AI is now commonplace in our daily lives, with its presence in a panoply of things, such as driverless vehicles, facial recognition devices, and in the customer service industry.

Currently, asset managers are exploring the potential that AI/ML systems can bring to the finance industry; close to 60 percent of managers predict that ML will have a medium-to-large impact across businesses.

MLs ability to analyze large data sets and continuously self-develop through trial and error translates to increased speed and better performance in data analysis for financial firms.

For instance, according to the Harvard Business Review, ML can spot potentially outperforming equities by identifying new patterns in existing data sets and examine the collected responses of CEOs in quarterly earnings calls of the S&P 500 companies for the past 20 years.

Following this, ML can then formulate a review of good and bad stocks, thus providing organizations with valuable insights to drive important business decisions. This data also paves the way for the system to assess the trustworthiness of forecasts from specific company leaders and compare the performance of competitors in the industry.

Besides that, ML also has the capacity to analyze various forms of data, including sound and images. In the past, such formats of information were challenging for computers to analyze, but todays ML algorithms can process images faster and better than humans.


For example, analysts use GPS locations from mobile devices to pattern foot traffic at retail hubs or refer to the point of sale data to trace revenues during major holiday seasons. Hence, data analysts can leverage on this technological advancement to identify trends and new areas for investment.

It is evident that ML is full of potential, but it still has some big shoes to fil if it were to replace the role of an investor.

Nishant Kumar aptly explained this in Bloomberg, Financial data is very noisy, markets are not stationary and powerful tools require deep understanding and talent thats hard to get. One quantitative analyst, or quant, estimates the failure rate in live tests at about 90 percent. Man AHL, a quant unit of Man Group, needed three years of workto gain enough confidence in a machine-learning strategy to devote client money to it. It later extended its use to four of its main money pools.

In other words, human talent and supervision are still essential to developing the right algorithm and in exercising sound investment judgment. After all, the purpose of a machine is to automate repetitive tasks. In this context, ML may seek out correlations of data without understanding their underlying rationale.

One ML expert said, his team spends days evaluating if patterns by ML are sensible, predictive, consistent, and additive. Even if a pattern falls in line with all four criteria, it may not bear much significance in supporting profitable investment decisions.

The bottom line is ML can streamline data analysis steps, but it cannot replace human judgment. Thus, active equity managers should invest in ML systems to remain competitive in this innovate or die era. Financial firms that successfully recruit professionals with the right data skills and sharp investment judgment stands to be at the forefront of the digital economy.

Read the original post:

Can machine learning take over the role of investors? - TechHQ

Written by admin

December 31st, 2019 at 11:46 pm

Posted in Machine Learning

Machine learning to grow innovation as smart personal device market peaks – IT Brief New Zealand

Posted: at 11:46 pm

without comments

Smart personal audio devices are lookingto have the strongest year in history in 2019, with true wireless stereo set to be the largest and fastest growing category, according to new data released by analyst firm Canalys.

New figures released show that in Q3 2019, the worldwide smart personal audio device market grew 53% to reach 96.7 million units. And the segment is expected to break the 100 million unit mark in the final quarter, with potential to exceed 350 million units for the full year.

Canalys latest research showed the TWS category was not only the fastest growing segment in this market, with a stellar 183% annual growth in Q3 2019, but it also overtook wireless earphones and wireless headphones to become the largest category.

The rising importance of streaming content, and the rapid uptake in a new form of social media including short videos, resulted in profound changes in mobile users audio consumption and these changes will accelerate in the next five years while technology advancements like machine learning and smart assistants will bring more radical innovations in areas such as audio content discovery and ambient computing, explainsNicole Peng, vice president of mobility at Canalys.

As users adjust their consumption habits, Peng says the TWS category enabled smartphone vendors to adapt and differentiate against traditional audio players in the market.

With 18.2 million units shipped in Q3 2019, Apple commands 43% of the TWS market share and continues to be the trend setter.

Apple is in clear leadership position and not only on the chipset technology front. The seamless integration with iPhone, unique sizing and noise cancelling features providing top of the class user experience, is where other smartphone vendors such as Samsung, Huawei and Xiaomi are aiming their TWS devices," says Peng.

"In the short-term, smart personal audio devices are seen as the best up-selling opportunities for smartphone vendors, compared with wearables and smart home devices."

Major audio brands such as Bose, Sennheiser, JBL, Sony and others are currently able to stand their ground with their respective audio signatures especially in the earphones and headphones categories, the research shows.

Canalys senior analyst Jason Low says demand for high-fidelity audio will continue to grow. However, the gap between audio players and smartphone vendors is narrowing.

"Smartphone vendors are developing proprietary technologies to not only catch up in audio quality, but also provide better integration for on-the-move user experiences, connectivity and battery life, he explains.

Traditional audio players must not underestimate the importance of the TWS category. The lack of control over any connected smart devices is the audio players biggest weakness," Low says.

"Audio players must come up with an industry standard enabling better integration with smartphones, while allowing developers to tap into the audio features to create new use cases to avoid obsoletion."

Low says the potential for TWS devices is far from being fully uncovered, and vendors must look beyond TWS as just a way to drive revenue growth.

"Coupled with information collected from sensors or provided by smart assistants via smartphones, TWS devices will become smarter and serve broader use cases beyond audio entertainments, such as payment, and health and fitness, he explains.

"Regardless of the form factor, the next challenge will be integrating smarter features and complex services on the smart personal audio platforms. Canalys expects the market of smart personal audio devices to grow exponentially in the next two years and the cake is big enough for many vendors to come in and compete for the top spots as technology leaders and volume leaders.

AWS leads cloud race, but Microsoft & Google grow faster

Vehicle connectivity market to surpass US$1b by 2022

Spark lifts earnings on the back of mobile, wireless, cloudservices

Bose revamps iconic QuietComfort headphones

Top four consolidate leadership in cloud services market

Spark recalls power back-up kit for wireless landline phones

Read this article:

Machine learning to grow innovation as smart personal device market peaks - IT Brief New Zealand

Written by admin

December 31st, 2019 at 11:46 pm

Posted in Machine Learning

This AI Agent Uses Reinforcement Learning To Self-Drive In A Video Game – Analytics India Magazine

Posted: at 11:46 pm

without comments

One of the most used machine learning (ML) algorithms of this year, reinforcement learning (RL) has been utilised to solve complex decision-making problems. In the present scenario, most of the researches are focussed on using RL algorithms which helps in improving the performance of the AI model in some controlled environment.

Ubisofts prototyping space, Ubisoft La Forge has been doing a lot of advancements in its AI space. The goal of this prototyping space is to bridge the gap between the theoretical academic work and the practical applications of AI in videogames as well as in the real world. In one of our articles, we discussed how Ubisoft is mainstreaming machine learning into game development. Recently, researchers from the La Forge project at Ubisoft Montreal proposed a hybrid AI algorithm known as Hybrid SAC, which is able to handle actions in a video game.

Most reinforcement learning research papers focus on environments where the agents actions are either discrete or continuous. However, when training an agent to play a video game, it is common to encounter situations where actions have both discrete and continuous components. For instance, when wanting the agent to control systems that have both discrete and continuous components, like driving a car by combining steering and acceleration (both continuous) with the usage of the hand brake (a discrete binary action).

This is where Hybrid SAC comes into play. Through this model, the researchers tried to sort out the common challenges in video game development techniques. The contribution consists of a different set of constraints which is mainly geared towards industry practitioners.

The approach in this research is based on Soft Actor-Critic which is designed for continuous action problems. Soft Actor-Critic (SAC) is a model-free algorithm which was originally proposed for continuous control tasks, however, the actions which are mostly encountered in video games are both continuous as well as discrete.

In order to deal with a mix of discrete and continuous action components, the researchers converted part of SACs continuous output into discrete actions. Thus the researchers further explored this approach and extended it to a hybrid form with both continuous and discrete actions. The researchers also introduced Hybrid SAC which is an extension to the SAC algorithm that can handle discrete, continuous and mixed actions discrete-continuous.

The researchers trained a vehicle in a Ubisoft game by using the proposed Hybrid SAC model with two continuous actions (acceleration and steering) and one binary discrete action (hand brake). The objective of the car is to follow a given path as fast as possible, and in this case, the discrete hand brake action plays a key role in staying on the road at such a high speed.

Hybrid SAC exhibits competitive performance with the state-of-the-art on parameterised actions benchmarks. The researchers showed that this hybrid model can be successfully applied to train a car on a high-speed driving task in a commercial video game, also, demonstrating the practical usefulness of such an algorithm for the video game industry.

While working with the mixed discrete-continuous actions, the researchers have gained several experiences and shared them as a piece of advice to obtain an appropriate representation for a given task.They are mentioned below


A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box. Contact:

See the rest here:

This AI Agent Uses Reinforcement Learning To Self-Drive In A Video Game - Analytics India Magazine

Written by admin

December 31st, 2019 at 11:46 pm

Posted in Machine Learning

10 Machine Learning Techniques and their Definitions – AiThority

Posted: December 9, 2019 at 7:52 pm

without comments

When one technology replaces another, its not easy to accurately ascertain how the new technology would impact our lives. With so much buzz around the modern applications of Artificial Intelligence, Machine Learning, and Data Science, it becomes difficult to track the developments of these technologies. Machine Learning, in particular, has undergone a remarkable evolution in recent years. Many Machine Learning (ML) techniques have come in the foreground recently, most of which go beyond the traditionally simple classifications of this highly scientific Data Science specialization.

Read More: Beyond RPA And Cognitive Document Automation: Intelligent Automation At Scale

Lets point out the top ML techniques that the industry leaders and investors are keenly following, their definition, and commercial application.

Perceptual Learning is the scientific technique of enabling AI ML algorithms with better perception abilities to categorize and differentiate spatial and temporal patterns in the physical world.

For humans, Perceptual Learning is mostly instinctive and condition-driven. It means humans learn perceptual skills without actual awareness. In the case of machines, these learning skills are mapped implicitly using sensors, mechanoreceptors, and connected intelligent machines.

Most AI ML engineering companies boast of developing and delivering AI ML models that run on an automated platform. They openly challenge the presence and need for a Data Scientist in the Engineering process.

Automated Machine Learning (AutoML) is defined as the fully automating the entire process of Machine Learning model development right up till the process of its application.

AutoML enables companies to leverage AI ML models in an automated environment without truly seeking the involvement and supervision of Data Scientists, AI Engineers or Analysts.

Google, Baidu, IBM, Amazon, H2O, and a bunch of other technology-innovation companies already offer a host of AutoML environment for many commercial applications. These applications have swept into every possible business in every industry, including in Healthcare, Manufacturing, FinTech, Marketing and Sales, Retail, Sports and more.

Bayesian Machine Learning is a unique specialization within AI ML projects that leverage statistical models along with Data Science techniques. Any ML technique that uses the Bayes Theorem and Bayesian statistical modeling approach in Machine Learning fall under the purview of Bayesian Machine Learning.

The contemporary applications of Bayesian ML involves the use of open-source coding platform Python. Unique applications include

A good ML program would be expected to perpetually learn to perform a set of complex tasks. This learning mechanism is understood from the specialized branch of AI ML techniques, called Meta-Learning.

The industry-wide definition for Meta-Learning is the ability to learn and generalize AI into different real-world scenarios encountered during the ML training time, using specific volume and variety of data.

Meta-Learning techniques can be further differentiated into three categories

In each of these categories, there is a unique learner, meta-learner, and vectors with labels that match Data-Time-Spatial vectors into a set of networking processes to weigh real-world scenarios labeled with context and inferences.

All the recent Image Processing and Voice Search techniques use the Meta-Learning techniques for their outcomes.

Adversarial ML is one of the fastest-growing and most sophisticated of all ML techniques. It is defined as the ML technique adopted to test and validate the effectiveness of any Machine Learning program in an adverse situation.

As the name suggests, its the antagonistic principle of genuine AI, but used nonetheless to test the veracity of any ML technique when it encounters a unique, adverse situation. It is mostly used to fool an ML model into doubting its own results, thereby leading to a malfunction.

Most ML models are capable of generating answer for one single parameter. But, can it be used to answer for x (unknown or variable) parameter. Thats where the Causal Inference ML techniques comes into play.

Most AI ML courses online are teaching Causal inference as a core ML modeling technique. Causal inference ML technique is defined as the causal reasoning process to draw a unique conclusion based on the impact variables and conditions have on the outcome. This technique is further categorized into Observational ML and Interventional ML, depending on what is driving the Causal Inference algorithm.

Also commercially popularized as Explainable AI (X AI), this technique involves the use of neural networking and interpretation models to make ML structures more easily understood by humans.

Deep Learning Interpretability is defined as the ML specialization to remove black boxes in AI models, providing decision-makers and data officers to understand data modeling structures and legally permit the use of AI ML for general purposes.

The ML technique may use one or more of these techniques for Deep Learning Interpretation.

Any data can be accurately plotted using graphs. In Machine Learning techniques, a graph is a data structure consisting of two components, Vertices (or nodes) and Edges.

Graph ML networks is a specialized ML technique used to connect problems with edges and graphs. Graph Neural Networks (NNs) give rise to the category of Connected NNs (CNSS) and AI NNs (ANN).

There are at least 50 more ML techniques that could be learned and deployed using various NN models and systems. Click here to know of the leading ML companies that are constantly transforming Data Science applications with AI ML techniques.

(To share your insights about ML techniques and commercial applications, please write to us at

Read more from the original source:

10 Machine Learning Techniques and their Definitions - AiThority

Written by admin

December 9th, 2019 at 7:52 pm

Posted in Machine Learning

Managing Big Data in Real-Time with AI and Machine Learning – Database Trends and Applications

Posted: at 7:52 pm

without comments

Dec 9, 2019

Processing big data in real-time for artificial intelligence, machine learning, and the Internet of Things poses significant infrastructure challenges.

Whether it is for autonomous vehicles, connected devices, or scientific research, legacy NoSQL solutions often struggle at hyperscale. Theyve been built on top of existing RDBMs and tend to strain when looking to analyze and act upon data at hyperscale - petabytes and beyond.

DBTA recently held a webinar featuring Theresa Melvin, chief architect of AI-driven big data solutions, HPE, and Noel Yuhanna, principal analyst serving enterprise architecture professionals, Forrester, who discussed trends in what enterprises are doing to manage big data in real-time.

Data is the new currency and it is driving todays business strategy to fuel innovation and growth, Yuhanna said.

According to a Forrester survey, the top data challenges are data governance, data silos, and data growth, he explained.

More than 35% of enterprises have failed to get value from big data projects largely because of skills, budget, complexity and strategy. Most organizations are dealing with growing multi-format data volume thats in multiple repositories -relational, NoSQL, Hadoop, data lake..

The need has grown for real-time and agile data requirements, he explained. There are too many data silos multiple repositories, cloud sources.

There is a lack of visibility into data across personas -- developer, data scientist, data engineers, data architects, security etc..Traditional data platforms have failed to support new business requirements such as data warehouse, relational DBMS, and ETL tools.

Its all about the customer and its critical for organizations to have a platform to succeed, Yuhanna said. Customers prefer personalization. Companies are still early on their AI journey but they believe it will improve efficiency and effectiveness.

AI and machine learning can hyper-personalize customer experience with targeted offers, he explained. It can also prevent line shutdowns by predicting machine failures.

AI is not one technology. It is comprised of one or more building block technologies. According to the Forrester survey, Yuhanna said AI/ML for data will help end-users and customers to support data intelligence to support new next-generation use cases such as customer personalization, fraud detection, advanced IoT analytics and rea-time data sharing and collaboration.

AI/ML as a platform feature will help support automation within the BI platform for data integration, data quality, security, governance, transformation, etc., minimizing human effort required. This helps deliver insights quicker in hours instead of days and months.

Melvin suggested using HPE Persistent Memory. The platform offers real-time analysis, real-time persist, a single source of truth, and a persistent record.

An archived on-demand replay of this webinar is available here.

See the article here:

Managing Big Data in Real-Time with AI and Machine Learning - Database Trends and Applications

Written by admin

December 9th, 2019 at 7:52 pm

Posted in Machine Learning

The NFL And Amazon Want To Transform Player Health Through Machine Learning – Forbes

Posted: at 7:52 pm

without comments

The NFL and Amazon announced an expansion of their partnership at their annual AWS re:Invent ... [+] conference in Las Vegas that will use artificial intelligence and machine learning to combat player injuries. (Photo by Michael Zagaris/San Francisco 49ers/Getty Images)

Injury prevention in sports is one of the most important issues facing a number of leagues. This is particularly true in the NFL, due to the brutal nature of that punishing sport, which leaves many players sidelined at some point during the season. A number of startups are utilizing technology to address football injury issues, specifically limiting the incidence of concussions. Now, one of the largest companies in the world is working with the league in these efforts.

A week after partnering with the Seattle Seahawks on its machine learning/artificial intelligence offerings, Amazon announced a partnership Thursday in which the technology giant will use those same tools to combat football injuries. Amazon has been involved with the league, with its Next Gen Stats partnership, and now the two companies will work to advance player health and safety as the sport moves forward after its 100th season this year. Amazons AWS cloud services will use its software to gather and analyze large volumes of player health data and scan video images with the objective of helping teams treat injuries and rehabilitate players more effectively. The larger goal will be to create a new Digital Athlete platform to anticipate injury before it even takes place.

This partnership expands the quickly growing relationship between the NFL and Amazon/AWS. as the two have already teamed up for two years with the leagues Thursday Night Football games streamed on the companys Amazon Prime Video platform. Amazon paid $130 million for rights that run through next season. The league also uses AWSs ML Solutions Lab,as well as Amazons SageMaker platform, that enables data scientists and developers to build and develop machine learning models that can also lead to the leagues ultimate goal of predicting and limiting player injury.

The NFL is committed to re-imagining the future of football, said NFL Commissioner Roger Goodell. When we apply next-generation technology to advance player health and safety, everyone wins from players to clubs to fans. The outcomes of our collaboration with AWS and what we will learn about the human body and how injuries happen could reach far beyond football. As we look ahead to our next 100 seasons, were proud to partner with AWS in that endeavor.

The new initiative was announced as part of Amazons AWS re:Invent conference in Las Vegas on Thursday. Among the technologies that AWS and the league announced in its Digital Athlete platform is a computer-simulated model of an NFL player that will model infinite scenarios within NFL gameplay in order to identify a game environment that limits the risk to a player. Digital Athlete uses Amazons full arsenal of technologies, including the AI, ML and computer vision technology that is used with Amazons Rekognition tool and that uses enormous data sets encompassing historical and more modern video to identify a wide variety of solutions, including the prediction of player injury.

By leveraging the breadth and depth of AWS services, the NFL is growing its leadership position in driving innovation and improvements in health and player safety, which is good news not only for NFL players but also for athletes everywhere, said Andy Jassy, CEO of AWS. This partnership represents an opportunity for the NFL and AWS to develop new approaches and advanced tools to prevent injury, both in and potentially beyond football.

These announcements come at a time when more NFL players are utilizing their large platforms to bring awareness to injuries and the enormous impact those injuries have on their bodies. Former New England Patriots tight end Rob Gronkowski has been one of the most productive NFL players at his position in league history but he had to retire from the league this year, at the age of 29, due to a rash of injuries.

The future Hall of Fame player estimated that he suffered probably 20 concussions in his football career. These admissions have significant consequences on youth participation rates in the sport. Partnerships like the one announced yesterday will need to be successful in order for the sport to remain on solid footing heading into the new decade.

See original here:

The NFL And Amazon Want To Transform Player Health Through Machine Learning - Forbes

Written by admin

December 9th, 2019 at 7:52 pm

Posted in Machine Learning

Machine Learning Answers: If Nvidia Stock Drops 10% A Week, Whats The Chance Itll Recoup Its Losses In A Month? – Forbes

Posted: at 7:51 pm

without comments

Jen-Hsun Huang, president and chief executive officer of Nvidia Corp., gestures as he speaks during ... [+] the company's event at the 2019 Consumer Electronics Show (CES) in Las Vegas, Nevada, U.S., on Sunday, Jan. 6, 2019. CES showcases more than 4,500 exhibiting companies, including manufacturers, developers and suppliers of consumer technology hardware, content, technology delivery systems and more. Photographer: David Paul Morris/Bloomberg

We found that if Nvidia Stock drops 10% or more in a week (5 trading days), there is a solid 36% chance itll recover 10% or more, over the next month (about 20 trading days)

Nvidia stock has seen significant volatility this year. While the company has been impacted by the broader correction in the semiconductor space and the trade war between the U.S. and China, the stock is being supported by a strong long-term outlook for GPU demand amid growing applications in Deep Learning and Artificial Intelligence.

Considering the recent price swings, we started with a simple question that investors could be asking about Nvidia stock: given a certain drop or rise, say a 10% drop in a week, what should we expect for the next week? Is it very likely that the stock will recover the next week? What about the next month or a quarter? You can test a variety of scenarios on the Trefis Machine Learning Engine to calculate if Nvidia stock dropped, whats the chance itll rise.

For example, after a 5% drop over a week (5 trading days), the Trefis machine learning engine says chances of an additional 5% drop over the next month, are about 40%. Quite significant, and helpful to know for someone trying to recover from a loss. Knowing what to expect for almost any scenario is powerful. It can help you avoid rash moves. Given the recent volatility in the market, the mix of macroeconomic events (including the trade war with China and interest rate easing by the U.S. Fed), we think investors can prepare better.

Below, we also discuss a few scenarios and answer common investor questions:

Question 1: Does a rise in Nvidia stock become more likely after a drop?


Not really.

Specifically, chances of a 5% rise in Nvidia stock over the next month:

= 40%% after Nvidia stock drops by 5% in a week.


= 44.5% after Nvidia stock rises by 5% in a week.

Question 2: What about the other way around, does a drop in Nvidia stock become more likely after a rise?



Specifically, chances of a 5% decline in Nvidia stock over the next month:

= 40% after NVIDIA stock drops by 5% in a week


= 27% after NVIDIA stock rises by 5% in a week

Question 3: Does patience pay?


According to data and Trefis machine learning engines calculations, largely yes!

Given a drop of 5% in Nvidia stock over a week (5 trading days), while there is only about 28% chance the Nvidia stock will gain 5% over the subsequent week, there is more than 58% chance this will happen in 6 months.

The table below shows the trend:


Question 4: What about the possibility of a drop after a rise if you wait for a while?


After seeing a rise of 5% over 5 days, the chances of a 5% drop in Nvidia stock are about 30% over the subsequent quarter of waiting (60 trading days). However, this chance drops slightly to about 29% when the waiting period is a year (250 trading days).

Whats behind Trefis? See How Its Powering New Collaboration and What-Ifs ForCFOs and Finance Teams|Product, R&D, and Marketing Teams More Trefis Data Like our charts? Exploreexample interactive dashboardsand create your own

Follow this link:

Machine Learning Answers: If Nvidia Stock Drops 10% A Week, Whats The Chance Itll Recoup Its Losses In A Month? - Forbes

Written by admin

December 9th, 2019 at 7:51 pm

Posted in Machine Learning

NFL Looks to Cloud and Machine Learning to Improve Player Safety – Which-50

Posted: at 7:51 pm

without comments

Americas National Football league is turning to emerging technology to try to solve its ongoing challenges around player safety. The sports governing body says it has amassed huge amounts of data but wants to apply machine learning to gain better insights and predictive capabilities.

It is hoped the insights will inform new rules, safer equipment, and better injury rehabilitation methods. However, the data will not be available to independent researchers.

Last week the NFL announced a partnership with Amazon Web Services to provide the digital services including machine learning and digital twin applications. Terms of the deal were not disclosed.

As the NFL has reached hyper professionalisation, data suggests player injuries have worsened, particularly head injuries sustained through high impact collisions. Several retired players have been diagnosed with or report symptoms of chronic traumatic encephalopathy, a neurodegenerative disease which can only be fully diagnosed post mortem.

As scrutiny has grown the NFL has responded with several rule changes and redesigning player helmets, both initiatives which it says has reduced concussions. However the league was also accused of failing to notify players of the links between concussions and brain injuries.

All of our initiatives on the health and safety side started with the engineering roadmap around minimising head impact on field, NFL executive vice president, Jeff Miller told Which-50 following the announcement.

Miller who is responsible for player health and safety, said the new technology is a new opportunity to minimise risk to players.

I think the speed, the pace of the insights that are available as a result of this [technology] are going to continue towards that same goal, hopefully in a much more efficient, and in fact mature, faster supersized scale.

Miller said the NFL has a responsibility to pass on the insights to lower levels of the game like high school and youth leagues. However, the data will not be available to external researchers initially.

As we find those insights I think were going to be able to share those, were going to be able to share those within the sport and hopefully over time outside of the sport as well.

NFL commissioner Roger Goodell announced the AWS deal, which builds on an existing partnership for game statistics, alongside Andy Jassy, the public cloud providers CEO, during the AWS:re:invent conference in Las Vegas last week.

Goodell said the NFL had amassed huge amounts of data from sensors and video feeds but needed the AWS tools to better leverage it.

When you take the combination of that the possibilities are enormous, the NFL boss said. We want to use the data to change the game. There are very few relationships we get involved with where the partner and the NFL can change the game.

When we apply next-generation technology to advance player health and safety, everyone wins from players to clubs to fans.

AWS machine learning tools will be applied to the data to help build a digital athlete, a type of digital twin which can be used to simulate certain scenarios including impacts.

The outcomes of our collaboration with AWS and what we will learn about the human body and how injuries happen could reach far beyond football, he said.

The author traveled to AWS re:Invent as a guest of Amazon.

Previous post

Next post

See more here:

NFL Looks to Cloud and Machine Learning to Improve Player Safety - Which-50

Written by admin

December 9th, 2019 at 7:51 pm

Posted in Machine Learning

Amazon Wants to Teach You Machine Learning Through Music? – Dice Insights

Posted: at 7:51 pm

without comments

Machine learning has rapidly become one of those buzzwords embraced by companies around the world. Even if they dont fully understand what it means, executives think that machine learning will magically transform their operations and generate massive profits. Thats good news for technologistsprovided they actually learn the technologys fundamentals, of course.

Amazon wants to help with the learning aspect of things. At this years AWS re:Invent, the company is previewing the DeepComposer, a 32-key keyboard thats designed to train you in machine learning fundamentals via the power of music.

No, seriously. AWS DeepComposer is the worlds first musical keyboard powered by machine learning to enable developers of all skill levels to learn Generative AI while creating original music outputs, reads Amazons ultra-helpful FAQ on the matter. DeepComposer consists of a USB keyboard that connects to the developers computer, and the DeepComposer service, accessed through the AWS Management Console.There are tutorials and training data included in the package.

Generative AI, the FAQ continues, allows computers to learn the underlying pattern of a given problem and use this knowledge to generate new content from input (such as image, music, and text). In other words, youre going to play a really simple song like Chopsticks, and this machine-learning platform will use that seed to build a four-hour Wagner-style opera. Just kidding! Or are we?

Jokes aside, the idea that a machine-learning platform can generate lots of data based on relatively little input is a powerful one. Of course, Amazon isnt totally altruistic in this endeavor; by serving as a training channel for up-and-coming technologists, the company obviously hopes that more people will turn to it for all of their machine learning and A.I. needs in future years. Those interested can sign up for the preview on a dedicated site.

This isnt the first time that Amazon has plunged into machine-learning training, either. Late last year, it introduced AWS DeepRacer, a model racecar designed to teach developers the principles of reinforcement learning. And in 2017, it rolled out AWS DeepLens camera, meant to introduce the technology world to Amazons take on computer vision and deep learning.

Membership has its benefits. Sign up for a free Dice profile, add your resume, discover great career insights and set your tech career in motion. Register now

For those who master the fundamentals of machine learning, the jobs can prove quite lucrative. In September, theIEEE-USA Salary & Benefits Salarysuggested that engineers with machine-learning knowledge make an annual average of $185,000. Earlier this year, meanwhile, Indeed pegged theaverage machine learning engineer salary at $146,085, and its job growth between 2015 and 2018 at 344 percent.

If youre not interested in Amazons version of a machine-learning education, there are other channels. For example, OpenAI, the sorta-nonprofit foundation (yes, its as odd as it sounds), hosts what it calls Gym, a toolkit for developing and comparing reinforcement algorithms; it also has a set of models and tools, along with a very extensive tutorialin deep reinforcement learning.

Google likewise has acrash course,complete with 25 lessons and 40+ exercises, thats a good introduction to machine learning concepts. Then theres Hacker Noon and its interesting breakdown of machine learning andartificial intelligence.

Once you have a firmer grasp on the core concepts, you can turn to Bloombergs Foundations of Machine Learning,a free online coursethat teaches advanced concepts such as optimization and kernel methods. A lot of math is involved.

Whatever learning route you take, its clear that machine learning skills have an incredible value right now. Familiarizing yourself through this technologywhether via traditional lessons or a musical keyboardcan only help your career in tech.

See more here:

Amazon Wants to Teach You Machine Learning Through Music? - Dice Insights

Written by admin

December 9th, 2019 at 7:51 pm

Posted in Machine Learning

Measuring Employee Engagement with A.I. and Machine Learning – Dice Insights

Posted: at 7:51 pm

without comments

A small number of companies have begun developing new tools to measure employee engagement without requiring workers to fill out surveys or sit through focus groups. HR professionals and engagement experts are watching to see if these tools gain traction and lead to more effective cultural and retention strategies.

Two of these companiesNetherlands-based KeenCorp and San Franciscos Cultivateglean data from day-to-day internal communications. KeenCorp analyzes patterns in an organizations (anonymized) email traffic to gauge changes in the level of tension experienced by a team, department or entire organization. Meanwhile, Cultivate analyzes manager email (and other digital communications) to provide leadership coaching.

These companies are likely to pitch to a ready audience of employers, especially in the technology space. With IT unemployment hovering around 2 percent, corporate and HR leaders cant help but be nervous about hiring and retention. When competition for talent is fierce, companies are likely to add more and more sweeteners to each offer until they reel in the candidates they want. Then theres the matter of retaining those employees in the face of equally sweet counteroffers.

Thats why businesses utilize a lot of effort and money on keeping their workers engaged. Companies spend more than $720 million annually on engagement, according to the Harvard Business Review. Yet their efforts have managed to engage just 13 percent of the workforce.

Given the competitive advantage tech organizations enjoy when their teams are happy and productivenot to mention the money they save by keeping employees in placeengagement and retention are critical. But HR cant create and maintain an engagement strategy if it doesnt know the workforces mindset. So companies have to measure, and they measure primarily through surveys.

Today, many experts believe surveys dont provide the information employers need to understand their workforces attitudes. Traditional surveys have their place, they say, but more effective methods are needed. They see the answer, of course, in artificial intelligence (A.I.) and machine learning (ML).

One issue with surveys is they only capture a part of the information, and thats the part that the employee is willing to release, said KeenCorp co-founder Viktor Mirovic. When surveyed, respondents often hold back information, he explained, leaving unsaid data that has an effect similar to unheard data.

I could try to raise an issue that you may not be open to because you have a prejudice, Mirovic added. If tools dont account for whats left unsaid and unheard, he argued, they provide an incomplete picture.

As an analogy, Mirovic described studies of combat aircraft damaged in World War II. By identifying where the most harm occurred, designers thought they could build safer planes. However, the study relied on the wrong data, Mirovic said. Why? Because they only looked at the planes that came back. The aircraft that presumably suffered the most grievous damagethose that were shot downwerent included in the research.

None of this means traditional surveys surveys dont provide value. I think the traditional methods are still useful, said Alex Kracov, head of marketing for Lattice, a San Francisco-based workforce management platform that focuses on small and mid-market employers. Sometimes just the idea of starting to track engagement in the first place, just to get a baseline, is really useful and can be powerful.

For example, Lattice itself recently surveyed its 60 employees for the first time. It was really interesting to see all of the data available and how people were feeling about specific themes and questions, he said. Similarly, Kracov believes that newer methods such as pulse surveyswhich are brief studies conducted at regular intervalscan prove useful in monitoring employee satisfaction, productivity and overall attitude.

Whereas surveys require an employees active participation, the up-and-coming tools dont ask them to do anything more than their work. When KeenCorps technology analyzes a companys email traffic, its looking for changes in the patterns of word use and compositional style. Fluctuations in the products index signify changes in collective levels of tension. When a change is flagged, HR can investigate to determine why attitudes are in flux and then proceed accordingly, either solving a problem or learning a lesson.

When I ask you a question, you have to think about the answer, Mirovic said. Once you think about the answer, you start to include all kinds of other attributes. You know, youre my boss or youve just given me a raise or youre married to my sister. Those could all affect my response. What we try to do is go in as objectively as possible, without disturbing people as we observe them in their natural habitats.

See the original post:

Measuring Employee Engagement with A.I. and Machine Learning - Dice Insights

Written by admin

December 9th, 2019 at 7:51 pm

Posted in Machine Learning

Page 16«..10..14151617