Achieving production-level governance with machine-learning projects currently presents unique challenges. A new space of tools and practices is emerging under the name MLOps. The space is analogous to DevOps but tailored to the practices and workflows of machine learning.

Machine learning models make predictions for new data based on the data they have been trained on. Managing this data in a way that can be safely used in live environments is challenging, and one of the key reasons why 80 per cent of data science projects never make it to production an estimate from Gartner.

It is essential that the data is clean, correct, and safe to use without any privacy or bias issues. Real-world data can also continuously change, so inputs and predictions have to be monitored for any shifts that may be problematic for the model. These are complex challenges that are distinct from those found in traditional DevOps.

DevOps practices are centred on the build and release process and continuous integration. Traditional development builds are packages of executable artifacts compiled from source code. Non-code supporting data in these builds tends to be limited to relatively small static config files. In essence, traditional DevOps is geared to building programs consisting of sets of explicitly defined rules that give specific outputs in response to specific inputs.

In contrast, machine-learning models make predictions by indirectly capturing patterns from data, not by formulating all the rules. A characteristic machine-learning problem involves making new predictions based on known data, such as predicting the price of a house using known house prices and details such as the number of bedrooms, square footage, and location. Machine-learning builds run a pipeline that extracts patterns from data and creates a weighted machine-learning model artifact. This makes these builds far more complex and the whole data science workflow more experimental. As a result, a key part of the MLOps challenge is supporting multi-step machine learning model builds that involve large data volumes and varying parameters.

To run projects safely in live environments, we need to be able to monitor for problem situations and see how to fix things when they go wrong. There are pretty standard DevOps practices for how to record code builds in order to go back to old versions. But MLOps does not yet have standardisation on how to record and go back to the data that was used to train a version of a model.

There are also special MLOps challenges to face in the live environment. There are largely agreed DevOps approaches for monitoring for error codes or an increase in latency. But its a different challenge to monitor for bad predictions. You may not have any direct way of knowing whether a prediction is good, and may have to instead monitor indirect signals such as customer behaviour (conversions, rate of customers leaving the site, any feedback submitted). It can also be hard to know in advance how well your training data represents your live data. For example, it might match well at a general level but there could be specific kinds of exceptions. This risk can be mitigated with careful monitoring and cautious management of the rollout of new versions.

The effort involved in solving MLOps challenges can be reduced by leveraging a platform and applying it to the particular case. Many organisations face a choice of whether to use an off-the-shelf machine-learning platform or try to put an in-house platform together themselves by assembling open-source components.

Some machine-learning platforms are part of a cloud providers offering, such as AWS SageMaker or AzureML. This may or may not appeal, depending on the cloud strategy of the organisation. Other platforms are not cloud-specific and instead offer self-install or a custom hosted solution (eg, Databricks MLflow).

Instead of choosing a platform, organisations can instead choose to assemble their own. This may be a preferred route when requirements are too niche to fit a current platform, such as needing integrations to other in-house systems or if data has to be stored in a particular location or format. Choosing to assemble an in-house platform requires learning to navigate the ML tool landscape. This landscape is complex with different tools specialising in different niches and in some cases there are competing tools approaching similar problems in different ways (see the Linux Foundations LF AI project for a visualization or categorised lists from the Institute for Ethical AI).

The Linux Foundations diagram of MLOps tools ... Click for full detail

For organisations using Kubernetes, the kubeflow project presents an interesting option as it aims to curate a set of open-source tools and make them work well together on kubernetes. The project is led by Google, and top contributors (as listed by IBM) include IBM, Cisco, Caicloud, Amazon, and Microsoft, as well as ML tooling provider Seldon, Chinese tech giant NetEase, Japanese tech conglomerate NTT, and hardware giant Intel.

Challenges around reproducibility and monitoring of machine learning systems are governance problems. They need to be addressed in order to be confident that a production system can be maintained and that any challenges from auditors or customers can be answered. For many projects these are not the only challenges as customers might reasonably expect to be able to ask why a prediction concerning them was made. In some cases this may also be a legal requirement as the European Unions General Data Protection Regulation states that a "data subject" has a right to "meaningful information about the logic involved" in any automated decision that relates to them.

Explainability is a data science problem in itself. Modelling techniques can be divided into black-box and white-box, depending on whether the method can naturally be inspected to provide insight into the reasons for particular predictions. With black-box models, such as proprietary neural networks, the options for interpreting results are more restricted and more difficult to use than the options for interpreting a white-box linear model. In highly regulated industries, it can be impossible for AI projects to move forward without supporting explainability. For example, medical diagnosis systems may need to be highly interpretable so that they can be investigated when things go wrong or so that the model can aid a human doctor. This can mean that projects are restricted to working with models that admit of acceptable interpretability. Making black-box models more interpretable is a fast-growth area, with new techniques rapidly becoming available.

The MLOps scene is evolving as machine-learning becomes more widely adopted, and we learn more about what counts as best practice for different use cases. Different organisations have different machine learning use cases and therefore differing needs. As the field evolves well likely see greater standardisation, and even the more challenging use cases will become better supported.

Ryan Dawson is a core member of the Seldon open-source team, providing tooling for machine-learning deployments to Kubernetes. He has spent 10 years working in the Java development scene in London across a variety of industries.

Bringing DevOps principles to machine learning throws up some unique challenges, not least very different workflows and artifacts. Ryan will dive into this topic in May at Continuous Lifecycle London 2020 a conference organized by The Register's mothership, Situation Publishing.

You can find out more, and book tickets, right here.

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Roundup Let's get cracking with some machine-learning news.

Starksy Robotics is no more: Self-driving truck startup Starsky Robotics has shut down after running out of money and failing to raise more funds.

CEO Stefan Seltz-Axmacher bid a touching farewell to his upstart, founded in 2016, in a Medium post this month. He was upfront and honest about why Starsky failed: Supervised machine learning doesnt live up to the hype, he declared. It isnt actual artificial intelligence akin to C-3PO, its a sophisticated pattern-matching tool.

Neural networks only learn to pick up on certain patterns after they are faced with millions of training examples. But driving is unpredictable, and the same route can differ day to day, depending on the weather or traffic conditions. Trying to model every scenario is not only impossible but expensive.

In fact, the better your model, the harder it is to find robust data sets of novel edge cases. Additionally, the better your model, the more accurate the data you need to improve it, Seltz-Axmacher said.

More time and money is needed to provide increasingly incremental improvements. Over time, only the most well funded startups can afford to stay in the game, he said.

Whenever someone says autonomy is ten years away thats almost certainly what their thought is. There arent many startups that can survive ten years without shipping, which means that almost no current autonomous team will ever ship AI decision makers if this is the case, he warned.

If Seltz-Axmacher is right, then we should start seeing smaller autonomous driving startups shutting down in the near future too. Watch this space.

Waymo to pause testing during Bay Area lockdown: Waymo, Googles self-driving car stablemate, announced it was pausing its operations in California to abide by the lockdown orders in place in Bay Area counties, including San Francisco, Santa Clara, San Mateo, Marin, Contra Costa and Alameda. Businesses deemed non-essential were advised to close and residents were told to stay at home, only popping out for things like buying groceries.

It will, however, continue to perform rides for deliveries and trucking services for its riders and partners in Phoenix, Arizona. These drives will be entirely driverless, however, to minimise the chance of spreading COVID-19.

Waymo also launched its Open Dataset Challenge. Developers can take part in a contest that looks for solutions to these problems:

Cash prizes are up for grabs too. The winner can expect to pocket $15,000, second place will get you $5,000, while third is $2,000.

You can find out more details on the rules of the competition and how to enter here. The challenge is open until 31 May.

More free resources to fight COVID-19 with AI: Tech companies are trying to chip in and do what they can to help quell the coronavirus pandemic. Nvidia and Scale AI both offered free resources to help developers using machine learning to further COVID-19 research.

Nvidia is providing a free 90-day license to Parabricks, a software package that speeds up the process of analyzing genome sequences using GPUs. The rush is on to analyze the genetic information of people that have been infected with COVID-19 to find out how the disease spreads and which communities are most at risk. Sequencing genomes requires a lot of number crunching, Parabricks slashes the time needed to complete the task.

Given the unprecedented spread of the pandemic, getting results in hours versus days could have an extraordinary impact on understanding the viruss evolution and the development of vaccines, it said this week.

Interested customers who have access to Nvidias GPUs should fill out a form requesting access to Parabricks.

Nvidia is inviting our family of partners to join us in matching this urgent effort to assist the research community. Were in discussions with cloud service providers and supercomputing centers to provide compute resources and access to Parabricks on their platforms.

Next up is Scale AI, the San Francisco based startup focused on annotating data for machine learning models. It is offering its labeling services for free to any researcher working on a potential vaccine, or on tracking, containing, or diagnosing COVID-19.

Given the scale of the pandemic, researchers should have every tool at their disposal as they try to track and counter this virus, it said in a statement.

Researchers have already shown how new machine learning techniques can help shed new light on this virus. But as with all new diseases, this work is much harder when there is so little existing data to go on.

In those situations, the role of well-annotated data to train models o diagnostic tools is even more critical. If you have a lot of data to analyse and think Scale AI could help then apply for their help here.

PyTorch users, AWS has finally integrated the framework: Amazon has finally integrated PyTorch support into Amazon Elastic Inference, its service that allows users to select the right amount of GPU resources on top of CPUs rented out in its cloud services Amazon SageMaker and Amazon EC2, in order to run inference operations on machine learning models.

Amazon Elastic Inference works like this: instead of paying for expensive GPUs, users select the right amount of GPU-powered inference acceleration on top of cheaper CPUs to zip through the inference process.

In order to use the service, however, users will have to convert their PyTorch code into TorchScript, another framework. You can run your models in any production environment by converting PyTorch models into TorchScript, Amazon said this week. That code is then processed by an API in order to use Amazon Elastic Inference.

The instructions to convert PyTorch models into the right format for the service have been described here.

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Self-driving truck boss: 'Supervised machine learning doesnt live up to the hype. It isnt C-3PO, its sophisticated pattern matching' - The Register

Do you know how Google Maps predicts traffic? Are you amused by how Amazon Prime or Netflix subscribes to you just the movie you would watch? We all know it must be some approach of Artificial Intelligence. Machine Learning involves algorithms and statistical models to perform tasks. This same approach is used to find faces in Facebook and detect cancer too. A Machine Learning course can educate in the development and application of such models.

Artificial Intelligence mimics human intelligence. Machine Learning is one of the significant branches of it. There is an ongoing and increasing need for its development.

Tasks as simple as Spam detection in Gmail illustrates its significance in our day-to-day lives. That is why the roles of Data scientists are in demand to yield more productivity at present. An aspiring data scientist can learn to develop algorithms and apply such by availing Machine Learning certification.

Machine learning as a subset of Artificial Intelligence, is applied for varied purposes. There is a misconception that applying Machine Learning algorithms would need a prior mathematical knowledge. But, a Machine Learning Online course would suggest otherwise. On contrary to the popular approach of studying, here top-to-bottom approach is involved. An aspiring data scientist, a business person or anyone can learn how to apply statistical models for various purposes. Here, is a list of some well-known applications of Machine Learning.

Microsofts research lab uses Machine Learning to study cancer. This helps in Individualized oncological treatment and detailed progress reports generation. The data engineers apply pattern recognition, Natural Language Processing and Computer vision algorithms to work through large data. This aids oncologists to conduct precise and breakthrough tests.

Likewise, machine learning is applied in biomedical engineering. This has led to automation of diagnostic tools. Such tools are used in detecting neurological and psychiatric disorders of many sorts.

We all have had a conversation with Siri or Alexa. They use speech recognition to input our requests. Machine Learning is applied here to auto generate responses based on previous data. Hello Barbie is the Siri version for the kids to play with. It uses advanced analytics, machine learning and Natural language processing to respond. This is the first AI enabled toy which could lead to more such inventions.

Google uses Machine Learning statistical models to acquire inputs. The statistical models collect details such as distance from the start point to the endpoint, duration and bus schedules. Such historical data is rescheduled and reused. Machine Learning algorithms are developed with the objective of data prediction. They recognise the pattern between such inputs and predict approximate time delays.

Another well-known application of Google, Google translate involves Machine Learning. Deep learning aids in learning language rules through recorded conversations. Neural networks such as Long-short term memory networks aids in long-term information updates and learning. Recurrent Neural networks identify the sequences of learning. Even bi-lingual processing is made feasible nowadays.

Facebook uses image recognition and computer vision to detect images. Such images are fed as inputs. The statistical models developed using Machine Learning maps any information associated with these images. Facebook generates automated captions for images. These captions are meant to provide directions for visually impaired people. This innovation of Facebook has nudged Data engineers to come up with other such valuable real-time applications.

The aim here is to increase the possibility of the customer, watching a movie recommendation. It is achieved by studying the previous thumbnails. An algorithm is developed to study these thumbnails and derive recommendation results. Every image of available movies has separate thumbnails. A recommendation is generated by pattern recognition among the numerical data. The thumbnails are assigned individual numerical values.

Tesla uses computer vision, data prediction, and path planning for this purpose. The machine learning practices applied makes the innovation stand-out. The deep neural networks work with trained data and generate instructions. Many technological advancements such as changing lanes are instructed based on imitation learning.

Gmail, Yahoo mail and Outlook engage machine learning techniques such as neural networks. These networks detect patterns in historical data. They train on received data about spamming messages and phishing messages. It is noted that these spam filters provide 99.9 percent accuracy.

As people grow more health conscious, the development of fitness monitoring applications are on the rise. Being on top of the market, Fitbit ensures its productivity by the employment of machine learning methods. The trained machine learning models predicts user activities. This is achieved through data pre-processing, data processing and data partitioning. There is a need to improve the application in terms of additional purposes.

The above mentioned applications are like the tip of an iceberg. Machine learning being a subset of Artificial Intelligence finds its necessity in many other streams of daily activities.

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Machine Learning: Real-life applications and it's significance in Data Science - Techstory

The Machine Learning as a Service market report presents an in-depth assessment of the Machine Learning as a Service together with market drivers, challenges, enabling technologies, applications, key trends, standardization, regulative landscape, case studies, opportunities, future roadmap, worth chain, system player profiles and techniques. The study provides historic data form 2015 to 2019 along with forecast from 2020 to 2025 based on sales (volume and value) and revenue (USD Million). During a recently published report by Reportspedia.com, the global Machine Learning as a Service market is predicted to register a high CAGR during the Forecast period.

The study demonstrates market dynamics that are expected to influence this challenges and future standing of the global Machine Learning as a Service market over the forecast period. This report also offers updates on manufacturers, trends, drivers, restraints, worth forecasts, and opportunities for makers in operation within the global and regional Machine Learning as a Service market.

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GoogleIBM CorporationMicrosoft CorporationAmazon Web ServicesBigMLFICOYottamine AnalyticsErsatz LabsPredictron LabsH2O.aiAT&TSift Science

The key regions and countries covered in this report are:

Assessment of the Machine Learning as a Service Market

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ManufacturingRetailHealthcare & Life SciencesTelecomBFSIOther (Energy & Utilities, Education, Government)

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Table of Contents for market shares by application, research objectives, market sections by type and forecast years considered.

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Table of Content:

1 Machine Learning as a Service Market Survey

2 Executive Synopsis

3 Global Machine Learning as a Service Market Race by Manufacturers

4 Global Machine Learning as a Service Production Market Share by Regions

5 Global Machine Learning as a Service Consumption by Regions

6 Global Machine Learning as a Service Production, Revenue, Price Trend by Type

7 Global Machine Learning as a Service Market Analysis by Applications

8 Machine Learning as a Service Manufacturing Cost Examination

9 Advertising Channel, Suppliers and Clienteles

10 Market Dynamics

11 Global Machine Learning as a Service Market Estimate

12 Investigations and Conclusion

13 Important Findings in the Global Machine Learning as a Service Study

14 Appendixes

15 company Profile

Continued.

Ask For Detailed Table Of Content With Table Of Figures:https://www.reportspedia.com/report/technology-and-media/global-machine-learning-as-a-service-market-2019-by-company,-regions,-type-and-application,-forecast-to-2025/17678#table_of_contents

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Global Machine Learning as a Service Market: Industrial Output, Import & Export, Consumer Consumption And Forecast 2025 - News Times

Written by Dave Nyczepir Feb 19, 2020 | FEDSCOOP

National Oceanic and Atmospheric Administration scientists are preparing to use machine learning (ML) to more easily monitor threatened ice seal populations in Alaska between April and May.

Ice flows are critical to seal life cycles but are melting due to climate change which has hit the Arctic and sub-Arctic regions hardest. So scientists are trying to track species population distributions.

But surveying millions of aerial photographs of sea ice a year for ice seals takes months. And the data is outdated by the time statisticians analyze it and share it with the NOAA assistant regional administrator for protected resources in Juneau, according to aMicrosoft blog post.

NOAAs Juneau office oversees conservation and recovery programs for marine mammals statewide and can instruct other agencies to limit permits for activities that might hurt species feeding or breeding. The faster NOAA processes scientific data, the faster it can implement environmental sustainability policies.

The amazing thing is how consistent these problems are from scientist to scientist, Dan Morris, principal scientist and program director of MicrosoftAI for Earth, told FedScoop.

To speed up monitoring from months to mere hours, NOAAs Marine Mammal Laboratory partnered with AI for Earth in the summer of 2018 to develop ML models recognizing seals in real-time aerial photos.

The models were trained during a one-week hackathon using 20 terabytes of historical survey data in the cloud.

In 2007, the first NOAA survey done by helicopter captured about 90,000 images that took months to analyze and find 200 seals. The challenge isthe seals are solitary, and aircraft cant fly so low as to spook them. But still, scientists need images to capture the difference between threatened bearded and ringed seals and unthreatened spotted and ribbon seals.

Alaskas rainy, cloudy climate has led scientists to adopt thermal and color cameras, but dirty ice and reflections continue to interfere. A 2016 survey of 1 million sets of images took three scientists six months to identify about 316,000 seal hotspots.

Microsofts ML, on the other hand, can distinguish seals from rocks and, coupled with improved cameras on a NOAA turboprop airplane, will be used in flyovers of the Beaufort Sea this spring.

NOAA released a finalized Artificial Intelligence Strategy on Tuesday aimed at reducing the cost of data processing and incorporating AI into scientific technologies and services addressing mission priorities.

Theyre a very mature organization in terms of thinking about incorporating AI into remote processing of their data, Morris said.

The camera systems on NOAA planes are also quite sophisticated because the agencys forward-thinking ecologists are assembling the best hardware, software and expertise for their biodiversity surveys, he added.

While the technical integration of AI for Earths models with the software systems on NOAAs planes has taken a year to perfect, another agency project was able to apply a similar algorithm more quickly.

The Cook Inlets endangered beluga whale population numbered 279 last year down from about 1,000three decades ago.

Belugas increasingly rely on echolocation to communicate with sediment from melting glaciers dirtying the water they live in. But the noise from an increasing number of cargo ships and military and commercial flights can disorient the whales. Calves can get lost if they cant hear their mothers clicks and whistles, and adults cant catch prey or identify predators.

NOAA is using ML tools to distinguish a whales whistle from man-made noises and identify areas where theres dangerous overlap, such as where belugas feed and breed. The agency can then limit construction or transportation during those periods, according to the blog post.

Previously, the projects 15 mics recorded sounds for six months along the seafloor, scientists collected the data, and then they spent the remainder of the year classifying noises to determine how the belugas spent their time.

AI for Earths algorithms matched scientists previously classified logs 99 percent of the time last fall and have been since introduced into the field.

The ML was implemented faster than the seal projects because the software runs offline at a lab in Seattle, so integration was easier, Morris said.

NOAA intends to employ ML in additional biodiversity surveys. And AI for Earth plans to announce more environmental sustainability projects in the acoustic space in the coming weeks, Morris added, thoughhe declined to name partners.

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Machine learning is making NOAA's efforts to save ice seals and belugas faster - FedScoop

At HIMSS20 next month, two machine learning experts will show how machine learning algorithms are evolving to handle complex physiological data and drive more detailed clinical insights.

During surgery and other critical care procedures, continuous monitoring of blood pressure to detect and avoid the onset of arterial hypotension is crucial. New machine learning technology developed by Edwards Lifesciences has proven to be an effective means of doing this.

In the prodromal stage of hemodynamic instability, which is characterized by subtle, complex changes in different physiologic variables unique dynamic arterial waveform "signatures" are formed, which require machine learning and complex feature extraction techniques to be utilized.

Feras Hatib, director of research and development for algorithms and signal processing at Edwards Lifesciences, explained his team developed a technology that could predict, in real-time and continuously, upcoming hypertension in acute-care patients, using an arterial pressure waveforms.

We used an arterial pressure signal to create hemodynamic features from that waveform, and we try to assess the state of the patient by analyzing those signals, said Hatib, who is scheduled to speak about his work at HIMSS20.

His teams success offers real-world evidence as to how advanced analytics can be used to inform clinical practice by training and validating machine learning algorithms using complex physiological data.

Machine learning approaches were applied to arterial waveforms to develop an algorithm that observes subtle signs to predict hypotension episodes.

In addition, real-world evidence and advanced data analytics were leveraged to quantify the association between hypotension exposure duration for various thresholds and critically ill sepsis patient morbidity and mortality outcomes.

"This technology has been in Europe for at least three years, and it has been used on thousands of patients, and has been available in the US for about a year now," he noted.

Hatib noted similar machine learning models could provide physicians and specialists with information that will help prevent re-admissions or other treatment options, or help prevent things like delirium current areas of active development.

"In addition to blood pressure, machine learning could find a great use in the ICU, in predicting sepsis, which is critical for patient survival," he noted. "Being able to process that data in the ICU or in the emergency department, that would be a critical area to use these machine learning analytics models."

Hatib pointed out the way in which data is annotated in his case, defining what is hypertension and what is not is essential in building the machine learning model.

"The way you label the data, and what data you include in the training is critical," he said. "Even if you have thousands of patients and include the wrong data, that isnt going to help its a little bit of an art to finding the right data to put into the model."

On the clinical side, its important to tell the clinician what the issue is in this case what is causing hypertension.

"You need to provide to them the reasons that could be causing the hypertension this is why we complimented the technology with a secondary screen telling the clinician what is physiologically is causing hypertension," he explained. "Helping them decide what do to about it was a critical factor."

Hatib said in the future machine learning will be everywhere, because scientists and universities across the globe are hard at work developing machine learning models to predict clinical conditions.

"The next big step I see is going toward using this ML techniques where the machine takes care of the patient and the clinician is only an observer," he said.

Feras Hatib, along with Sibyl Munson of Boston Strategic Partners, will share some machine learning best practices during his HIMSS20 in a session, "Building a Machine Learning Model to Drive Clinical Insights." It's scheduled for Wednesday, March 11, from 8:30-9:30 a.m. in room W304A.

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Machine learning and clinical insights: building the best model - Healthcare IT News

The report on the global machine learning as a service market provides qualitative and quantitative analysis for the period from 2016 to 2024. The report predicts the global machine learning as a service market to grow with a CAGR of 38.

New York, Feb. 20, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Machine Learning as a Service Market: Global Industry Analysis, Trends, Market Size, and Forecasts up to 2024" - https://www.reportlinker.com/p05751673/?utm_source=GNW 5% over the forecast period from 2018-2024. The study on machine learning as a service market covers the analysis of the leading geographies such as North America, Europe, Asia-Pacific, and RoW for the period of 2016 to 2024.

The report on machine learning as a service market is a comprehensive study and presentation of drivers, restraints, opportunities, demand factors, market size, forecasts, and trends in the global machine learning as a service market over the period of 2016 to 2024. Moreover, the report is a collective presentation of primary and secondary research findings.

Porters five forces model in the report provides insights into the competitive rivalry, supplier and buyer positions in the market and opportunities for the new entrants in the global machine learning as a service market over the period of 2016 to 2024. Further, IGR- Growth Matrix gave in the report brings an insight into the investment areas that existing or new market players can consider.

Report Findings1) Drivers Increasing use in cloud technologies Provides statistical analysis along with reduce time and cost Growing adoption of cloud based systems2) Restraints Less skilled personnel3) Opportunities Technological advancement

Research Methodology

A) Primary ResearchOur primary research involves extensive interviews and analysis of the opinions provided by the primary respondents. The primary research starts with identifying and approaching the primary respondents, the primary respondents are approached include1. Key Opinion Leaders associated with Infinium Global Research2. Internal and External subject matter experts3. Professionals and participants from the industry

Our primary research respondents typically include1. Executives working with leading companies in the market under review2. Product/brand/marketing managers3. CXO level executives4. Regional/zonal/ country managers5. Vice President level executives.

B) Secondary ResearchSecondary research involves extensive exploring through the secondary sources of information available in both the public domain and paid sources. At Infinium Global Research, each research study is based on over 500 hours of secondary research accompanied by primary research. The information obtained through the secondary sources is validated through the crosscheck on various data sources.

The secondary sources of the data typically include1. Company reports and publications2. Government/institutional publications3. Trade and associations journals4. Databases such as WTO, OECD, World Bank, and among others.5. Websites and publications by research agencies

Segment CoveredThe global machine learning as a service market is segmented on the basis of component, application, and end user.

The Global Machine Learning As a Service Market by Component Software Services

The Global Machine Learning As a Service Market by Application Marketing & Advertising Fraud Detection & Risk Management Predictive Analytics Augmented & Virtual Reality Security & Surveillance Others

The Global Machine Learning As a Service Market by End User Retail Manufacturing BFSI Healthcare & Life Sciences Telecom Others

Company Profiles IBM PREDICTRON LABS H2O.ai. Google LLC Crunchbase Inc. Microsoft Yottamine Analytics, LLC Fair Isaac Corporation. BigML, Inc. Amazon Web Services, Inc.

What does this report deliver?1. Comprehensive analysis of the global as well as regional markets of the machine learning as a service market.2. Complete coverage of all the segments in the machine learning as a service market to analyze the trends, developments in the global market and forecast of market size up to 2024.3. Comprehensive analysis of the companies operating in the global machine learning as a service market. The company profile includes analysis of product portfolio, revenue, SWOT analysis and latest developments of the company.4. IGR- Growth Matrix presents an analysis of the product segments and geographies that market players should focus to invest, consolidate, expand and/or diversify.Read the full report: https://www.reportlinker.com/p05751673/?utm_source=GNW

About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

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Global machine learning as a service market is expected to grow with a CAGR of 38.5% over the forecast period from 2018-2024 - Yahoo Finance

Retailers are harnessing the power of new technology to dig deeper into customer decisions and bring people back into stores.

The National Retail Federation's 2020 Big Show in New York was jam packed full of robots, frictionless store mock-ups, and audacious displays of the latest technology now available to retailers.

Dozens of robots, digital signage tools, and more were available for retail representatives to test out, with hundreds of the biggest tech companies in attendance offering a bounty of eye-popping gadgets designed to increase efficiency and bring the wow factor back to brick-and-mortar stores.

SEE: Artificial intelligence: A business leader's guide (free PDF) (TechRepublic)

Here are some of the biggest takeaways from the annual retail event.

With the explosion in popularity of Amazon, Alibaba, and other e-commerce sites ready to deliver goods right to your door within days, many analysts and retailers figured the brick-and-mortar stores of the past were on their last legs.

But it turns out billions of customers still want the personal, tailored touch of in-store experiences and are not ready to completely abandon physical retail outlets.

"It's not a retail apocalypse. It's a retail renaissance," said Lori Mitchell-Keller, executive vice president and global general manager of consumer industries at SAP.

As leader of SAP's retail, wholesale distribution, consumer products, and life sciences industries division, Mitchell-Keller said she was surprised to see that retailers had shifted their stance and were looking to find ways to beef up their online experience while infusing stores with useful but flashy technology.

"Brick-and-mortar stores have this unique capability to have a specific advantage against online retailers. So despite the trend where everything was going online, it did not mean online at the expense of brick-and-mortar. There is a balance between the two. Those companies that have a great online experience and capability combined with a brick-and-mortar store are in the best place in terms of their ability to be profitable," Mitchell-Keller said during an interview at NRF 2020.

"There is an experience that you cannot get online. This whole idea of customer experience and experience management is definitely the best battleground for the guys that can't compete in delivery. Even for the ones that can compete on delivery, like the Walmarts and Targets, they are using their brick-and-mortar stores to offer an experience that you can't get online. We thought five years ago that brick-and-mortar was dead and it's absolutely not dead. It's actually an asset."

In her experience working with the world's biggest retailers, companies that have a physical presence actually have a huge advantage because customers are now yearning for a personalized experience they can't get online. While e-commerce sites are fast, nothing can beat the ability to have real people answer questions and help customers work through their options, regardless of what they're shopping for.

Retailers are also transforming parts of their stores into fulfillment centers for their online sales, which have the doubling effect of bringing customers into the store where they may spend even more on things they see.

"The brick-and-mortar stores that are using their stores as fulfillment centers have a much lower cost of delivery because they're typically within a few miles of customers. If they have a great online capability and good store fulfillment, they're able to get to customers faster than the aggregators," Mitchell-Keller said. "It's better to have both."

SEE: Feature comparison: E-commerce services and software (TechRepublic Premium)

But one of the main trends, and problems, highlighted at NRF 2020 was the sometimes difficult transition many retailers have had to make to a digitized world.

NRF 2020 was full of decadent tech retail tools like digital price tags, shelf-stocking robots and next-gen advertising signage, but none of this could be incorporated into a retail environment without a basic amount tech talent and systems to back it all.

"It can be very overwhelmingly complicated, not to mention costly, just to have a team to manage technology and an environment that is highly digitally integrated. The solution we try to bring to bear is to add all these capabilities or applications into a turn key environment because fundamentally, none of it works without the network," said Michael Colaneri, AT&T's vice president of retail, restaurants and hospitality.

While it would be easy for a retailer to leave NRF 2020 with a fancy robot or cool gadget, companies typically have to think bigger about the changes they want to see, and generally these kinds of digital transformations have to be embedded deep throughout the supply chain before they can be incorporated into stores themselves.

Colaneri said much of AT&T's work involved figuring out how retailers could connect the store system, the enterprise, the supply chain and then the consumer, to both online and offline systems. The e-commerce part of retailer's business now had to work hand in hand with the functionality of the brick-and-mortar experience because each part rides on top of the network.

"There are five things that retailers ask me to solve: Customer experience, inventory visibility, supply chain efficiency, analytics, and the integration of media experiences like a robot, electronic shelves or digital price tags. How do I pull all this together into a unified experience that is streamlined for customers?" Colaneri said.

"Sometimes they talk to me about technical components, but our number one priority is inventory visibility. I want to track products from raw material to where it is in the legacy retail environment. Retailers also want more data and analytics so they can get some business intelligence out of the disparate data lakes they now have."

The transition to digitized environments is different for every retailer, Colaneri added. Some want slow transitions and gradual introductions of technology while others are desperate for a leg up on the competition and are interested in quick makeovers.

While some retailers have balked at the thought, and price, of wholesale changes, the opposite approach can end up being just as costly.

"Anybody that sells you a digital sign, robot, Magic Mirror or any one of those assets is usually partnering with network providers because it requires the network. And more importantly, what typically happens is if someone buys an asset, they are underestimating the requirements it's going to need from their current network," Colaneri said.

"Then when their team says 'we're already out of bandwidth,' you'll realize it wasn't engineered and that the application wasn't accommodated. It's not going to work. It can turn into a big food fight."

Retailers are increasingly realizing the value of artificial intelligence and machine learning as a way to churn through troves of data collected from customers through e-commerce sites. While these tools require the kind of digital base that both Mitchell-Keller and Colaneri mentioned, artificial intelligence (AI) and machine learning can be used to address a lot of the pain points retailers are now struggling with.

Mitchell-Keller spoke of SAP's work with Costco as an example of the kind of real-world value AI and machine learning can add to a business. Costco needed help reducing waste in their bakeries and wanted better visibility into when customers were going to buy particular products on specific days or at specific times.

"Using machine learning, what SAP did was take four years of data out of five different stores for Costco as a pilot and used AI and machine learning to look through the data for patterns to be able to better improve their forecasting. They're driving all of their bakery needs based on the forecast and that forcecast helped Costco so much they were able to reduce their waste by about 30%," Mitchell-Keller said, adding that their program improved productivity by 10%.

SAP and dozens of other tech companies at NRF 2020 offered AI-based systems for a variety of supply chain management tools, employee payment systems and even resume matches. But AI and machine learning systems are nothing without more data.

SEE:Managing AI and ML in the enterprise 2019: Tech leaders expect more difficulty than previous IT projects(TechRepublic Premium)

Jeff Warren, vice president of Oracle Retail, said there has been a massive shift toward better understanding customers through increased data collection. Historically, retailers simply focused on getting products through the supply chain and into the hands of consumers. But now, retailers are pivoting toward focusing on how to better cater services and goods to the customer.

Warren said Oracle Retail works with about 6,000 retailers in 96 different countries and that much of their work now prioritizes collecting information from every customer interaction.

"What is new is that when you think of the journey of the consumer, it's not just about selling anymore. It's not just about ringing up a transaction or line busting. All of the interactions between you and me have value and hold something meaningful from a data perspective," he said, adding that retailers are seeking to break down silos and pool their data into a single platform for greater ease of use.

"Context would help retailers deliver a better experience to you. Its petabytes of information about what the US consumer market is spending and where they're spending. We can take the information that we get from those interactions that are happening at the point of sale about our best customers and learn more."

With the Oracle platform, retailers can learn about their customers and others who may have similar interests or live in similar places. Companies can do a better job of targeting new customers when they know more about their current customers and what else they may want.

IBM is working on similar projects with hundreds of different retailers , all looking to learn more about their customers and tailor their e-commerce as well as in-store experience to suit their biggest fans.

IBM global managing director for consumer industries Luq Niazi told TechRepublic during a booth tour that learning about consumer interests was just one aspect of how retailers could appeal to customers in the digital age.

"Retailers are struggling to work through what tech they need. When there is so much tech choice, how do you decide what's important? Many companies are implementing tech that is good but implemented badly, so how do you help them do good tech implemented well?" Niazi said.

"You have all this old tech in stores and you have all of this new tech. You have to think about how you bring the capability together in the right way to deploy flexibly whatever apps and experiences you need from your store associate, for your point of sale, for your order management system that is connected physically and digitally. You've got to bring those together in different ways. We have to help people think about how they design the store of the future."

Get expert tips on mastering the fundamentals of big data analytics, and keep up with the latest developments in artificial intelligence. Delivered Mondays

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AI, machine learning, robots, and marketing tech coming to a store near you - TechRepublic

Global Machine Learning In Medicine Market Insights, Forecast to 2026:

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Global Machine Learning in Automobile Market Insight Growth Analysis on Volume, Revenue and Forecast to 2019-2025 - News Parents

(Left- Wilson To, Head of Worldwide Healthcare BD, Amazon Web Services (AWS) & Patrick Combes, Worldwide Technical Leader Healthcare and Life Sciences at Amazon Web Services (AWS)- Right)

Navigating the healthcare system is often a complex journey involving multiple physicians from hospitals, clinics, and general practices. At each junction, healthcare providers collect data that serve as pieces in a patients medical puzzle. When all of that data can be shared at each point, the puzzle is complete and practitioners can better diagnose, care for, and treat that patient. However, a lack of interoperability inhibits the sharing of data across providers, meaning pieces of the puzzle can go unseen and potentially impact patient health.

The Challenge of Achieving Interoperability

True interoperability requires two parts: syntactic and semantic. Syntactic interoperability requires a common structure so that data can be exchanged and interpreted between health information technology (IT) systems, while semantic interoperability requires a common language so that the meaning of data is transferred along with the data itself.This combination supports data fluidity. But for this to work, organizations must look to technologies like artificial intelligence (AI) and machine learning (ML) to apply across that data to shift the industry from a fee-for-service where government agencies reimburse healthcare providers based on the number of services they provide or procedures ordered to a value-based model that puts focus back on the patient.

The industry has started to make significant strides toward reducing barriers to interoperability. For example, industry guidelines and resources like the Fast Healthcare Interoperability Resources (FHIR) have helped to set a standard, but there is still more work to be done. Among the biggest barriers in healthcare right now is the fact there are significant variations in the way data is shared, read, and understood across healthcare systems, which can result in information being siloed and overlooked or misinterpreted.

For example, a doctor may know that a diagnosis of dropsy or edema may be indicative of congestive heart failure, however, a computer alone may not be able to draw that parallel. Without syntactic and semantic interoperability, that diagnosis runs the risk of getting lost in translation when shared digitally with multiple health providers.

Employing AI, ML and Interoperability in Healthcare

Change Healthcare is one organization making strides to enable interoperability and help health organizations achieve this triple aim. Recently, Change Healthcareannounced that it is providing free interoperability services that breakdown information silos to enhance patients access to their medical records and support clinical decisions that influence patients health and wellbeing.

While companies like Change Healthcare are creating services that better allow for interoperability, others like Fred Hutchinson Cancer Research Center and Beth Israel Deaconess Medical Center (BIDMC) are using AI and ML to further break down obstacles to quality care.

For example, Fred Hutch is using ML to help identify patients for clinical trials who may benefit from specific cancer therapies. By using ML to evaluate millions of clinical notes and extract and index medical conditions, medications, and choice of cancer therapeutic options, Fred Hutch reduced the time to process each document from hours, to seconds, meaning they could connect more patients to more potentially life-saving clinical trials.

In addition, BIDMC is using AI and ML to ensure medical forms are completed when scheduling surgeries. By identifying incomplete forms or missing information, BIDMC can prevent delays in surgeries, ultimately enhancing the patient experience, improving hospital operations, and reducing costs.

An Opportunity to Transform The Industry

As technology creates more data across healthcare organizations, AI and ML will be essential to help take that data and create the shared structure and meaning necessary to achieve interoperability.

As an example, Cernera U.S. supplier of health information technology solutionsis deploying interoperability solutions that pull together anonymized patient data into longitudinal records that can be developed along with physician correlations. Coupled with other unstructured data, Cerner uses the data to power machine learning models and algorithms that help with earlier detection of congestive heart failure.

As healthcare organizations take the necessary steps toward syntactic and semantic interoperability, the industry will be able to use data to place a renewed focus on patient care. In practice, Philips HealthSuite digital platform stores and analyses 15 petabytes of patient data from 390 million imaging studies, medical records and patient inputsadding as much as one petabyte of new data each month.

With machine learning applied to this data, the company can identify at-risk patients, deliver definitive diagnoses and develop evidence-based treatment plans to drive meaningful patient results. That orchestration and execution of data is the definition of valuable patient-focused careand the future of what we see for interoperability drive by AI and ML in the United States. With access to the right information at the right time that informs the right care, health practitioners will have access to all pieces of a patients medical puzzleand that will bring meaningful improvement not only in care decisions, but in patients lives.

About Wilson To, Global Healthcare Business Development lead at AWS & Patrick Combes, Global Healthcare IT Lead at AWS

Wilson To is the Head Worldwide Healthcare Business Development at Amazon Web Services (AWS). currently leads business development efforts across the AWS worldwide healthcare practice.To has led teams across startup and corporate environments, receiving international recognition for his work in global health efforts. Wilson joined Amazon Web Services in October 2016 to lead product management and strategic initiatives.

Patrick Combes is the Worldwide Technical Leader for Healthcare Life & Sciences at Amazon (AWS) where he is responsible for AWS world-wide technical strategy in Healthcare and Life Sciences (HCLS). Patrick helps develop and implement the strategic plan to engage customers and partners in the industry and leads the community of technically focused HCLS specialists within AWS wide technical strategy in Healthcare and Life Sciences (HCLS). Patrick helps develop and implement the strategic plan to engage customers and partners in the industry and leads the community of technically focused HCLS specialists within AWS.

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Leveraging AI and Machine Learning to Advance Interoperability in Healthcare - - HIT Consultant

Dear Reader,

As of the beginning of January 2020, its my great pleasure to join The Machine Learning Times as editor in chief! Ive taken over the main editorial duties from Eric Siegel, who founded the ML Times (also the founder of the Predictive Analytics World conference series). As youve likely noticed, weve renamed to The Machine Learning Times what until recently was The Predictive Analytics Times. In addition to a new, shiny name, this rebranding corresponds with new efforts to expand and intensify our breadth of coverage. As editor in chief, Im taking the lead in this growth initiative. Were growing the MLTimes both quantitatively and qualitatively more articles, more writers, and more topics. One particular area of focus will be to increase our coverage of deep learning.

And speaking of deep learning, please consider joining me at this summers Deep Learning World 2020 May 31 June 4 in Las Vegas the co-located sister conference of Predictive Analytics World and part of Machine Learning Week. For the third year, I am chairing and moderating a broad ranging lineup of the latest industry use cases and applications in deep learning. This year, DLW features a new track on large scale deep learning deployment. You can view the full agenda here. In the coming months, the MLTimes will be featuring interviews with the speakers giving you sneak peeks into the upcoming conference presentations.

In addition to supporting the community in these two roles with the MLTimes and Deep Learning World, I am a fellow analytics practitioner yes, I practice what I preach! To learn more about my work leading and executing on advanced data science projects for high tech firms and major research universities in Silicon Valley, click here.

And finally, Attention All Writers: Whether youve published with us in the past or are considering publishing for the very first time, wed love to see original content submissions from you. Published articles gain strong exposure on our site, as well as within the monthly MLTimes email send. If you currently publish elsewhere, such as on a personal blog, consider publishing items as an article with us first, and then in your own blog two weeks thereafter (per our editorial guidelines). Doing so would provide you the opportunity to gain our readers eyes in addition to those you already reach.

Im excited to lead the MLTimes into a strong year. Weve already got a good start with greater amounts of exciting original content lined up for this and coming months. Please feel free to reach out to me with any feedback on our published content or if you are interested in submitting articles for consideration. For general inquiries, see the information on our editorial page and the contact information there. And to reach out to me directly, connect with me on LinkedIn.

Thanks for reading!

Best Regards,

Luba GloukhovaEditor in Chief, The Machine Learning TimesFounding Chair, Deep Learning World

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The ML Times Is Growing A Letter from the New Editor in Chief - Machine Learning Times - machine learning & data science news - The Predictive...

Originally published in EthicalSystems.org, December 1, 2019

In 2004, when a webcam was relatively unheard-of tech, Mark Newman knew that it would be the future of hiring. One of the first things the 20-year old did, after getting his degree in international business, was to co-found HireVue, a company offering a digital interviewing platform. Business trickled in. While Newman lived at his parents house, in Salt Lake City, the company, in its first five years, made just $100,000 in revenue. HireVue later received some outside capital, expanded and, in 2012, boasted some 200 clientsincluding Nike, Starbucks, and Walmartwhich would pay HireVue, depending on project volume, between $5,000 and $1 million. Recently, HireVue, which was bought earlier this year by the Carlyle Group, has become the source of some alarm, or at least trepidation, for its foray into the application of artificial intelligence in the hiring process. No longer does the company merely offer clients an asynchronous interviewing service, a way for hiring managers to screen thousands of applicants quickly by reviewing their video interview HireVue can now give companies the option of letting machine-learning algorithms choose the best candidates for them, based on, among other things, applicants tone, facial expressions, and sentence construction.

If that gives you the creeps, youre not alone. A 2017 Pew Research Center report found few Americans to be enthused, and many worried, by the prospect of companies using hiring algorithms. More recently, around a dozen interviewees assessed by HireVues AI told the Washington Post that it felt alienating and dehumanizing to have to wow a computer before being deemed worthy of a companys time. They also wondered how their recording might be used without their knowledge. Several applicants mentioned passing on the opportunity because thinking about the AI interview, as one of them told the paper, made my skin crawl. Had these applicants sat for a standard 30-minute interview, comprised of a half-dozen questions, the AI could have analyzed up to 500,000 data points. Nathan Mondragon, HireVues chief industrial-organizational psychologist, told the Washington Post that each one of those points become ingredients in the persons calculated score, between 1 and 100, on which hiring decisions candepend. New scores are ranked against a store of traitsmostly having to do with language use and verbal skillsfrom previous candidates for a similar position, who went on to thrive on the job.

HireVue wants you to believe that this is a good thing. After all, their pitch goes, humans are biased. If something like hunger can affect a hiring managers decisionlet alone classism, sexism, lookism, and other ismsthen why not rely on the less capricious, more objective decisions of machine-learning algorithms? No doubt some job seekers agree with the sentiment Loren Larsen, HireVues Chief Technology Officer, shared recently with theTelegraph: I would much prefer having my first screening with an algorithm that treats me fairly rather than one that depends on how tired the recruiter is that day. Of course, the appeal of AI hiring isnt just about doing right by the applicants. As a 2019 white paper, from the Society for Industrial and Organizational Psychology, notes, AI applied to assessing and selecting talent offers some exciting promises for making hiring decisions less costly and more accurate for organizations while also being less burdensome and (potentially) fairer for job seekers.

Do HireVues algorithms treat potential employees fairly? Some researchers in machine learning and human-computer interaction doubt it. Luke Stark, a postdoc at Microsoft Research Montreal who studies how AI, ethics, and emotion interact, told the Washington Post that HireVues claimsthat its automated software can glean a workers personality and predict their performance from such things as toneshould make us skeptical:

Systems like HireVue, he said, have become quite skilled at spitting out data points that seem convincing, even when theyre not backed by science. And he finds this charisma of numbers really troubling because of the overconfidence employers might lend them while seeking to decide the path of applicants careers.

The best AI systems today, he said, are notoriously prone to misunderstanding meaning and intent. But he worried that even their perceived success at divining a persons true worth could help perpetuate a homogenous corporate monoculture of automatons, each new hire modeled after the last.

Eric Siegel, an expert in machine learning and author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, echoed Starks remarks. In an email, Siegel told me, Companies that buy into HireVue are inevitably, to a great degree, falling for that feeling of wonderment and speculation that a kid has when playing with a Magic Eight Ball. That, in itself, doesnt mean HireVues algorithms are completely unhelpful. Driving decisions with data has the potential to overcome human bias in some situations, but also, if not managed correctly, could easily instill, perpetuate, magnify, and automate human biases, he said.

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The Problem with Hiring Algorithms - Machine Learning Times - machine learning & data science news - The Predictive Analytics Times

Fleet Owner returned to CES, the annual mega technology show in Las Vegas, in search of potential transportation technology that could help fleets of the future. Here are some news and notes from the more than a million square feet of exhibit space. You can read our coverage of other news out of CES here: (DOT on autonomous vehicles,Peterbilt,Kenworth and Dana,Bosch and ZF,BlackBerry, andmore).

Plus.ai announced at CES 2020 it will expand testing of its self-driving trucks to cover all permissible continental states in the U.S. by the end of 2020. This will include closed-course testing and public road testing, with a safety driver and operations specialist onboard to assume manual control if needed.

"We want to build a technology solution that is applicable across different weather, terrains, and driving scenarios, said Shawn Kerrigan, COO and co-founder ofPlus.ai. Testing our trucks readiness means we need to put them through stringent safety tests, on every highway in the country.

Plus.ai has conducted autonomous truck testing in 17 states: Arizona, California, Colorado, Illinois, Indiana, Kansas, Minnesota, Missouri, Nevada, New Mexico, Ohio, Pennsylvania, South Dakota, Texas, Utah, West Virginia and Wyoming.

"Thesmart mobility ecosystem weve established in Ohiois a premier testing ground for autonomous vehicles," said Patrick Smith, interim executive director ofDriveOhio. Ohio is excited to welcome leading autonomous trucking companies like Plus.ai to test at our state-of-the-art facilities and infrastructure.

Plus.ai expects that the new testing sites and states will be selected by the spring and implementation will take place through the rest of the year.

Ryder's outdoor booth at CES 2020 featured a Nikola Two truck.Josh Fisher/Fleet Owner

Ryder System was among the trucking and logistics companies exhibiting at CES this year. And helping the company catch the eye of attendees was the Nikola Two tractor on display at its outdoor booth that focused on the future of transportation logistics and equipment.

Ryder is showing current and potential leasing customers what is available now and around the corner in electric and automated trucks and how they can help increase supply chain efficiency.

Bridgestone made its first appearance at CES, and highlighted its mobility solutions that look toward an autonomous future focused on extended mobility, improved safety and increased efficiency.

The company showed off its future airless tires, smart tire technology and its Webfleet Solutions platform. That platform uses data and analytics to move millions of vehicles as efficiently as possible.

"Bridgestone has a nearly 90-year history of using technology and research to develop advanced products, services and solutions for a world in motion," said TJ Higgins, global chief strategic officer of Bridgestone. As we look to the future, we are combining our core tire expertise with a wide range of digital solutions to deliver connected products and services that promote safe, sustainable mobility and continue contributing to society's advancement."

The company's CES showcase demonstrated how airless tires from Bridgestone combine a tire's tread and wheel into one durable, high-strength structure. This design eliminates the need for tires to be filled and maintained with air.

The company also showed how its digital twin and connected tire technology can be used to generate specific, actionable predictions that can enhance the precision of vehicle safety systems.

The Bosch Virtual Visor uses LCD and AI technology to keep a driver's eyes in the shade.Bosch Global

Bosch unveiled what is called the most drastic improvement to the 100-year-old sun visor.

The Virtual Visor links an LCD panel with a driver or occupant-monitoring camera to track the suns casted shadow on the drivers face.

The system uses artificial intelligence to locate the driver within the image from the driver-facing camera. It also utilizes AI to determine the landmarks on the face including where the eyes, nose and mouth are located so it can identify shadows on the face.

The algorithm analyzes the drivers view, darkening only the section of the display through which light hits the drivers eyes. The rest of the display remains transparent, no longer obscuring a large section of the drivers field of vision.

We discovered early in the development that users adjust their traditional sun visors to always cast a shadow on their own eyes, said Jason Zink, technical expert for Bosch in North America and one of the co-creators of the Virtual Visor. This realization was profound in helping simplify the product concept and fuel the design of the technology.

This use of liquid crystal technology to block a specific light source decreases dangerous sun glare, driver discomfort and accident risk; it also increases driver visibility, comfort and safety.

The World Economic Forum and Deepen AI unveiled Safety Pool, a global incentive-based brokerage of shared driving scenarios and safety data for safe autonomous driving systems.

Aptiv was one of the first publicly announced members of the initiative.

"At Aptiv, we believe that our industry makes progress by sharing, especially when it comes to safety. We are proud to be part of the World Economic Forum's Safety Pool, and we are confident that with continued collaboration, we will deliver the safer and more accessible mobility solutions our communities deserve," said Karl Iagnemma, Aptivs president of autonomous mobility.

Safety Pool is gathering a vast and diverse set of driving scenarios and safety data from the major industry players developing ADAS systems and autonomous driving technology, it was announced at CES 2020. Data will be accessible by the members while an incentive scheme ensures the right value is taken and given by every Safety Pool participant, regardless of their size, level of funding, or years of operations.

According to Deepen, WEF and the first publicly announced Safety Pool pioneering members, sharing this data on such a large scale will generate tremendous positive externalities for the whole industry.

Each company developing ADAS systems and autonomous driving technology will have the chance to tap into a massive, common and shared database of driving scenarios on which to train and validate their machine learning models. In this way, the overall safety of operations will drastically increase, accelerating time to deployment.

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BlackBerry combines AI and machine learning to create connected fleet security solution - Fleet Owner

Dell's Latitude 9510 shakes up corporate laptops with 5G, machine learning, and thin bezels | PCWorld ');consent.ads.queue.push(function(){ try { IDG.GPT.addDisplayedAd("gpt-superstitial", "true"); $('#gpt-superstitial').responsiveAd({screenSize:'971 1115', scriptTags: []}); IDG.GPT.log("Creating ad: gpt-superstitial [971 1115]"); }catch (exception) {console.log("Error with IDG.GPT: " + exception);} }); This business workhorse has a lot to like.

Dell Latitude 9510 hands-on: The three best features

Dell's Latitude 9510 has three features we especially love: The integrated 5G, the Dell Optimizer Utility that tunes the laptop to your preferences, and the thin bezels around the huge display.

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The Dell Latitude 9510 is a new breed of corporate laptop. Inspired in part by the companys powerful and much-loved Dell XPS 15, its the first model in an ultra-premium business line packed with the best of the best, tuned for business users.

Announced January 2 and unveiled Monday at CES in Las Vegas, the Latitude 9510 weighs just 3.2 pounds and promises up to 30 hours of battery life.PCWorld had a chance to delve into the guts of the Latitude 9510, learning more about whats in it and how it was built. Here are the coolest things we saw:

The Dell Latitude 9510 is shown disassembled, with (top, left to right) the magnesium bottom panel, the aluminum display lid, and the internals; and (bottom) the array of ports, speaker chambers, keyboard, and other small parts.

The thin bezels around the 15.6-inch screen (see top of story) are the biggest hint that the Latitude 9510 took inspiration from its cousin, the XPS 15. Despite the size of the screen, the Latitude 9510 is amazingly compact. And yet, Dell managed to squeeze in a camera above the displaythanks to a teeny, tiny sliver of a module.

A closer look at the motherboard of the Dell Latitude 9510 shows the 52Wh battery and the areas around the periphery where Dell put the 5G antennas.

The Latitude 9510 is one of the first laptops weve seen with integrated 5G networking. The challenge of 5G in laptops is integrating all the antennas you need within a metal chassis thats decidedly radio-unfriendly.

Dell made some careful choices, arraying the antennas around the edges of the laptop and inserting plastic pieces strategically to improve reception. Two of the antennas, for instance, are placed underneath the plastic speaker components and plastic speaker grille.

The Dell Latitude 9510 incorporated plastic speaker panels to allow reception for the 5G antennas underneath.

Not ready for 5G? No worries. Dell also offers the Latitude 9510 with Wi-Fi 6, the latest wireless networking standard.

You are constantly asking your PC to do things for you, usually the same things, over and over. Dells Optimizer software, which debuts on the Latitude 9510, analyzes your usage patterns and tries to save you time with routine tasks.

For instance, the Express SignIn feature logs you in faster. The ExpressResponse feature learns which applications you fire up first and loads them faster for you. Express Charge watches your battery usage and will adjust settings to save bettery, or step in with faster charging when you need some juice, pronto. Intelligent Audio will try to block out background noise so you can videoconference with less distraction.

The Dell Latitude 9510s advanced features and great looks should elevate corporate laptops in performance as well as style.It will come in clamshell and 2-in-1 versions, and is due to ship March 26. Pricing is not yet available.

Melissa Riofrio spent her formative journalistic years reviewing some of the biggest iron at PCWorld--desktops, laptops, storage, printers. As PCWorld's Executive Editor she leads PCWorlds content direction and covers productivity laptops and Chromebooks.

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Dell's Latitude 9510 shakes up corporate laptops with 5G, machine learning, and thin bezels - PCWorld

MIT boffins have devised a software-based tool for predicting how processors will perform when executing code for specific applications.

In three papers released over the past seven months, ten computer scientists describe Ithemal (Instruction THroughput Estimator using MAchine Learning), a tool for predicting the number processor clock cycles necessary to execute an instruction sequence when looped in steady state, and include a supporting benchmark and algorithm.

Throughput stats matter to compiler designers and performance engineers, but it isn't practical to make such measurements on-demand, according to MIT computer scientists Saman Amarasinghe, Eric Atkinson, Ajay Brahmakshatriya, Michael Carbin, Yishen Chen, Charith Mendis, Yewen Pu, Alex Renda, Ondrej Sykora, and Cambridge Yang.

So most systems rely on analytical models for their predictions. LLVM offers a command-line tool called llvm-mca that can presents a model for throughput estimation, and Intel offers a closed-source machine code analyzer called IACA (Intel Architecture Code Analyzer), which takes advantage of the company's internal knowledge about its processors.

Michael Carbin, a co-author of the research and an assistant professor and AI researcher at MIT, told the MIT News Service on Monday that performance model design is something of a black art, made more difficult by Intel's omission of certain proprietary details from its processor documentation.

The Ithemal paper [PDF], presented in June at the International Conference on Machine Learning, explains that these hand-crafted models tend to be an order of magnitude faster than measuring basic block throughput sequences of instructions without branches or jumps. But building these models is a tedious, manual process that's prone to errors, particularly when processor details aren't entirely disclosed.

Using a neural network, Ithemal can learn to predict throughout using a set of labelled data. It relies on what the researchers describe as "a hierarchical multiscale recurrent neural network" to create its prediction model.

"We show that Ithemals learned model is significantly more accurate than the analytical models, dropping the mean absolute percent error by more than 50 per cent across all benchmarks, while still delivering fast estimation speeds," the paper explains.

A second paper presented in November at the IEEE International Symposium on Workload Characterization, "BHive: A Benchmark Suite and Measurement Framework for Validating x86-64 Basic Block Performance Models," describes the BHive benchmark for evaluating Ithemal and competing models, IACAm llvm-mca, and OSACA (Open Source Architecture Code Analyzer). It found Ithemal outperformed other models except on vectorized basic blocks.

And in December at the NeurIPS conference, the boffins presented a third paper titled Compiler Auto-Vectorization with Imitation Learning that describes a way to automatically generate compiler optimizations in a way that outperforms LLVMs SLP vectorizer.

The academics argue that their work shows the value of machine learning in the context of performance analysis.

"Ithemal demonstrates that future compilation and performance engineering tools can be augmented with datadriven approaches to improve their performance and portability, while minimizing developer effort," the paper concludes.

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Finally, a good use for AI: Machine-learning tool guesstimates how well your code will run on a CPU core - The Register

The chassis the new model will boast has been improved as well. The new G80's rear-drive platform is lower, which allows for more interior space and better handling, and crucially, isn't shared with any lowly Hyundais or Kias. Nineteen percent of the G80's body is now aluminum, resulting in a car that's 243 pounds lighter than the model it replaces. It's apparently quieter, too, thanks to improved door seals, new engine compartment sound insulation, and sound-reducing wheels. Electronically Controlled Suspension with Road Preview uses the front camera to anticipate bumps, potholes, and rough surfaces just like the Audi A8.

Its luxurious interior is equipped with a 12.3-inch digital instrument cluster and an ultra-wide 14.5-inch infotainment screen with Apple CarPlay, Android Auto, and the ability to receive over-the-air navigation updates. Genesis' latest active safety and assisted driving systems are all accounted for as well, including Highway Driving Assist that can now change lanes at the flick of the turn signal and Smart Cruise Control with Machine Learning that intelligently adapts to its owner's driving style.

So, presumably, if you drive like an idiot, your G80 will drive like one too. Although we don't think local law enforcement will take too kindly to that excuse when they catch your Genesis autonomously cutting somebody off a little too aggressively.

Official pricing has yet to be announced but we expect it to start somewhere in the $50,000 ballpark just like the BMW 5 Series and Mercedes-Benz E-Class.

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Key Dynamics of Machine Learning and Intelligent Automation in Contemporary Market

Automation has generated great buzz across many industries globally. And as more and more organizations are shifting their focus to digital transformation and innovation, they are adopting automation technologies to increase their business efficiency by reducing human errors. Moreover, when mixed with machine learning capabilities, automation tends to serve with an attractive proposition to an organization and its services across the market. The combination is popularly known as intelligent automation.

Intelligent automation as a blend of innovative AI capabilities and automation is extensively applicable to the more sophisticated end of the automation-aided workflow continuum. The potential benefits of ML-enabled intelligent automation capabilities, in terms of additional insights and financial impact, can be greatly augmented.

Today, to stay relevant, competitive, and efficient, organizations need to contemplate their business processes with the addition of machine learning and automation. Together they can provide great advantages to organisations. Being substantially different technologies, together they have the ability to evaluate the process and make cognitive decisions.

To make your automation process more dynamic, the successful integration of machine learning is key. Moreover, intelligent automation as an amalgamation is a two-way improvement strategy, where automation tools are exposed to huge amounts of data, and machine learning can be leveraged to determine how robots can be programmed to store and filter useful data.

Individually, both technologies are very fast-growing markets. The global machine learning market size is expected to reach US$96.7 billion by 2025, according to market reports, expanding at a CAGR of 43.8% from 2019 to 2025. Also, the global automation market size is expected to reach US$368.4 billion in 2025, from US$190.2 billion in 2017 growing at a CAGR of 8.8% from 2018 to 2025.

Moreover, the intelligent process automation market was valued at US$6.25 billion in 2017 and is projected to reach US$13.75 billion by 2023, at a CAGR of 12.9% from 2018 to 2023.

Organizations are becoming more open today, allowing their products and technologies to be better integrated and share data and this trend has given rise to innovative technology like intelligent automation.

With the incorporation of machine learning capabilities, intelligent automation possesses the ability to empower humans with advanced smart technologies and agile processes to enable fast and informative decisions. It also caters to a wide array of business operations with key benefits including increasing process efficiency and customer experience, better optimization of back-office operations, reduction in costs, and minimizing risk factors. Intelligent automation also optimizes the workforce productivity with better and effective monitoring and fraud detection. It also enables a more comprehensive product and service innovation.

Being an undeniable catalyst to progress, moreover, intelligent automation is no threat to human jobs. Rather its incorporation in a collaborative manner can help employees reshape their skills and creatives. Intelligent automation has the core benefit to extensively improve and digitalize business processes along with human judgment.

Therefore, the time has arrived when companies should consider investing strategically in automation and ML capabilities in order to understand and meet the expectations of customers which eventually leads to improved productivity and low-cost scalability.

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Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

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Protesters and police in Seattle's Capitol Hill neighborhood. (Getty Images)

When incidents of police brutality occur, typically departments enact police reforms and fire bad cops, but machine learning could potentially predict when a police officer may go over the line.

Rayid Ghani is a professor at Carnegie Mellon and joined Seattles Morning News to discuss using machine learning in police reform. Hes working on tech that could predict not only which cops might not be suited to be cops, but which cops might be best for a particular call.

AI and technology and machine learning, and all these buzzwords, theyre not able to to fix racism or bad policing, they are a small but important tool that we can use to help, Ghani said. I was looking at the systems called early intervention systems that a lot of large police departments have. Theyre supposed to raise alerts, raise flags when a police officer is at risk of doing something that they shouldnt be doing, like excessive use of force.

What level of privacy can we expect online?

What we found when looking at data from several police departments is that these existing systems were mostly ineffective, he added. If theyve done three things in the last three months that raised the flag, well thats great. But at the same time, its not an early intervention. Its a late intervention.

So they built a system that works to potentially identify high risk officers before an incident happens, but how exactly do you predict how somebody is going to behave?

We build a predictive system that would identify high risk officers We took everything we know about a police officer from their HR data, from their dispatch history, from who they arrested , their internal affairs, the complaints that are coming against them, the investigations that have happened, Ghani said.

Can the medical system and patients afford coronavirus-related costs?

What we found were some of the obvious predictors were what you think is their historical behavior. But some of the other non-obvious ones were things like repeated dispatches to suicide attempts or repeated dispatches to domestic abuse cases, especially involving kids. Those types of dispatches put officers at high risk for the near future.

While this might suggest that officers who regularly dealt with traumatic dispatches might be the ones who are higher risk, the data doesnt explain why, it just identifies possibilities.

It doesnt necessarily allow us to figure out the why, it allows us to narrow down which officers are high risk, Ghani said. Lets say a call comes in to dispatch and the nearest officer is two minutes away, but is high risk of one of these types of incidents. The next nearest officer is maybe four minutes away and is not high risk. If this dispatch is not time critical for the two minutes extra it would take, could you dispatch the second officer?

So if an officer has been sent to a multiple child abuse cases in a row, it makes more sense to assign somebody else the next time.

Thats right, Ghani said. Thats what that were finding is they become high risk It looks like its a stress indicator or a trauma indicator, and they might need a cool-off period, they might need counseling.

But in this case, the useful thing to think about also is that they havent done anything yet, he added. This is preventative, this is proactive. And so the intervention is not punitive. You dont fire them. You give them the tools that they need.

Listen to Seattles Morning News weekday mornings from 5 9 a.m. on KIRO Radio, 97.3 FM. Subscribe to thepodcast here.

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Even in a world that is as currently far off the rails as this one is, were going to go out on a limb and say that this machine learning, servo-powered prayer bot is going to be the strangest thing you see today. Were happy to be wrong about that, though, and if we are, please send links.

The Prayer, as [Diemut Strebe]s work is called, may look strange, but its another in a string of pieces by various artists that explores just what it means to be human at a time when machines are blurring the line between them and us. The hardware is straightforward: a silicone rubber representation of a human nasopharyngeal cavity, servos for moving the lips, and a speaker to create the vocals. Those are generated by a machine-learning algorithm that was trained against the sacred texts of many of the worlds major religions, including the Christian Bible, the Koran, the Baghavad Gita, Taoist texts, and the Book of Mormon. The algorithm analyzes the structure of sacred verses and recreates random prayers and hymns using Amazon Polly that sound a lot like the real thing. That the lips move in synchrony with the ersatz devotions only adds to the otherworldliness of the piece. Watch it in action below.

Weve featured several AI-based projects that poke at some interesting questions. This kinetic sculpture that uses machine learning to achieve balance comes to mind, while AI has even been employed in the search for spirits from the other side.

[Via Twitter, but we recommend abstaining from the comments, for obvious reasons.]

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Silicone And AI Power This Prayerful Robotic Intercessor - Hackaday