Category Archives: Machine Learning

Advances in innovation to capture and process a lot of data have left us suffocating in information. This makes it hard to extricate insights from data at the rate we get it. This is the place where machine learning offers some benefit to a digital business.

We need strategies to improve machine learning performance all the more effectively. Since, supposing that we put forth efforts in the wrong direction, we cant get a lot of progress and burn through a lot of time. Then, we need to get a few expectations toward the path we picked, for instance, how much precision can be improved.

There are by and large two kinds of organizations that participate in machine learning: those that build applications with a trained ML model inside as their core business proposition and those that apply ML to upgrade existing business work processes. In the latter case, articulating the issue will be the underlying challenge. Diminishing the expense or increasing income should be limited to the moment that it gets solvable by gaining the right data.

For example, if you need to minimize the churn rate, data may assist you with detecting clients with a high fly risk by analyzing their activities on a website, a SaaS application, or even online media. In spite of the fact that you can depend on traditional metrics and make suppositions, the algorithm may unwind shrouded dependencies between the data in clients profiles and the probability to leave.

Resource management has become a significant part of a data scientists duties. For instance, it is a challenge having a GPU worker on-prem for a group of five data scientists. A lot of time is spent sorting out some way to share those GPUs simply and effectively. Allocation of compute resources for machine learning can be a major agony, and takes time away from doing data science tasks.

Data science is an expansive field of practices pointed toward removing significant insights from data in any structure. Furthermore, utilizing data science in decision-making is a better method to stay away from bias. Nonetheless, that might be trickier than you might suspect. Indeed, even Google has as of late fallen into a trap of indicating more esteemed jobs to men in their ads than to women. Clearly, it isnt so much that Google data scientists are sexist, but instead the data that the algorithm utilizes is one-sided on the grounds that it was gathered from our interactions on the web.

Machine learning is compute-intensive. A scalable machine learning foundation should be compute agnostic. Joining public clouds, private clouds, and on-premise resources offers flexibility and agility as far as running AI workloads. Since the kinds of workloads shift significantly between AI workloads, companies that construct a hybrid cloud infrastructure can dispense assets all the more deftly in custom sizes. You can bring down CapEx expenditure with public cloud, and offer the scalability required for times of high compute demands. In companies with strict security demands, the expansion of private cloud is essential, and can bring down OpEx over the long-term. Hybrid cloud encourages you to accomplish the control and flexibility necessary to improve planning of resources.

A large portion of the models are created on a static subset of information, and they capture the conditions of the time frame when the data was gathered. When you have a model or various them deployed, they become dated over time and give less exact expectations. Contingent upon how effectively the patterns in your business climate change, you should pretty much regularly replace models or retrain them

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Improve Machine Learning Performance with These 5 Strategies - Analytics Insight

HOGWILD! Wild as it sounds, the paper that goes by the same name was supposed to be an art project by Christopher Re, an associate professor at Stanford AI Lab, and his peers. Little did they know that the paper would change the way we do machine learning. Ten years later, it even bagged the prestigious Test of Time award at the latest NeurIPS conference.

To identify the most impactful paper in the past decade, the conference organisers selected a list of 12 papers published at NeurIPS over the years NeurIPS 2009, NeurIPS 2010, NeurIPS 2011 with the highest numbers of citations since their publication. They also collected data about the recent citations counts for each of these papers by aggregating citations that these papers received in the past two years at NeurIPS, ICML and ICLR. The organisers then asked the whole senior program committee with 64 SACs to vote on up to three of these papers to help in picking an impactful paper.

Most of the machine learning is about finding the right kind of variables for converging towards reasonable predictions. Hogwild! is a method that helps in finding those variables very efficiently. The reason it had such a crazy name, to begin with, was it was intentionally a crazy idea, said Re in an interview for Stanford AI.

With its small memory footprint, robustness against noise, and rapid learning rates, Stochastic Gradient Descent (SGD) has proved to be well suited to data-intensive machine learning tasks. However, SGDs scalability is limited by its inherently sequential nature; it is difficult to parallelise. A decade ago, when the hardware was still playing catch up with the algorithms, the key objective for scalable data analysis, on vast data, is to minimise the overhead caused due to locking. Back then, when parallelisation of SGD was proposed, there was no way around memory locking, which deteriorated the performance. Memory locking was essential to reduce latency for between processes.

Re and his colleagues demonstrated that this work aims to show using novel theoretical analysis, algorithms, and implementation that stochastic gradient descent can be implemented without any locking.

In Hogwild!, the authors made the processors have equal access to shared memory and were able to update individual components of memory at will. The risk here is that a lock-free scheme can fail as processors could overwrite each others progress. However, when the data access is sparse, meaning that individual SGD steps only to modify a small part of the decision variable, we show that memory overwrites are rare and that they introduce barely any error into the computation when they do occur, explained the authors.

When asked about the weird exclamation point at the end of the already weird name I thought the phrase going hog-wild was hysterical to describe what we were trying. So I thought an exclamation point would just make it better, quipped Re.

In spite of being honoured with being a catalyst behind driving ML revolution, Re believes that this change would have happened with or without their paper. What really stands out, according to him, is that an odd-ball, goofy sounding research is recognised even after a decade. This is a testimony to an old adage there is no such thing as a bad idea!

Find the original paper here.

Here are the test of time award winners in the past:

2017: Random Features for Large-Scale Kernel Machines by Ali Rahimi and Ben Recht

2018: The Trade-Offs of Large Scale Learning by Lon Bottou

2019: Dual Averaging Method for Regularized Stochastic Learning and Online Optimisation by Lin Xiao

I have a master's degree in Robotics and I write about machine learning advancements.email:ram.sagar@analyticsindiamag.com

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How A Crazy Idea Changed The Way We Do Machine Learning: Test Of Time Award Winner - Analytics India Magazine

Anyone whos ever been on an earnings call knows company executives already tend to look at the world through rose-colored glasses, but a new study by economics and machine learning researchers says thats getting worse, thanks to machine learning. The analysis found that companies are adapting their language in forecasts, SEC regulatory filings, and earnings calls due to the proliferation of AI used to analyze and derive signals from the words they use. In other words: Businesses are beginning to change the way they talk because they know machines are listening.

Forms of natural language processing are used to parse and process text in the financial documents companies are required to submit to the SEC. Machine learning tools are then able to do things like summarize text or determine whether language used is positive, neutral, or negative. Signals these tools provide are used to inform the decisions advisors, analysts, and investors make. Machine downloads are associated with faster trading after an SEC filing is posted.

This trend has implications for the financial industry and economy, as more companies shift their language in an attempt to influence machine learning reports. A paper detailing the analysis, originally published in October by researchers from Columbia University and Georgia State Universitys J. Mack Robinson College of Business, was highlighted in this months National Bureau of Economic Research (NBER) digest. Lead author Sean Cao studies how deep learning can be applied to corporate accounting and disclosure data.

More and more companies realize that the target audience of their mandatory and voluntary disclosures no longer consists of just human analysts and investors. A substantial amount of buying and selling of shares [is] triggered by recommendations made by robots and algorithms which process information with machine learning tools and natural language processing kits, the paper reads. Anecdotal evidence suggests that executives have become aware that their speech patterns and emotions, evaluated by human or software, impact their assessment by investors and analysts.

The researchers examined nearly 360,000 SEC filings between 2003 and 2016. Over that time period, regulatory filing downloads from the SECs Electronic Data Gathering, Analysis, and Retrieval (EDGAR) tool increased from roughly 360,000 filing downloads to 165 million, climbing from 39% of all downloads in 2003 to 78% in 2016.

A 2011 study concluded that the majority of words identified as negative by a Harvard dictionary arent actually considered negative in a financial context. That study also included lists of negative words used in 10-K filings. After the release of that list,researchers found high machine download companies began to change their behavior and use fewer negative words.

Generally, the stock market responds more positively to disclosures with fewer negative words or strong modal words.

As more and more investors use AI tools such as natural language processing and sentiment analyses, we hypothesize that companies adjust the way they talk in order to communicate effectively and predictably, the paper reads. If managers are aware that their disclosure documents could be parsed by machines, then they should also expect that their machine readers may also be using voice analyzers to extract signals from vocal patterns and emotions contained in managers speeches.

A study released earlier this year by Yale University researchers used machine learning to analyze startup pitch videos and found that positive (i.e., passionate, warm) pitches increase funding probability. And another study from earlier this year (by Crane, Crotty, and Umar) showed hedge funds that use machines to automate downloads of corporate filings perform better than those that do not.

In other applications at the locus of AI and investor decisions, last year InReach Ventures launched a $60 million fund that uses AI as part of its process for evaluating startups.

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How machines are changing the way companies talk - VentureBeat

Its that time of year again! Were continuing our longrunning tradition of publishing a list of predictions fromAI experts who know whats happening on the ground, in the research labs, and at the boardroom tables.

Without further ado, lets dive in and see what the pros think will happen in the wake of 2020.

Dr. Arash Rahnama, Head of Applied AI Research at Modzy:

Just as advances in AI systems are racing forward, so too are opportunities and abilities for adversaries to trick AI models into making wrong predictions. Deep neural networks are vulnerable to subtle adversarial perturbations applied to their inputs adversarial AI which are imperceptible to the human eye. These attacks pose a great risk to the successful deployment of AI models in mission critical environments. At the rate were going, there will be a major AI security incident in 2021 unless organizations begin to adopt proactive adversarial defenses into their AI security posture.

2021 will be the year of explainability. As organization integrate AI, explainability will become a major part of ML pipelines to establish trust for the users. Understanding how machine learning reasons against real-world data helps build trust between people and models. Without understanding outputs and decision processes, there will never be true confidence in AI-enabled decision-making. Explainability will be critical in moving forward into the next phase of AI adoption.

The combination of explainability, and new training approaches initially designed to deal with adversarial attacks, will lead to a revolution in the field. Explainability can help understand what data influenced a models prediction and how to understand bias information which can then be used to train robust models that are more trusted, reliable and hardened against attacks. This tactical knowledge of how a model operates, will help create better model quality and security as a whole. AI scientists will re-define model performance to encompass not only prediction accuracy but issues such as lack of bias, robustness and strong generalizability to unpredicted environmental changes.

Dr. Kim Duffy, Life Science Product Manager at Vicon.

Forming predictions for artificial intelligence (AI) and machine learning (ML) is particularly difficult to do while only looking one year into the future. For example, in clinical gait analysis, which looks at a patients lower limb movement to identify underlying problems that result in difficulties walking and running, methodologies like AI and ML are very much in their infancy. This is something Vicon highlights in our recent life sciences report, A deeper understanding of human movement. To utilize these methodologies and see true benefits and advancements for clinical gait will take several years. Effective AI and ML requires a mass amount of data to effectively train trends and pattern identifications using the appropriate algorithms.

For 2021, however, we may see more clinicians, biomechanists, and researchers adopting these approaches during data analysis. Over the last few years, we have seen more literature presenting AI and ML work in gait. I believe this will continue into 2021, with more collaborations occurring between clinical and research groups to develop machine learning algorithms that facilitate automatic interpretations of gait data. Ultimately, these algorithms may help propose interventions in the clinical space sooner.

It is unlikely we will see the true benefits and effects of machine learning in 2021. Instead, well see more adoption and consideration of this approach when processing gait data. For example, the presidents of Gait and Postures affiliate society provided a perspective on the clinical impact of instrumented motion analysis in their latest issue, where they emphasized the need to use methods like ML on big-data in order to create better evidence of the efficiency of instrumented gait analysis. This would also provide better understanding and less subjectivity in clinical decision-making based on instrumented gait analysis. Were also seeing more credible endorsements of AI/ML such as the Gait and Clinical Movement Analysis Society which will also encourage further adoption by the clinical community moving forward.

Joe Petro, CTO of Nuance Communications:

In 2021, we will continue to see AI come down from the hype cycle, and the promise, claims, and aspirations of AI solutions will increasingly need to be backed up by demonstrable progress and measurable outcomes. As a result, we will see organizations shift to focus more on specific problem solving and creating solutions that deliver real outcomes that translate into tangible ROI not gimmicks or building technology for technologys sake. Those companies that have a deep understanding of the complexities and challenges their customers are looking to solve will maintain the advantage in the field, and this will affect not only how technology companies invest their R&D dollars, but also how technologists approach their career paths and educational pursuits.

With AI permeating nearly every aspect of technology, there will be an increased focus on ethics and deeply understanding the implications of AI in producing unintentional consequential bias. Consumers will become more aware of their digital footprint, and how their personal data is being leveraged across systems, industries, and the brands they interact with, which means companies partnering with AI vendors will increase the rigor and scrutiny around how their customers data is being used, and whether or not it is being monetized by third parties.

Dr. Max Versace, CEO and Co-Founder, Neurala:

Well see AI be deployed in the form of inexpensive and lightweight hardware. Its no secret that 2020 was a tumultuous year, and the economic outlook is such that capital intensive, complex solutions will be sidestepped for lighter-weight, perhaps software-only, less expensive solutions. This will allow manufacturers to realize ROIs in the short term without massive up-front investments. It will also give them the flexibility needed to respond to fluctuations the supply chain and customer demands something that weve seen play out on a larger scale throughout the pandemic.

Humans will turn their attention to why AI makes the decisions it makes. When we think about the explainability of AI, it has often been talked about in the context of bias and other ethical challenges. But as AI comes of age and gets more precise, reliable and finds more applications in real-world scenarios, well see people start to question the why? The reason? Trust: humans are reluctant to give power to automatic systems they do not fully understand. For instance, in manufacturing settings, AI will need to not only be accurate, but also explain why a product was classified as normal or defective, so that human operators can develop confidence and trust in the system and let it do its job.

Another year, another set of predictions. You can see how our experts did last year by clicking here. You can see how our experts did this year by building a time machine and traveling to the future. Happy Holidays!

Published December 28, 2020 07:00 UTC

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Neurals AI predictions for 2021 - The Next Web

New Jersey, United States,- The report, titled Machine Learning Market Size By Types, Applications, Segmentation, and Growth Global Analysis and Forecast to 2019-2027 first introduced the fundamentals of Machine Learning: definitions, classifications, applications and market overview; Product specifications; Production method; Cost Structures, Raw Materials, etc. The report takes into account the impact of the novel COVID-19 pandemic on the Machine Learning market and also provides an assessment of the market definition as well as the identification of the top key manufacturers which are analyzed in-depth as opposed to the competitive landscape. In terms of Price, Sales, Capacity, Import, Export, Machine Learning Market Size, Consumption, Gross, Gross Margin, Sales, and Market Share. Quantitative analysis of the Machine Learning industry from 2019 to 2027 by region, type, application, and consumption rating by region.

Impact of COVID-19 on Machine Learning Market: The Coronavirus Recession is an economic recession that will hit the global economy in 2020 due to the COVID-19 pandemic. The pandemic could affect three main aspects of the global economy: manufacturing, supply chain, business and financial markets. The report offers a full version of the Machine Learning Market, outlining the impact of COVID-19 and the changes expected on the future prospects of the industry, taking into account political, economic, social, and technological parameters.

Request Sample Copy of this Report @ Machine Learning Market Size

In market segmentation by manufacturers, the report covers the following companies-

How to overcome obstacles for the septennial 2020-2027 using the Global Machine Learning market report?

Presently, going to the main part-outside elements. Porters five powers are the main components to be thought of while moving into new business markets. The customers get the opportunity to use the approaches to plan the field-tested strategies without any preparation for the impending monetary years.

We have faith in our services and the data we share with our esteemed customers. In this way, we have done long periods of examination and top to bottom investigation of the Global Machine Learning market to give out profound bits of knowledge about the Global Machine Learning market. Along these lines, the customers are enabled with the instruments of data (as far as raw numbers are concerned).

The graphs, diagrams and infographics are utilized to speak out about the market drifts that have formed the market. Past patterns uncover the market turbulences and the final results on the markets. Then again, the investigation of latest things uncovered the ways, the organizations must take for shaping themselves to line up with the market.

Machine Learning Market: Regional analysis includes:

?Asia-Pacific(Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia)?Europe(Turkey, Germany, Russia UK, Italy, France, etc.)?North America(the United States, Mexico, and Canada.)?South America(Brazil etc.)?The Middle East and Africa(GCC Countries and Egypt.)

The report includes Competitors Landscape:

? Major trends and growth projections by region and country? Key winning strategies followed by the competitors? Who are the key competitors in this industry?? What shall be the potential of this industry over the forecast tenure?? What are the factors propelling the demand for the Machine Learning Industry?? What are the opportunities that shall aid in the significant proliferation of market growth?? What are the regional and country wise regulations that shall either hamper or boost the demand for Machine Learning Industry?? How has the covid-19 impacted the growth of the market?? Has the supply chain disruption caused changes in the entire value chain?

The report also covers the trade scenario,Porters Analysis,PESTLE analysis, value chain analysis, company market share, segmental analysis.

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Market Research Blogs is a leading Global Research and Consulting firm servicing over 5000+ customers. Market Research Blogs provides advanced analytical research solutions while offering information enriched research studies. We offer insight into strategic and growth analyses, Data necessary to achieve corporate goals, and critical revenue decisions.

Our 250 Analysts and SMEs offer a high level of expertise in data collection and governance use industrial techniques to collect and analyze data on more than 15,000 high impact and niche markets. Our analysts are trained to combine modern data collection techniques, superior research methodology, expertise, and years of collective experience to produce informative and accurate research.

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Machine Learning Market Size 2020 by Top Key Players, Global Trend, Types, Applications, Regional Demand, Forecast to 2027 - LionLowdown

Toward the tail end of pre-pandemic 2019, Mayo Clinic Chief Information Officer Cris Ross stood on a stage in California and declared, "This artificial intelligence stuff is real."

Indeed, while some may argue that AI and machine learning might have been harnessed better during the early days of COVID-19, and while the risk of algorithmic bias is very real, there's little question that artificial intelligence, evolving and maturing by the day for an array of use cases across healthcare.

Here are the most-read stories about AI during this most unusual year.

UK to use AI for COVID-19 vaccine side effects. On a day when vaccines, developed in record time, first begin to be administered in the U.S., it's worth remembering AI's crucial role in helping the world get to this hopefully pivotal moment.

AI algorithm IDs abnormal chest X-rays from COVID-19 patients. Machine learning has been a hugely valuable diagnostic tool as well, as illustrated by this story about a tool from cognitive computing vendor behold.ai that promises 'instant triage" based on lung scans offering faster diagnosis of COVID-19 patients and helping with resource allocation.

How AI use cases are evolving in the time of COVID-19. In a HIMSS20 Digital presentation, leaders from Google Cloud, Nuance and Health Data Analytics Institute shared perspective on how AI and automation were being deployed for pandemic response from the hunt for therapeutics and vaccines to analytics to optimize revenue cycle strategies.

Microsoft launches major $40M AI for Health initiative. The company said the the five-year AI for Health (part of its $165 million AI for Good initiative) will help healthcare organizations around the world deploy with leading edge technologies in the service of three key areas: accelerating medical research, improving worldwide understanding to protect against global health crises such as COVID-19 and reducing health inequity.

How AI and machine learning are transforming clinical decision support. "Todays digital tools only scratch the surface," said Mayo Clinic Platform President Dr. John Halamka. "Incorporating newly developed algorithms that take advantage of machine learning, neural networks, and a variety of other types of artificial intelligence can help address many of the shortcomings of human intelligence."

Clinical AI vendor Jvion unveils COVID Community Vulnerability Map. In the very early days of the pandemic, clinical AI company Jvion launched this intereactive map, which tracks the social determinants of health, helping identify populations down to the census-block level that are at risk for severe outcomes.

AI bias may worsen COVID-19 health disparities for people of color. An article in the Journal of the American Medical Informatics Association asserts that biased data models could further the disproportionate impact the COVID-19 pandemic is already having on people of color. "If not properly addressed, propagating these biases under the mantle of AI has the potential to exaggerate the health disparities faced by minority populations already bearing the highest disease burden," said researchers.

The origins of AI in healthcare, and where it can help the industry now. "The intersection of medicine and AI is really not a new concept," said Dr. Taha Kass-Hout, director of machine learning and chief medical officer at Amazon Web Services. (There were limited chatbots and other clinical applications as far back as the mid-60s.) But over the past few years, it has become ubiquitous across the healthcare ecosystem. "Today, if youre looking at PubMed, it cites over 12,000 publications with deep learning, over 50,000 machine learning," he said.

AI, telehealth could help address hospital workforce challenges. "Labor is the largest single cost for most hospitals, and the workforce is essential to the critical mission of providing life-saving care," noted a January American Hospital Association report on the administrative, financial, operational and clinical uses of artificial intelligence. "Although there are challenges, there also are opportunities to improve care, motivate and re-skill staff, and modernize processes and business models that reflect the shift toward providing the right care, at the right time, in the right setting."

AI is helping reinvent CDS, unlock COVID-19 insights at Mayo Clinic. In a HIMSS20 presentation, JohnHalamka shared some of the most promising recent clinical decision support advances at the Minnesota health system and described how they're informing treatment decisions for an array of different specialties and helping shape its understanding of COVID-19. "Imagine the power [of] an AI algorithm if you could make available every pathology slide that has ever been created in the history of the Mayo Clinic," he said. "That's something we're certainly working on."

Twitter:@MikeMiliardHITNEmail the writer:mike.miliard@himssmedia.comHealthcare IT News is a HIMSS publication.

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Top 10 AI and machine learning stories of 2020 - Healthcare IT News