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- How researchers are mapping the future of quantum computing, using the tech of today – GeekWire
- Colorado makes a bid for quantum computing hardware plant that would bring more than 700 jobs – The Denver Post
- The Worldwide Quantum Computing Industry is Expected to Reach $1.7 Billion by 2026 – PRNewswire
- bp Joins the IBM Quantum Network to Advance Use of Quantum Computing in Energy – HPCwire
- The Fourth Industrial Revolution AI, Quantum, and IoT Impacts on Cybersecurity – Security Boulevard
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Category Archives: Machine Learning
Safe Internet: WOT uses machine learning and crowdsourcing to protect your phone and tablet – PhoneArena
Advertorial by WOT: the opinions expressed in this story may not reflect the positions of PhoneArena!
WOT is available in the form of an Android app or extension for Firefox, Opera, Chrome, and even the Samsung browser. This means you can use it on absolutely any Android device in your household, plus the family desktop PC.
In order to ensure its protection is always up to date, WOT utilizes a mixture of crowdsourcing, machine learning, and third party blacklists. It will analyze user behavior and compare it against databases of known scams to make sure its constantly on top of its game.
If you subscribe to premium ($2.49 per month on an annual plan), you gain access to WOTs superb Anti-Phishing shield, which will keep a lookout for clever scams. Premium users also have no limit on how many apps they can lock, and gain an auto-scanning feature, which will automatically check new Wi-Fi networks and apps for security flaws.
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Safe Internet: WOT uses machine learning and crowdsourcing to protect your phone and tablet - PhoneArena
A patient enters the hospital struggling to breathe they have COVID-19. Their healthcare team decides to admit them to the hospital. Will they be one of the fortunate ones who steadily improves and are soon discharged? Or will they end up needing mechanical ventilation?
That question may be easier to answer, thanks to a recent study from Michigan Medicine describing an algorithm to predict which patients are likely to quickly deteriorate while hospitalized.
You can see large variability in how different patients with COVID-19 do, even among close relatives with similar environments and genetic risk, says Nicholas J. Douville, M.D., Ph.D., of the Department of Anesthesiology, one of the studys lead authors. At the peak of the surge, it was very difficult for clinicians to know how to plan and allocate resources.
Combining data science and their collective experiences caring for COVID-19 patients in the intensive care unit, Douville, Milo Engoren, M.D., and their colleagues explored the potential of predictive machine learning. They looked at a set of patients with COVID-19 hospitalized during the first pandemic surge from March to May 2020 and modeled their clinical course.
The team generated an algorithm with inputs such as a patients age, whether they had underlying medical conditions and what medications they were on when entering the hospital, as well as variables that changed while hospitalized, including vital signs like blood pressure, heart rate and oxygenation ratio, among others.
Their question: which of these data points helped to best predict which patients would decompensate and require mechanical ventilator or die within 24 hours?
Of the 398 patients in their study, 93 required a ventilator or died within two weeks. The model was able to predict mechanical ventilation most accurately based upon key vital signs, including oxygen saturation ratio (SpO2/FiO2), respiratory rate, heart rate, blood pressure and blood glucose level.
The team assessed the data points of interest at 4, 8, 24 and 48 hour increments, in an attempt to identify the optimal amount of time necessary to predictand intervenebefore a patient deteriorates.
"The closer we were to the event, the higher our ability to predict, which we expected.But we were still able to predict the outcomes with good discrimination at 48 hours, giving providers time to make alterations to the patients care or to mobilize resources, says Douville.
For instance, the algorithm could quickly identify a patient on a general medical floor who would be a good candidate for transfer to the ICU, before their condition deteriorated to the point where ventilation would be more difficult.
In the long term, Douville and his colleagues hope the algorithm can be integrated into existing clinical decision support tools already used in the ICU. In the short term, the study brings to light patient characteristics that clinicians caring for patients with COVID-19 should keep in the back of their minds. The work also raises new questions about which COVID-19 therapies, such as anti-coagulants or anti-viral drugs, may or may not alter a patients clinical trajectory.
Says Douville, While many of our model features are well known to experienced clinicians, the utility of our model is that it performs a more complex calculation than the clinician could perform on the back of the envelope it also distills the overall risk to an easily interpretable value, which can be used to flag patients in a way so they are not missed.
Paper cited: Clinically Applicable Approach for Predicting Mechanical Ventilation in Patients with COVID-19, British Journal of Anaesthesia. DOI: 10.1016/j.bja.2020.11.03
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
How A Crazy Idea Changed The Way We Do Machine Learning: Test Of Time Award Winner – Analytics India Magazine
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:firstname.lastname@example.org
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.
See the article here:
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