We all hear about Artificial intelligence and Machine learning in everyday conversations, the terms are becoming increasingly popular across businesses of all sizes and in all industries. We know AI is the future, but how can it be useful to businesses today? Having encountered numerous organisations that are confused about the actual benefits of Machine Learning, AI experts agree it is necessary to outline its key applications in simple terms that most companies can relate to.

Here are the three most impactful Machine Learning applications that can transform your business today.

Machine learning can be used to automate a host of business operations, such as document processing, database analysis, system management, employee analytics, spam detection, chatbots. A lot of manual, time consuming processes can now be replaced or at least supported by off-the-shelf AI solutions. For those companies with unique requirements, looking to create or maintain a competitive advantage or otherwise prefer to retain control of the intellectual property (IP), it is worth reaching out to end-to-end service providers that can assist in planning, developing and implementing bespoke solutions to meet these business needs.

The reason why machine learning often ends up performing better than humans at a single task is that it can very quickly improve its performance through analysing vast amounts of historical data. In other words, it can learn from its own mistakes and optimise its performance very quickly and at scale. There is no ego and no hard feelings involved, simply objective analysis, enabling optimisation to be achieved with a high efficiency and effectiveness. Popular examples of optimisation with machine learning can be found around product quality control, customer satisfaction, storage, logistics, supply chain and sustainability. If you think something in your business is not running as efficiently as it could and you have access to data, machine learning may just be the right solution.

Companies are inundated with data these days. Capturing data is one thing, but navigating and extracting value from big, disconnected databases containing different types of data on different areas of your organisation adds complexity, cost, reduces efficiency and impedes effective decision making. Data management systems can help create clarity and put your data in order. You would be surprised how much valuable information can then be extracted from your data using machine learning. Typical applications in this space include churn prediction, sales forecasting, customer segmentation, personalisation, or predictive maintenance. Machine learning can teach you more about your organisation in a month than you have learned over the past year.

If you think one of the above applications might be helpful to your business now is a good time to start. As much as companies are reluctant to invest in innovation and new technologies, especially due to difficulties caused by Covid-19, it is important to recognise that the afore mentioned applications can bring a long-term benefits to your business such as cost savings, increased efficiency, improved operations and enhanced customer value. Get started and become a leader in your field thanks to the new machine learning technologies available to you today.

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Top 3 Applications Of Machine Learning To Transform Your Business - Forbes

Humanity is still a long way away from a fully artificial intelligence system. For now at least, AI is particularly good at some specialized tasks, such as classifying cats in videos. Now it has a new skill set: identifying spiral patterns in galaxies.

As with all AI skills, this one started out with categorized data. In this case, that data consisted of images of galaxies taken by the Subaru Telescope in Mauna Kea, Hawaii. The telescope is run by the National Astronomical Observatory of Japan (NAOJ), and has identified upwards of 560,000 galaxies in images it has taken.

Only a small sub-set of those half a million were manually categorized by scientists at NAOJ. The scientists then trained a deep-learning algorithm to identify galaxies that contained a spiral pattern, similar to the Milky Way. When applied to a further sub-set of the half a million galaxies (known as a test set), the algorithm accurately classified 97.5% of the galaxies surveyed as either spiral or non-spiral.

The research team then applied the algorithm to the fully 560,000 galaxies identified in the data so far. It classified about 80,000 of them as spiral, leaving about 480,000 as non-spiral galaxies. Admittedly, there may be some galaxies that are actually spirals that were not identified as such by the algorithm, as they might only be visible edge-on from Earths vantage point. In that case, even human classifiers would have a hard time correctly identifying a galaxy as a spiral.

The next step for the researchers is to train the deep learning algorithm to identify even more types and sub-types of galaxies. But to do that, they will need even more well categorized data. To help with that process, they have launched GALAXY CRUISE, a citizen science project where volunteers help to identify galaxies that are merging or colliding. They will be following in the footsteps of another effort by scientists at the Sloan Digital Sky Survey, which used Galaxy Zoo, collection of citizen science projects, to train a AI algorithm to identify spiral vs non-spiral galaxies as well. After the manual classification is done, the team hopes to upgrade the AI algorithm and analyze all half a million galaxies again to see how many of them might be colliding. Who knows, a few of those colliding galaxies might even look like cats.

Learn More:EurekaAlert: Classifying galaxies with artificial intelligencePhysics Letters B: Classifying galaxies with AI and people powerUniverse Today: Try your hand at identifying galaxiesUnite.ai: Astronomers Apply AI to Discover and Classify Galaxies

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Machine Learning Just Classified Over Half a Million Galaxies - Universe Today

Any researcher whos focused on applying machine learning to real-world problems has likely received a response like this one: The authors present a solution for an original and highly motivating problem, but it is an application and the significance seems limited for the machine-learning community.

These words are straight from a review I received for a paper I submitted to the NeurIPS (Neural Information Processing Systems) conference, a top venue for machine-learning research. Ive seen the refrain time and again in reviews of papers where my coauthors and I presented a method motivated by an application, and Ive heard similar stories from countless others.

This makes me wonder: If the community feels that aiming to solve high-impact real-world problems with machine learning is of limited significance, then what are we trying to achieve?

The goal of artificial intelligence (pdf) is to push forward the frontier of machine intelligence. In the field of machine learning, a novel development usually means a new algorithm or procedure, orin the case of deep learninga new network architecture. As others have pointed out, this hyperfocus on novel methods leads to a scourge of papers that report marginal or incremental improvements on benchmark data sets and exhibit flawed scholarship (pdf) as researchers race to top the leaderboard.

Meanwhile, many papers that describe new applications present both novel concepts and high-impact results. But even a hint of the word application seems to spoil the paper for reviewers. As a result, such research is marginalized at major conferences. Their authors only real hope is to have their papers accepted in workshops, which rarely get the same attention from the community.

This is a problem because machine learning holds great promise for advancing health, agriculture, scientific discovery, and more. The first image of a black hole was produced using machine learning. The most accurate predictions of protein structures, an important step for drug discovery, are made using machine learning. If others in the field had prioritized real-world applications, what other groundbreaking discoveries would we have made by now?

This is not a new revelation. To quote a classic paper titled Machine Learning that Matters (pdf), by NASA computer scientist Kiri Wagstaff: Much of current machine learning research has lost its connection to problems of import to the larger world of science and society. The same year that Wagstaff published her paper, a convolutional neural network called AlexNet won a high-profile competition for image recognition centered on the popular ImageNet data set, leading to an explosion of interest in deep learning. Unfortunately, the disconnect she described appears to have grown even worse since then.

Marginalizing applications research has real consequences. Benchmark data sets, such as ImageNet or COCO, have been key to advancing machine learning. They enable algorithms to train and be compared on the same data. However, these data sets contain biases that can get built into the resulting models.

More than half of the images in ImageNet (pdf) come from the US and Great Britain, for example. That imbalance leads systems to inaccurately classify images in categories that differ by geography (pdf). Popular face data sets, such as the AT&T Database of Faces, contain primarily light-skinned male subjects, which leads to systems that struggle to recognize dark-skinned and female faces.

While researchers try to outdo one another on contrived benchmarks, one in every nine people in the world is starving.

When studies on real-world applications of machine learning are excluded from the mainstream, its difficult for researchers to see the impact of their biased models, making it far less likely that they will work to solve these problems.

One reason applications research is minimized might be that others in machine learning think this work consists of simply applying methods that already exist. In reality, though, adapting machine-learning tools to specific real-world problems takes significant algorithmic and engineering work. Machine-learning researchers who fail to realize this and expect tools to work off the shelf often wind up creating ineffective models. Either they evaluate a models performance using metrics that dont translate to real-world impact, or they choose the wrong target altogether.

For example, most studies applying deep learning to echocardiogram analysis try to surpass a physicians ability to predict disease. But predicting normal heart function (pdf) would actually save cardiologists more time by identifying patients who do not need their expertise. Many studies applying machine learning to viticulture aim to optimize grape yields (pdf), but winemakers want the right levels of sugar and acid, not just lots of big watery berries, says Drake Whitcraft of Whitcraft Winery in California.

Another reason applications research should matter to mainstream machine learning is that the fields benchmark data sets are woefully out of touch with reality.

New machine-learning models are measured against large, curated data sets that lack noise and have well-defined, explicitly labeled categories (cat, dog, bird). Deep learning does well for these problems because it assumes a largely stable world (pdf).

But in the real world, these categories are constantly changing over time or according to geographic and cultural context. Unfortunately, the response has not been to develop new methods that address the difficulties of real-world data; rather, theres been a push for applications researchers to create their own benchmark data sets.

The goal of these efforts is essentially to squeeze real-world problems into the paradigm that other machine-learning researchers use to measure performance. But the domain-specific data sets are likely to be no better than existing versions at representing real-world scenarios. The results could do more harm than good. People who might have been helped by these researchers work will become disillusioned by technologies that perform poorly when it matters most.

Because of the fields misguided priorities, people who are trying to solve the worlds biggest challenges are not benefiting as much as they could from AIs very real promise. While researchers try to outdo one another on contrived benchmarks, one in every nine people in the world is starving. Earth is warming and sea level is rising at an alarming rate.

As neuroscientist and AI thought leader Gary Marcus once wrote (pdf): AIs greatest contributions to society could and should ultimately come in domains like automated scientific discovery, leading among other things towards vastly more sophisticated versions of medicine than are currently possible. But to get there we need to make sure that the field as whole doesnt first get stuck in a local minimum.

For the world to benefit from machine learning, the community must again ask itself, as Wagstaff once put it: What is the fields objective function? If the answer is to have a positive impact in the world, we must change the way we think about applications.

Hannah Kerner is an assistant research professor at the University of Maryland in College Park. She researches machine learning methods for remote sensing applications in agricultural monitoring and food security as part of the NASA Harvest program.

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Too many AI researchers think real-world problems are not relevant - MIT Technology Review

A comprehensive research report namelyGlobal Machine Learning in Manufacturing Market which discloses an all-encompassing breakdown of the global industry by delivering detailed information about Forthcoming Trends. The Machine Learning in Manufacturing Market report delivers an exhaustive analysis of global market size, segmentation market growth, market share, competitive Landscape also an in-depth study of the market enlightening key forecast to 2027, recent developments, opportunities analysis, strategic market growth analysis, and technological innovations.

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Major Companies Profiled in This Machine Learning in Manufacturing Market Report:

Domino Data Lab, Inc.Amazon Web Services Inc.Luminoso Technologies, Inc.SAP SEBoschTIBCO Software Inc.Oracle CorporationMicrosoft CorporationAlpine DataBigML, Inc.Baidu, Inc.TrademarkVisionNVIDIASiemensFractal Analytics Inc.FunacIntel CorporationIBM CorporationGEGoogle, Inc.Dell Inc.KNIME.com AGHewlett Packard Enterprise Development LPFair Isaac CorporationSAS Institute Inc.RapidMiner, Inc.TeradataAngoss Software CorporationKukaDataiku

Machine Learning in Manufacturing Market report Segmentation: North America, Europe, Asia Pacific, Latin America, and the Middle East and Africa. This report similarly reduces the current, past, and upcoming market business strategies, estimation analysis having a place with the forecast conditions.

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Machine Learning in Manufacturing Market Segmentation by Type:

CloudOn-Premises

Based on End Users/Application, the Machine Learning in Manufacturing Market has been segmented into:

Auto industryElectronics industryAviation industryOthers

Years Considered to Estimate the Machine Learning in Manufacturing Market Size:

History Year: 2015-2019

Base Year: 2019

Estimated Year: 2020

Forecast Year: 2020-2027

Do Make an inquiry of Machine Learning in Manufacturing Market Research [emailprotected]https://www.reportspedia.com/report/technology-and-media/2015-2027-global-machine-learning-in-manufacturing-industry-market-research-report,-segment-by-player,-type,-application,-marketing-channel,-and-region/66409#inquiry_before_buying

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Global Machine Learning in Manufacturing Market 2020 Future Development Status, Business Outlook, Segmentation and COVID-19 Impact Analysis 2027 -...

This article was originally published here

Intern Emerg Med. 2020 Aug 18. doi: 10.1007/s11739-020-02475-0. Online ahead of print.

ABSTRACT

Among patients with Coronavirus disease (COVID-19), the ability to identify patients at risk for deterioration during their hospital stay is essential for effective patient allocation and management. To predict patient risk for critical COVID-19 based on status at admission using machine-learning models. Retrospective study based on a database of tertiary medical center with designated departments for patients with COVID-19. Patients with severe COVID-19 at admission, based on low oxygen saturation, low partial arterial oxygen pressure, were excluded. The primary outcome was risk for critical disease, defined as mechanical ventilation, multi-organ failure, admission to the ICU, and/or death. Three different machine-learning models were used to predict patient deterioration and compared to currently suggested predictors and to the APACHEII risk-prediction score. Among 6995 patients evaluated, 162 were hospitalized with non-severe COVID-19, of them, 25 (15.4%) patients deteriorated to critical COVID-19. Machine-learning models outperformed the all other parameters, including the APACHE II score (ROC AUC of 0.92 vs. 0.79, respectively), reaching 88.0% sensitivity, 92.7% specificity and 92.0% accuracy in predicting critical COVID-19. The most contributory variables to the models were APACHE II score, white blood cell count, time from symptoms to admission, oxygen saturation and blood lymphocytes count. Machine-learning models demonstrated high efficacy in predicting critical COVID-19 compared to the most efficacious tools available. Hence, artificial intelligence may be applied for accurate risk prediction of patients with COVID-19, to optimize patients triage and in-hospital allocation, better prioritization of medical resources and improved overall management of the COVID-19 pandemic.

PMID:32812204 | DOI:10.1007/s11739-020-02475-0

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Utilization of machine-learning models to accurately predict the risk for critical COVID-19 - DocWire News

Improving the diagnosis of mental disorders and enabling experts to better personalize treatment is at the heart of a federal investment in machine learning and precision health at the University of Alberta.

Psychiatry professor Bo Cao, along with Russell Greiner and Serdar Dursun, received $258,000 from the Canada Foundation for Innovation (CFI) John R. Evans Leaders Fund to build infrastructure in the Computational Psychiatry Group, which will develop machine learning models from large populations for a host of different datasets for mental illnesses, such as depression, bipolar disorder and schizophrenia.

Cao, who also holds the Canada Research Chair in Computational Psychiatry, explained there are two major roles in computational psychiatryto detect a disease early, helping prevention and timely intervention, and to predict the progression and treatment outcomes for the disease, both of which emphasize individualized predictions using multiple types of data.

For example, he explained one day a machine learning model will be built that will see a brain scan of a patient compared against a database of brain scans, so the model can assist in making decisions about the diagnosis and treatments.

Basically we would like to apply big data and machine learning approaches to psychiatry, and eventually personalize the diagnosis and treatment for mental health, said Cao. That's actually the ultimate goalwe're not there yet, but we are on a promising path.

He added his teams overarching aim is to make these machine learning tools accurate and reliable, improving current clinical judgment in diagnosis and treatment selection.

It's not aiming for replacing doctors but assisting themit's still the doctors and patients making the decisions.

Cao said the Computational Psychiatry Group is a long-term collaboration between the Department of Psychiatry in the Faculty of Medicine & Dentistry and the Department of Computing Science in the Faculty of Science, which builds on more than four decades of expertise in AI and machine learning. The group has active collaborations with Amii, IBM Centers for Advanced Studies, AltaML, Alberta Healthand AHS, and is a core part of two of the universitys signature areas:Precision Health and AI4Society.

The equipment is not just for the lab but for the Computational Psychiatry Group. We hope to help extend the effort jointly with more researchers who are interested in this new field within and beyond the U of A, so that we can achieve personalized mental health for the public good, said Cao.

All told, 16 U of A research projects will receive CFI grants totalling $3.4 million, as well as matching funds from the Government of Alberta.

Forecasting community reassembly in changing seascapes: Cross-scale science to uncover patterns, processes, consequencesStephanie Green, Faculty of Science$148,000

Projected media in live performancesGuido Tondino, Robert Shannon and Lee Livingstone, Faculty of Arts$98,000

Additive manufacturing using a direct energy laser system for the resource sectorHani Henein, Ahmed Qureshi and Jason Myatt, Faculty of Engineering$195,000

Field lab for the investigation of altitude related population adaptation and healthCraig Steinback, Faculty of Kinesiology, Sport, and Recreation$236,000

The human explanted heart program: A translational bridge for cardiovascular medicine and drug developmentJohn Seubert and Gavin Oudit, Faculty of Medicine & Dentistry$217,000

The RASMAPKcapicua axis, a converging molecular highway in neurological disorders and leukemiaQiumin Tan, Faculty of Medicine & Dentistry$159,000

Terawatt laser facility for advanced applicationsRobert Fedosejevs, Faculty of Engineering$516,000

An all-optical platform for the investigation of animal models of neuropsychiatric and neurodegenerative diseaseAllen Chan, Faculty of Medicine & Dentistry$217,000

Defining the role of proteases in health and diseases using innovative systems biology approachesOlivier Julien and Joanne Lemieux, Faculty of Medicine & Dentistry$130,000

High speed confocal microscopic system for interfacial scienceXuehua Zhang, Faculty of Engineering$234,000

Infrastructure for emerging priority in AI and computational psychiatryBo Cao, Faculty of Medicine & Dentistry; Russell Greiner, Faculty of Science; and Serdar Dursun, Faculty of Medicine & Dentistry$258,000

Avian behaviour, ecology and energeticsKimberley Mathot, Faculty of Science$170,000

Laboratory for Sound Art, Sound Health, and Sound Communities (Sound3 Lab)Scott Smallwood, Faculty of Arts$211,000

Development and magnetometric application of powerful ultraviolet frequency comb lasersGil Porat, Faculty of Engineering$111,000

Chemistry at the interfaces: Devices for capturing and storing renewable energyLingzi Sang, Faculty of Science$227,000

Advanced structural response characterization system for civil infrastructureDouglas Tomlinson, Faculty of Engineering$269,000

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Researchers aim to use machine learning to improve diagnosis, treatment of mental illness - Folio - University of Alberta

Financial services company DBS has collaborated with Amazon Web Services (AWS) to launch DBS x AWS DeepRacer League in a bid to equip its employees with fundamental skills in artificial intelligence (AI) and machine learning (ML) by the end of the year.This comes as DBS sets its sights on accelerating the use of AI and ML across its business.

Through the DBS x AWS DeepRacer League, DBS expects at least 3,000 employees, including the banks senior leadership, to learn new AI and ML skills this year. During the programme, employees will participate in a series of hands-on online tutorials before putting their new knowledge to the test by programming autonomous model race car. These ML models will then be uploaded onto a virtual racing environment where employees can experiment and iteratively fine tune their models as they engage each other in friendly competition.

As part of DBS drive to ingrain digital learning behaviours among employees, the DBS x AWS DeepRacer League will be run completely online powered by AWS, from classroom to racetrack. This comes on the back of DBS effort to scale up its digital learning tools and platforms to enable its employees to upgrade their skills and pick up new knowledge even when they are not physically in the office.

Paul Cobban, chief data and transformation officer at DBS said that the company is "aware of the need to stay ahead of the technology curve to continue exceeding its customers expectations". He added that DBS had never believed in limiting digital expertise to a small team, and instead passionately believed in democratising technology skillsets among all employees, so that they could run alongside the company as it advanced on its digital transformation.

Additionally, we wanted to adopt a different approach from our previous digital and data skills revolutions. In line with our ethos of keeping work and learning fun, we sought to introduce gamification elements to better engage our employees, and the AWS DeepRacer League platform presented the perfect opportunity, Cobban explained.

Conor McNamara, MD of AWS ASEAN said the financial services industry was rapidly evolving, and that DBS once again demonstrated why it was a global award-winning bank by transforming its workforce for the digital age and equipping them with the latest knowledge on cloud technology. We are excited to collaborate with DBS to develop a talent pool that can further unlock the flexibility and power of cloud technology," McNamara added.

In 2019, DBS digitalised and simplified end-to-end credit processing, setting the foundation for advanced credit risk management using data analytics and ML. It also deployed an AI-powered engine to provide accurate self-service digital options to its retail customers based on their digital footprint, according to its press statement. Separately, DBS recentlypartnered with ride-hailing company Gojek to integrate Gojeks services into its PayLah! platform. Aimed at boosting the adoption of digital payments, this partnership allowed DBS PayLah! users to book and pay for their Gojek rides directly through the PayLah! platform.

Related articles:DBS and Gojek further push digital payment with PayLah! partnershipGood things come in pairs: DBS and POSB double up for a sustainable CNYDBS quoting German communist Friedrich Engels for IWD raises eyebrows

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DBS partners Amazon to upskill 3,000 employees in AI and machine learning - Marketing Interactive

A comprehensive research report namelyGlobal Machine Learning Chip Market which discloses an all-encompassing breakdown of the global industry by delivering detailed information about Forthcoming Trends. The Machine Learning Chip Market report delivers an exhaustive analysis of global market size, segmentation market growth, market share, competitive Landscape also an in-depth study of the market enlightening key forecast to 2027, recent developments, opportunities analysis, strategic market growth analysis, and technological innovations.

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XilinxBitmain TechnologiesAMD (Advanced Micro Devices)BaiduSamsungGoogle, Inc.QualcommNVIDIAIntel CorporationAmazon

Machine Learning Chip Market report Segmentation: North America, Europe, Asia Pacific, Latin America, and the Middle East and Africa. This report similarly reduces the current, past, and upcoming market business strategies, estimation analysis having a place with the forecast conditions.

Grab Your Report at an Impressive Discount! Please click here:

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This all-inclusive study covers an overview of various aspects of the industry including outlook, current Machine Learning Chip Market trends, and advance during the forecast period. Along with this, an in-depth analysis of each section of the report is also provided in the report that consists of the strategies adopted by the key players, challenges, and threats as well as advancements in the industry.

Machine Learning Chip Market Segmentation by Type:

GPUASICFPGACPUOthers

Based on End Users/Application, the Machine Learning Chip Market has been segmented into:

Media & AdvertisingBFSIIT & TelecomRetailHealthcareAutomotive & TransportationOthers

Years Considered to Estimate the Machine Learning Chip Market Size:

History Year: 2015-2019

Base Year: 2019

Estimated Year: 2020

Forecast Year: 2020-2027

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Global Machine Learning Chip Market 2020 | With Top Growing Manufacturers & Coronavirus (COVID-19) Impact Analysis, Significant Growth, Key Trends...