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Category Archives: Machine Learning

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

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

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Machine Learning Practices And The Art of Research Management – Analytics India Magazine

Allegro AI offers the first true end-to-end ML / DL product life-cycle management solution with a focus on deep learning applied to unstructured data.

Machine learning projects involve iterative and recursive R&D process of data gathering, data annotation, research, QA, deployment, additional data gathering from deployed units and back again. The effectiveness of a machine learning product depends on how intact the synergies are between data, model and various teams across the organisation.

In this informative session at CVDC 2020, a 2 day event organised by ADaSci, Dan Malowany of Allegro.AI presented the attendees with the best practices to imbibe during the lifecycle of an ML productfrom inception to production.

Dan Malowany is currently the head of deep learning research at allegro.ai. His Ph.D. research at the Laboratory of Autonomous Robotics (LAR) was focused on integrating mechanisms of the human visual system with deep convolutional neural networks. His research interests include computer vision, convolutional neural networks, reinforcement learning, the visual cortex and robotics.

Dan spoke about the features required to boost productivity in the different R&D stages. This talk specifically focused on the following:

Dan, who has worked for 15 years at the Directorate for Defense Research & Development and led various R&D programs, briefed the attendees about various complexities involved in developing deep learning applications. He shed light on the unattractive and often overlooked aspects of research. He explained the trade offs between effort and accuracy through concepts of diminishing returns in the case of increased inputs.

When your model is as good as your data then the role of data management becomes crucial. Organisations are often in the pursuit of achieving better results with less data. Practices such as mixing and matching data sets with detailed control and creating optimised synthetic data come in handy.

Underlining the importance of data and experiment management, Dan advised the attendees to track the various versions of data and treat it as a hyperparameter. Dan also highlighted the various risk factors involved in improper data management. He took the example of developing a deep learning solution for diagnosis of diabetic retinopathy. He followed this up with an overview of the benefits of resource management.

Unstructured data management is only a part of the solution. There are other challenges, which Allegro AI claims to solve. In this talk Dan introduced the audience to their customised solutions.

Towards the end of the talk, Dan gave a glimpse about the various tools integrated with allegro.ais services. Allegro AIs products are market proven and have partnered with leading global brands, such as Intel, NVIDIA, NetApp, IBM and Microsoft. Allegro AI is backed by world-class firms including household name strategic investors: Samsung, Bosch and Hyundai.

Allegro AI helps companies develop, deploy and manage machine & deep learning solutions. The companys products are based on the Allegro Trains open source ML & DL experiment manager and ML-Ops package. Here are a few features:

Unstructured Data Management

Resource Management & ML-Ops

Know more here.

Stay tuned to AIM for more updates on CVDC2020.

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

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Machine Learning Practices And The Art of Research Management - Analytics India Magazine

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Mphasis Partners With Ashoka University to Create ‘Mphasis Laboratory for Machine Learning and Computational Thinking’ – AiThority

Mphasis,an Information Technology solutions provider specialising in cloud and cognitive services,is coming together withAshoka University toset upalaboratory for machine learning and computational thinking, through a grant of INR 10 crore that Mphasis F1Foundation, the CSR arm ofthe company. The Mphasis Laboratory for Machine Learning and Computational Thinking will apply ML and design thinking to produce world-class papers and compelling proof-of-concepts of systems/prototypes with a potential for large societal impact.

The laboratory will be the setting for cutting edge research and a novel educational initiative that is focused on bringing thoroughly researched, pedagogy-based learning modules to Indian students. Through this laboratory, Mphasis and Ashoka University will work to translate research activity into educational modules focusing on the construction of entire systems that allow students to understand and experientially recreate the project. This approach to education is aimed creating a more engaging and widely accessible mode of learning.

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Mphasis believes that in order to fully embrace the digital learning paradigm, one needs to champion for accessibility and invest in quality education in mainstream academic spaces. Through this partnership, we hope to encourage students across disciplines and socio-economic backgrounds to learn and flourish. As Ashoka University also has a strong focus on diverse liberal arts disciplines, we hope to find avenues to expand some of Mphasis efforts towards Design (CX Design and Design Thinking) through this collaboration and eventually tap into the talent pool from Ashoka, saidNitin Rakesh, Chief Executive Officer, Mphasis.

Being ready to welcome students into the world of virtual learning is not enough Mphasis and Ashoka seek to enable an innovative pedagogy based on a problem-solving approach to learning about AI, ML, Design Thinking and System Design. Through this grant, Mphasis and Ashoka will establish avenues for knowledge exchange in the areas of Core Machine Learning, Information Curation, Accessibility for persons with disabilities and Health & Medicine. They seek to encourage a hands-on learning approach in areas such as core machine learning and information curation, which form the foundation of solution-driven design. They also seek to address the accessibility barrier through public-domain placement of all intellectual property produced in the laboratory which will benefit millions of students across the country.

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We stand at the threshold of a discontinuity brought about by an increased ability to sense and produce enormous amounts of data and to create extremely large clusters driven by parallel runtimes.These developments have enabled ML and other data-driven approaches to become the paradigm of choice for complex problem solving. There is now a considerable opportunity to improving life-at-large based on these capabilities, said Prof. Ravi Kothari, HOD, Computer Science at Ashoka University.

With that as our over-arching goal, we proposed the creation of aLaboratory for Machine Learning and Computational Thinkingand found heartening support in Mphasis.saidAshish Dhawan, Founder & Chairman, Board of Trustees, Ashoka University.

While universities world-over have taken great strides to bring quality education to digital platforms, higher educational institutions in India have begun to address questions surrounding accessibility in a post-COVID setting. The collaboration between Mphasis and Ashoka is pioneering in its effort to establish a centre of excellence for collaborative and human-centred design that aims to fuel data-driven solutions for real-life challenges and address key areas of reform at the larger community level.

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Mphasis Partners With Ashoka University to Create 'Mphasis Laboratory for Machine Learning and Computational Thinking' - AiThority

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Benefits Of AI And Machine Learning | Expert Panel | Security News – SecurityInformed

The real possibility of advancing intelligence through deep learning and other AI-driven technology applied to video is that, in the long term, were not going to be looking at the video until after something has happened. The goal of gathering this high level of intelligence through video has the potential to be automated to the point that security operators will not be required to make the decisions necessary for response. Instead, the intelligence-driven next steps will be automatically communicated to various stakeholders from on-site guards to local police/fire departments. Instead, when security leaders access the video that corresponds to an incident, it will be because they want to see the incident for themselves. And isnt the automation, the ability to streamline response, and the instantaneous response the goal of an overall, data-rich surveillance strategy? For almost any enterprise, the answer is yes.

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Machine Learning as a Service (MLaaS) Market Size: Opportunities, Current Trends And Industry Analysis by 2028 | Microsoft, IBM Corporation,…

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Machine Learning as a Service (MLaaS)

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Machine Learning as a Service (MLaaS) Market Size: Opportunities, Current Trends And Industry Analysis by 2028 | Microsoft, IBM Corporation,...

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How AI can help payers navigate a coming wave of delayed and deferred care – FierceHealthcare

So far insurers have seen healthcare use plummet since the onset of the COVID-19 pandemic.

But experts are concerned about a wave of deferred care that could hit as patients start to return to patients and hospitals putting insurers on the hook for an unexpected surge of healthcare spending.

Artificial intelligence and machine learning could lend insurers a hand.

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We are using the AI approaches to try to protect future cost bubbles, said Colt Courtright, chief data and analytics officer at Premera Blue Cross, during a session with Fierce AI Week on Wednesday.

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He noted that people are not going in and getting even routine cancer screenings.

If people have delay in diagnostics and delay in medical care how is that going to play out in the future when we think about those individuals and the need for clinical programs and the cost and how do we manage that? he said.

Insurers have started in some ways to incorporate AI and machine learning in several different facets such as claims management and customer service, but insurers are also starting to explore how AI can be used to predict healthcare costs and outcomes.

In some ways, the pandemic has accelerated the use of AI and digital technologies in general.

If we can predict, forecast and personalize care virtually, then why not do that, said Rajeev Ronanki, senior vice president and chief digital officer for Anthem, during the session.

The pandemic has led to a boom in virtual telemedicine as the Trump administration has increased flexibility for getting Medicare payments for telehealth and patients have been scared to go to hospitals and physician offices.

But Ronanki said that AI cant just help with predicting healthcare costs, but also on fixing supply chains wracked by the pandemic.

He noted that the manufacturing global supply chain is extremely optimized, especially with just-in-time ordering that doesnt require businesses to have a large amount of inventory.

But that method doesnt really work during a pandemic when there is a vast imbalance in supply and demand with personal protective equipment, said Ronanki.

When you connect all those dots, AI can then be used to configure supply and demand better in anticipation of issues like this, he said.

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