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New Machine Learning Theory Raises Questions About the Very Nature of Science – SciTechDaily

A novel computer algorithm, or set of rules, that accurately predicts the orbits of planets in the solar system could be adapted to better predict and control the behavior of the plasma that fuels fusion facilities designed to harvest on Earth the fusion energy that powers the sun and stars.

The algorithm, devised by a scientist at the U.S. Department of Energys (DOE) Princeton Plasma Physics Laboratory (PPPL), applies machine learning, the form of artificial intelligence (AI) that learns from experience, to develop the predictions. Usually in physics, you make observations, create a theory based on those observations, and then use that theory to predict new observations, said PPPL physicist Hong Qin, author of a paper detailing the concept in Scientific Reports. What Im doing is replacing this process with a type of black box that can produce accurate predictions without using a traditional theory or law.

Qin (pronounced Chin) created a computer program into which he fed data from past observations of the orbits of Mercury, Venus, Earth, Mars, Jupiter, and the dwarf planet Ceres. This program, along with an additional program known as a serving algorithm, then made accurate predictions of the orbits of other planets in the solar system without using Newtons laws of motion and gravitation. Essentially, I bypassed all the fundamental ingredients of physics. I go directly from data to data, Qin said. There is no law of physics in the middle.

PPPL physicist Hong Qin in front of images of planetary orbits and computer code. Credit: Elle Starkman / PPPL Office of Communications

The program does not happen upon accurate predictions by accident. Hong taught the program the underlying principle used by nature to determine the dynamics of any physical system, said Joshua Burby, a physicist at the DOEs Los Alamos National Laboratory who earned his Ph.D. at Princeton under Qins mentorship. The payoff is that the network learns the laws of planetary motion after witnessing very few training examples. In other words, his code really learns the laws of physics.

Machine learning is what makes computer programs like Google Translate possible. Google Translate sifts through a vast amount of information to determine how frequently one word in one language has been translated into a word in the other language. In this way, the program can make an accurate translation without actually learning either language.

The process also appears in philosophical thought experiments like John Searles Chinese Room. In that scenario, a person who did not know Chinese could nevertheless translate a Chinese sentence into English or any other language by using a set of instructions, or rules, that would substitute for understanding. The thought experiment raises questions about what, at root, it means to understand anything at all, and whether understanding implies that something else is happening in the mind besides following rules.

Qin was inspired in part by Oxford philosopher Nick Bostroms philosophical thought experiment that the universe is a computer simulation. If that were true, then fundamental physical laws should reveal that the universe consists of individual chunks of space-time, like pixels in a video game. If we live in a simulation, our world has to be discrete, Qin said. The black box technique Qin devised does not require that physicists believe the simulation conjecture literally, though it builds on this idea to create a program that makes accurate physical predictions.

The resulting pixelated view of the world, akin to what is portrayed in the movie The Matrix, is known as a discrete field theory, which views the universe as composed of individual bits and differs from the theories that people normally create. While scientists typically devise overarching concepts of how the physical world behaves, computers just assemble a collection of data points.

Qin and Eric Palmerduca, a graduate student in the Princeton University Program in Plasma Physics, are now developing ways to use discrete field theories to predict the behavior of particles of plasma in fusion experiments conducted by scientists around the world. The most widely used fusion facilities are doughnut-shaped tokamaks that confine the plasma in powerful magnetic fields.

Fusion, the power that drives the sun and stars, combines light elements in the form of plasma the hot, charged state of matter composed of free electrons and atomic nuclei that represents 99% of the visible universe to generate massive amounts of energy. Scientists are seeking to replicate fusion on Earth for a virtually inexhaustible supply of power to generate electricity.

In a magnetic fusion device, the dynamics of plasmas are complexand multi-scale, and the effective governing laws or computational models for a particular physical process that we are interested in are not always clear, Qin said. In these scenarios, we can apply the machine learning technique that I developed to create a discrete field theory and then apply this discrete field theory to understand and predict new experimental observations.

This process opens up questions about the nature of science itself. Dont scientists want to develop physics theories that explain the world, instead of simply amassing data? Arent theories fundamental to physics and necessary to explain and understand phenomena?

I would argue that the ultimate goal of any scientist is prediction, Qin said. You might not necessarily need a law. For example, if I can perfectly predict a planetary orbit, I dont need to know Newtons laws of gravitation and motion. You could argue that by doing so you would understand less than if you knew Newtons laws. In a sense, that is correct. But from a practical point of view, making accurate predictions is not doing anything less.

Machine learning could also open up possibilities for more research. It significantly broadens the scope of problems that you can tackle because all you need to get going is data, Palmerduca said.

The technique could also lead to the development of a traditional physical theory. While in some sense this method precludes the need of such a theory, it can also be viewed as a path toward one, Palmerduca said. When youre trying to deduce a theory, youd like to have as much data at your disposal as possible. If youre given some data, you can use machine learning to fill in gaps in that data or otherwise expand the data set.

Reference: Machine learning and serving of discrete field theories by Hong Qin, 9 November 2020, Scientific Reports.DOI: 10.1038/s41598-020-76301-0

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Using AI and Machine Learning will increase in horti industry – hortidaily.com

The expectation is that in 2021, artificial intelligence and machine learning technologies will continue to become more mainstream. Businesses that havent traditionally viewed themselves as candidates for AI applications will embrace these technologies.

A great story of machine learning being used in an industry that is not known for its technology investments is the story of Makoto Koike. Using Googles TensorFlow, Makoto initially developed a cucumber sorting system using pictures that he took of the cucumbers. With that small step, a machine learning cucumber sorting system was born.

Getting started with AI and machine learning is becoming increasingly accessible for organizations of all sizes. Technology-as-a-service companies including Microsoft, AWS and Google all have offerings that will get most organizations started on their AI and machine learning journeys. These technologies can be used to automate and streamline manual business processes that have historically been resource-intensive.

An article on forbes.com claims that, as business leaders continue to refine their processes to support the new normal of the Covid-19 pandemic, they should be considering where these technologies might help reduce manual, resource-intensive or paper-based processes. Any manual process should be fair game for review for automation possibilities.

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Machine Learning in Medicine Market 2021 to Perceive Biggest Trend and Opportunity by 2028 KSU | The Sentinel Newspaper – KSU | The Sentinel…

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Machine Learning in Medicine Market 2021 to Perceive Biggest Trend and Opportunity by 2028 KSU | The Sentinel Newspaper - KSU | The Sentinel...

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The head of JPMorgan’s machine learning platform explained what it’s like to work there – eFinancialCareers

For the past few years, JPMorgan has been busy building out its machine learning capability underDaryush Laqab, its San Francisco-based head of AI platform product management, who was hired from Google in 2019. Last time we looked, the bank seemed to be paying salaries of $160-$170k to new joiners onLaqab's team.

If that sounds appealing, you might want to watch the video below so that you know what you're getting into. Recorded at the AWS re:Invent conferencein December, it's only just made it to you YouTube. The video is flagged as a day in the life of JPMorgan's machine learning data scientists, butLaqab arguably does a better of job of highlighting some of the constraints data professionals at allbanks have to work under.

"There are some barriers to smooth data science at JPMorgan," he explains - a bank is not the same as a large technology firm.

For example, data scientists at JPMorgan have to check data is authorized for use, saysLaqab: "They need to go to a process to log that use and make surethat they have the adequate approvals for that intent in terms of use."

They also have to deal with the legacy infrastructureissue: "We are a large organization, we have a lot of legacy infrastructure," says Laqab. "Like any other legacy infrastructure, it is built over time,it is patched over time. These are tightly integrated,so moving part or all of that infrastructure to public cloud,replacing rule base engines with AI/ML based engines.All of that takes time and brings inertia to the innovation."

JPMorgan's size and complexity is another source of inertia as multiple business lines in multiple regulated entities in different regulated environments need to be considered. "Making sure that those regulatory obligationsare taken care of, again, slows down data science at times," saysLaqab.

And then there are more specific regulations such as those concerning model governance. At JPMorgan, a machine learning model can't go straight into a production environment."It needs to go through a model review and a model governance process," says Laqab. "- To make sure we have another set of eyes that looksat how that model was created, how that model was developed..." And then there are software governance issues too.

Despite all these hindrances, JPMorgan has already productionized AI models and built an 'Omni AI ecosystem,'which Laqab heads,to help employees to identify and ingest minimum viable data so that they canbuild models faster. Laqab saysthe bank saved $150m in expenses in 2019 as a result. JPMorgan's AI researchers are now working on everything fromFAQ bots and chat bots, to NLP search models for the bank'sown content, pattern recognition in equities markets and email processing. - The breadth of work on offer is considerable. "We play in every market that is out there," saysLaqab,

The bank has also learned that the best way to structure its AI team is to split people into data scientists who train and create models and machine learning engineers who operationalize models, saysLaqab. - Before you apply, you might want to consider which you'd rather be.

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Mental health diagnoses and the role of machine learning – Health Europa

It is common for patients with psychosis or depression to experience symptoms of both conditions which has meant that traditionally, mental health diagnoses have been given for a primary illness with secondary symptoms of the other.

Making an accurate diagnosis often poses difficulties to mental health clinicians and diagnoses often do not accurately reflect the complexity of individual experience or neurobiology. For example, a patient being diagnosed with psychosis will often have depression regarded as a secondary condition, with more focus on the psychosis symptoms, such as hallucinations or delusions; this has implications on treatment decisions for patients.

A team at the University of Birminghams Institute for Mental Health and Centre for Human Brain Health, along with researchers at the European Union-funded PRONIA consortium, explored the possibility of using machine learning to create extremely accurate models of pure forms of both illnesses and using these models to investigate the diagnostic accuracy of a cohort of patients with mixed symptoms. The results of this study have been published in Schizophrenia Bulletin.

Paris Alexandros Lalousis, lead author, explains that the majority of patients have co-morbidities, so people with psychosis also have depressive symptoms and vice versa That presents a big challenge for clinicians in terms of diagnosing and then delivering treatments that are designed for patients without co-morbidity. Its not that patients are misdiagnosed, but the current diagnostic categories we have do not accurately reflect the clinical and neurobiological reality.

The researchers analysed questionnaire responses and detailed clinical interviews, as well as data from structural magnetic resonance imaging from a cohort of 300 patients taking part in the study. From this group of patients, they identified small subgroups of patients, who could be classified as suffering either from psychosis without any symptoms of depression, or from depression without any psychotic symptoms.

With the goal of developing a precise disease profile for each patient and testing it against their diagnosis to see how accurate it was, the research team was able to identify machine learning models of pure depression, and pure psychosis by using the collected data. They were then able to use machine learning methods to apply these models to patients with symptoms of both illnesses.

The team discovered that patients with depression as a primary illness were more likely to have accurate mental health diagnoses, whereas patients with psychosis with depression had symptoms which most frequently leaned towards the depression dimension. This may suggest that depression plays a greater part in the illness than had previously been thought.

Lalousis added: There is a pressing need for better treatments for psychosis and depression, conditions which constitute a major mental health challenge worldwide. Our study highlights the need for clinicians to understand better the complex neurobiology of these conditions, and the role of co-morbid symptoms; in particular considering carefully the role that depression is playing in the illness.

In this study we have shown how using sophisticated machine learning algorithms, which take into account clinical, neurocognitive, and neurobiological factors can aid our understanding of the complexity of mental illness. In the future, we think machine learning could become a critical tool for accurate diagnosis. We have a real opportunity to develop data-driven diagnostic methods this is an area in which mental health is keeping pace with physical health and its really important that we keep up that momentum.

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5 Ways the IoT and Machine Learning Improve Operations – BOSS Magazine

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By Emily Newton

The Internet of Things (IoT) and machine learning are two of the most disruptive technologies in business today. Separately, both of these innovations can bring remarkable benefits to any company. Together, they can transform your business entirely.

The intersection of IoT devices and machine learning is a natural progression. Machine learning needs large pools of relevant data to work at its best, and the IoT can supply it. As adoption of both soars, companies should start using them in conjunction.

Here are five ways the IoT and machine learning can improve operations in any business.

Around 25% of businesses today use IoT devices, and this figure will keep climbing. As companies implement more of these sensors, they add places where they can gather data. Machine learning algorithms can then analyze this data to find inefficiencies in the workplace.

Looking at various workplace data, a machine learning program could see where a company spends an unusually high amount of time. It could then suggest a new workflow that would reduce the effort employees expend in that area. Business leaders may not have ever realized this was a problem area without machine learning.

Machine learning programs are skilled at making connections between data points that humans may miss. They can also make predictions 20 times earlier than traditional tools and do so with more accuracy. With IoT devices feeding them more data, theyll only become faster and more accurate.

Machine learning and the IoT can also automate routine tasks. Business process automation (BPA) leverages AI to handle a range of administrative tasks, so workers dont have to. As IoT devices feed more data into these programs, they become even more effective.

Over time, technology like this has contributed to a 40% productivity increase in some industries. Automating and streamlining tasks like scheduling and record-keeping frees employees to focus on other, value-adding work. BPAs potential doesnt stop there, either.

BPA can automate more than straightforward data manipulation tasks. It can talk to customers, plan and schedule events, run marketing campaigns and more. With more comprehensive IoT implementation, it would have access to more areas, becoming even more versatile.

One of the most promising areas for IoT implementation is in the supply chain. IoT sensors in vehicles or shipping containers can provide companies with critical information like real-time location data or product quality. This data alone improves supply chain visibility, but paired with machine learning, it could transform your business.

Machine learning programs can take this real-time data from IoT sensors and put it into action. It could predict possible disruptions and warn workers so they can respond accordingly. These predictive analytics could save companies the all-too-familiar headache of supply chain delays.

UPS Orion tool is the gold standard for what machine learning can do for supply chains. The system has saved the shipping giant 10 million gallons of fuel a year by adjusting routes on the fly based on traffic and weather data.

If a company cant understand the vulnerabilities it faces, business leaders cant make fully informed decisions. IoT devices can provide the data businesses need to get a better understanding of these risks. Machine learning can take it a step further and find points of concern in this data that humans could miss.

IoT devices can gather data about the workplace or customers that machine learning programs then process. For example, Progressive has made more than 1.7 trillion observations about its customers driving habits through Snapshot, an IoT tracking device. These analytics help the company adjust clients insurance rates based on the dangers their driving presents.

Business risks arent the only hazards the Internet of Things and machine learning can predict. IoT air quality sensors could alert businesses when to change HVAC filters to protect employee health. Similarly, machine learning cybersecurity programs could sense when hackers are trying to infiltrate a companys network.

Another way the IoT and machine learning could transform your business is by eliminating waste. Data from IoT sensors can reveal where the company could be using more resources than it needs. Machine learning algorithms can then analyze this data to suggest ways to improve.

One of the most common culprits of waste in businesses is energy. Thanks to various inefficiencies, 68% of power in America ends up wasted. IoT sensors can measure where this waste is happening, and with machine learning, adjust to stop it.

Machine learning algorithms in conjunction with IoT devices could restrict energy use, so processes only use what they need. Alternatively, they could suggest new workflows or procedures that would be less wasteful. While many of these steps may seem small, they add up to substantial savings.

Without the IoT and machine learning, businesses cant reach their full potential. These technologies enable savings companies couldnt achieve otherwise. As they advance, theyll only become more effective.

The Internet of Things and machine learning are reshaping the business world. Those that dont take advantage of them now could soon fall behind.

Emily Newton is the Editor-in-Chief of Revolutionized, a magazine exploring how innovations change our world. She has over 3 years experience writing articles in the industrial and tech sectors.

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