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Getting Started With Machine Learning: Definition and Applications – CMSWire

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Artificial intelligence (AI) and machine learning (ML) are positioned to disrupt the way we live and work, even the way we interact and think. Machine learning is a core sub-area of AI. It makes computers get into a self-learning mode without explicit programming.

At this point, most organizations are still approaching ML as a technology in the realm of research and exploration. In this first article of a series, we delve deeper into the world of machine learning and its applications. The following articles will focus on building an ML implementation plan. In doing so we not only understand the concepts behind the technology, but also why it can make the difference between keeping up with competition or falling further behind.

Gartner defines machine learning as:Advanced learning algorithms composed of many technologies (such as deep learning, neural networks and natural language processing), used in unsupervised and supervised learning, that operate guided by lessons from existing information.

Machine learning is the process of teaching computers to develop intuitive knowledge and understanding through the use of repetitive algorithms and patterns. Machine learning in lay-man's terms is the process of schooling a repetitive activity to a dumb system that needs to develop some innate intelligence. The goal is to feed the system large amounts of data so it learns from each pattern and its variations, so it can eventually be able to identify the pattern and its variants on its own. The advantage a machine has over the human mind here is its ability to ingest and process large amounts of data. The human brain, although limitless in its capacity to ingest data, may not be able to process it at the same time and can only recall a limited set at one time.

There are three key types of machine learning: supervised, unsupervised and reinforced.

Other aspects of machine learning include neural networks and deep learning.

Neural networks have been studies for a long time. These algorithms endeavor to recognize the underlying relationships in data, just the way the human brain operates.

Deep learning is a class of machine learning algorithms that involves multiple layers of neural networks where the output of one network becomes the input to another.

The key to understanding machine learning is to understand the power of data. These algorithms work by finding patterns in massive amounts of data. This data, encompasses a lot of thingsnumbers, words, images, videos, sound files etc. Any data or meta data that can be digitally stored, can be fed into a machine-learning algorithm.

Related Article: Machine Learning Fragmentation Is Slowing Us Down: There Is a Solution

Machine learning, in conjunction with deep learning, have a wide variety of applications in our home and businesses today. It is currently used in everyday services such as recommendation systems like those on Netflix and Amazon; voice assistants like Siri and Alexa; car technology in parking assist and preventing accidents. Deep learning is already heavily used in autonomous vehicles and facial recognition systems. As the technology matures and receives widespread acceptance, we expect to see its applicability grow in these areas:

And many more .

Related Article: Why Artificial Intelligence May Not Offer the Business Value You Think

The availability of widespread computing power though the use of cloud technologies along with an increasing volume of readily available data has driven a number of advancements in the field of AI and ML. Organizations need to first build an understanding of the technology itself, collaborate on building a vision for using the technology internally and then build an implementation plan collaboratively between business and IT. In part two of this ML series we will focus on building a vision and implementation plan.

Geetika Tandon is a senior director at Booz Allen Hamilton, a management and technology consulting firm. She was born in Delhi, India, holds a Bachelors in architecture from Delhi University, a Masters in architecture from the University of Southern California and a Masters in computer science from the University of California Santa Barbara.

The views and opinions expressed in these articles are those of the author and do not necessarily reflect the official policy or position of her employer.

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Getting Started With Machine Learning: Definition and Applications - CMSWire

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February 20th, 2021 at 7:45 pm

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This Biotech Company Combines Single Cell Genomics with Machine Learning (ML) Algorithms To Enable High Resolution Profiling of the Immune System -…

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Immunai is a biotech company using machine learning algorithms that combine single-cell genomics to empower the human immune systems high-resolution profiling. Based out of New York, this company was established merely three years ago, but it is growing at a breakneck pace with the largest dataset in the world for single-cell immunity characteristics. Recently, the startup managed to raise a whopping $60 million in Series A funding. The total number of funds raised now stands at $80 million. With its machine learning algorithms, Immunai has already powered the existing immunotherapies with an enhanced performance level by bettering the analysis of an individuals immunity. It is now ready for a new dawn. With the help of the new funding received, Immunai will delve into the arena of creating new therapies altogether with the help of its vast expanse of data and advanced machine learning algorithms.

The human immune system has been a highly researched topic, and with the onset of the pandemic, the reprogramming of immunity has been under the limelight. To get an in-depth analysis of the same, Immunai makes use of the multiomic approach which helps in the layering analysis of the various types of biological data available. What makes Immunai stand out from the crowd is that it uses and combines the richest data sets. These data sets are procured from the best immunological research organizations from across the globe with machine learning algorithms designed to deliver analytics at a never seen before pace.

Immunai has two great co-founders in its ambit: Noan Solomon and Luis Voloch. Both the founders have extreme knowledge in computer science as well as artificial intelligence. Their efforts right from the beginning were aligned towards the usage of machine learning technology in the field of immunology. Prior to the funding, the main job being done at Immunai was the observation of cells. In contrast, now, they will observe the cells and perturb them to see the aftermath. The machine learning algorithms being used at Immunai allow them to evaluate an approach practically. This makes their model more feasible and influential in the real world.

After successfully understanding the human immune profile, the next step will be to administer new drugs to help fight potential diseases. To understand it better, we can take the example of Google Maps, wherein initially, it takes years to understand the road mapping solely. Similarly, as of now, Immunai is working on understanding the different pathways present in the immune system with the help of machine learning. Once done, the roads and paths underdeveloped or those that havent been built can be given a lending hand. This will eventually lead to a healthier world more armed to fight any disease, even like the pandemic that we are faced with within the current scenario.

One major milestone that any immunotherapy needs to achieve is finding the right immunotherapy for the right patient. This poses a herculean task given the complex structure of the human immune system, but with the advancement of the machine learning models, one can expect to overcome this roadblock soon.


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Immunai Raises $60M to Decode the Immune System with Machine Learning and AI – AlleyWatch

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The immune system at its core is a complex system of cells, organs, and tissues. These components work in unison to fight infection in the form of microbes. Developing an understanding of how this intricate system works is critical in ensuring that society as a whole has adequate immune health to combat disease and infection.Immunaihas built the largest database for immunology in the world using machine learning and AI to map the entire immune system at a granular and specific level. This data can be leveraged by the healthcare industry to provide better therapeutics that get to market faster. This understanding will also allow biotech companies and pharmaceutical manufacturers to radically personalize therapeutics in the future. Immunai is initially focused on the oncology market but the offering is versatile can be applied to things like autoimmune disorders and infectious diseases like COVID-19.

AlleyWatch caught up with CEO and Cofounder Noam Solomon to learn more about the impact that Immunai is having in the understanding of the immune system, the companys partnerships, experience fundraising during the pandemic, latest funding round, and much, much more

Who were your investors and how much did you raise?

This $60M Series A round was led by Schusterman Family Investments, Duquesne Family Office, Catalio Capital Management, and Dexcel Pharma, with additional participation from existing investors Viola Ventures and TLV Partners.

Tell us about the product or service that Immunai offers.

Immunai is on a mission to reprogram the immune system to advance personalized medicine to better detect, diagnose, and treat disease. To do so, Immunai has generated the largest proprietary database for immunology in the world, known as the Annotated Multi-omic Immune Cell Atlas (AMICA). This platform incorporates variables such as clinical lab metadata (e.g., processing wait time) and batch data (e.g., hospital), and others; then, it leverages machine learning and artificial intelligence to complete the annotation and characterization of immune cells. Immunais team of computational biologists and immunologists work with our partners at pharmaceutical companies to figure out the implications of what Immunai has found, whether its a new therapy, a drug combination, or a diagnostic.

What inspired the start of Immunai?

When I met my cofounder Luis, I was a math postdoc at MIT and Luis was working to apply machine learning to biology. Together, we wanted to bring transfer learning AI methods to what we believe would solve the biggest problem in society today disease.

All disease can be traced back to the immune system. But what we realized is that pharmaceutical companies dont have access to any comprehensive, granular insight into how the immune system works, how it responds to the drugs or therapies theyre developing, and what patients are most likely to benefit. With our scientific cofounders, Ansu Satpathy (assistant professor at Stanford for cancer immunology), Danny Wells (researcher at the Parker Institute for cancer immunotherapy) and Dan Littman (Professor at NYU and HHMI investigator) we realized that with single-cell technologies we would be able to measure and map the immune system with granularity and specificity like never available before.

At Immunai, weve combined the brightest minds across single-cell genomics, data science, and engineering to build the largest proprietary database on immunology in the world. We hope our work will lead to a better understanding of how to overcome the key unsolved problems and bottlenecks in immunotherapy discovery and development. We want to enable the development of more effective therapies and combinations for each patient, accelerate the ability to bring these therapies to market, and ultimately, provide better options for patients at a faster pace than ever before.

How is Immunai different?

No one is doing exactly what were doing. Companies have been trying to understand the immune system for years, but have been limited by traditional bulk sequencing technologies, which dont provide nearly enough data. By analyzing gene expression levels, protein markers, TCR and BCR fragments, and other single-cell omics, weve compiled 10,000 times more data for each immune cell than others before, giving partners a view of the immune system with a full spectrum of color and dimensionality.

Further, our proprietary machine learning and single-cell analysis that we apply to mine AMICA , the worlds largest proprietary Multiomic Immune Cell Atlas, allow us to understand the immune system at scale with unprecedented granularity and consistency. This provides a solution to the prohibitive batch effect problem that our competitors have not been able to solve.

What market does Immunai target and how big is it?

Immunais offering can be applied to multiple disease areas from cancer to autoimmune disorders to infectious diseases like COVID-19. The company is primarily focusing on the oncology market, which is currently set to surpass $469.5 billion by 2026.

Whats your business model?

Immunai partners with biopharmaceutical and biotech companies to answer critical questions like what makes T-cells expand, persist, and penetrate a tumor, which cells are cytotoxic, which cells in a cell therapy drive response, what are the immunological signatures that are more likely to lead to clinical response to different therapies, and more. These partnerships are usually structured as milestone-based collaborations, ranging from prospective clinical trial design and biomarker discovery to earlier target discovery and target validation.

How has COVID-19 impacted your business?

COVID-19 has impacted the way we work and the pace at which we work. Weve asked our employees who are not working in the lab to work from home and have implemented strict social distancing protocols within the lab. In the biopharma world, business is bigger than ever before, so we have many new partnerships in a variety of disease areas, including Immuno-Oncology, Autoimmunity, Neurodegenerative diseases, and infectious diseases .

What was the funding process like?

Fast but complex. It happened over a few very eventful months, with many important partnerships forged and multiple parties involved in the financing round, which all took place during a worldwide pandemic, of course.

What are the biggest challenges that you faced while raising capital?

The financing round happened as we were closing a few important partnerships, so running both responsibilities as CEO was non-trivial. In the middle of it all, life happened, and we had to deal with family health issues, including the fact that my wife and I had caught COVID, but we were both fine, luckily.

But what I didnt expect from the pandemic was being able to raise $60M without meeting the lead investors face to face. This is something that frankly, I didnt expect happening, and definitely didnt expect would happen so fast.

What factors about your business led your investors to write the check?

Our investors have witnessed the accelerated growth of our platform and are aligned with our vision to reprogram immunity. Machine learning crossed with genomics will unlock the mysteries of the immune system and lead to improved therapies. To actually execute on this vision, a world-class team is required, and weve put it together.

What are the milestones you plan to achieve in the next six months?

Were going to use this new financing round to build and improve our platform. With our expansion into functional genomics, well be funding collaborations with partners to answer the most pressing questions in immuno-oncology, cell therapy, infectious disease, and autoimmunity, including key biology driving clinical endpoints and target discovery.

We also plan to invest heavily in growth and double our team of 70 by year-end. We currently have a large lab in New York with 50 scientists working on sequencing and tech development. Were looking to add more people to the team to develop new assets and IP.

We also plan to invest heavily in growth and double our team of 70 by year-end. We currently have a large lab in New York with 50 scientists working on sequencing and tech development. Were looking to add more people to the team to develop new assets and IP.

What advice can you offer companies in New York that do not have a fresh injection of capital in the bank?

Understand the essence of what youre building and bring it to market quickly. Lean Startup is one of the most important business books Ive read; its critical for any business, but particularly for one with a limited runway. Whats the most expeditious experiment you can run to see if your customers actually care about your product.

Where do you see the company going now over the near term?

Were transitioning from observational genomics to functional genomics. Were concentrating on two major projects: improving the ability to target new checkpoints and validate targets for cell therapies. Just in the last year, weve been able to identify new mechanisms of resistance with partners in record time. At this pace, we hope the work well be able to do in the next couple of years will be groundbreaking and life-saving, but its too early to say specifically where well be.

Whats your favorite outdoor dining restaurant in NYC

Cafe Mogador on St Marks.

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Immunai Raises $60M to Decode the Immune System with Machine Learning and AI - AlleyWatch

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Cloud Machine Learning Market: Indoor Applications Projected to be the Most Attractive Segment during 2021-2029 KSU | The Sentinel Newspaper – KSU |…

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Reports published inMarket Research Incfor the Cloud Machine Learning market are spread out over several pages and provide the latest industry data, market future trends, enabling products and end users to drive revenue growth and profitability. Industry reports list and study key competitors and provide strategic industry analysis of key factors affecting market dynamics. This report begins with an overview of the Cloud Machine Learning market and is available throughout development. It provides a comprehensive analysis of all regional and major player segments that provide insight into current market conditions and future market opportunities along with drivers, trend segments, consumer behavior, price factors and market performance and estimates over the forecast period.

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Key Strategic Manufacturers:: Oracle Corporation , Google Inc, IBM, Alibaba Group, Microsoft Corporation, Amazon Web Services , Fair, Isaac

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Key Objectives of Cloud Machine Learning Market Report: Study of the annual revenues and market developments of the major players that supply Cloud Machine Learning Analysis of the demand for Cloud Machine Learning by component Assessment of future trends and growth of architecture in the Cloud Machine Learning market Assessment of the Cloud Machine Learning market with respect to the type of application Study of the market trends in various regions and countries, by component, of the Cloud Machine Learning market Study of contracts and developments related to the Cloud Machine Learning market by key players across different regions Finalization of overall market sizes by triangulating the supply-side data, which includes product developments, supply chain, and annual revenues of companies supplying Cloud Machine Learning across the globe.

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Historical year 2016-2020

Base year 2020

Forecast period 2021to 2029

Table of Content:

Cloud Machine Learning Market Research Report Chapter 1: Industry Overview Chapter 2: Analysis of Revenue by Classifications Chapter 3: Analysis of Revenue by Regions and Applications Chapter 6: Analysis of Market Revenue Market Status. Chapter 4: Analysis of Industry Key Manufacturers Chapter 5: Marketing Trader or Distributor Analysis of Market. Chapter 6: Development Trend of Cloud Machine Learning market

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Cloud Machine Learning Market: Indoor Applications Projected to be the Most Attractive Segment during 2021-2029 KSU | The Sentinel Newspaper - KSU |...

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Machine Learning in Insurance Market: Indoor Applications Projected to be the Most Attractive Segment during 2021-2029 KSU | The Sentinel Newspaper -…

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The global research report titledMachine Learning in Insurancemarket was published byMarket Research Inc. The study elucidates current market statistics, in addition to underlying future predictions of the market. The research report has been compiled by means of effective techniques such as primary and secondary research methodologies. Top level industries are enlisted in order to obtain penetrative business insights. The companies profiled in this research report include erudite information on product types, features, capacity, and productivity.

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Top key players:State Farm, Liberty Mutual, Allstate, Progressive, Accenture

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The geographical segmentation includes study of global regions such asNorth America, Latin America, Asia-Pacific, Africa, and Europe. The report also draws attention to recent advancements in technologies and certain methodologies which further help to boost the outcome of the businesses. Furthermore, it also offers a comprehensive data of cost structure such as the cost of manpower, tools, technologies, and cost of raw material. The report is an expansive source of analytical information of different business verticals such as type, size, applications, and end-users.

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Study of the annual revenues and market developments of the major players that supply Machine Learning in Insurance Analysis of the demand for Machine Learning in Insurance by component Assessment of future trends and growth of architecture in the Machine Learning in Insurance market Assessment of the Machine Learning in Insurance market with respect to the type of application Study of the market trends in various regions and countries, by component, of the Machine Learning in Insurance market Study of contracts and developments related to the Machine Learning in Insurance market by key players across different regions Finalization of overall market sizes by triangulating the supply-side data, which includes product developments, supply chain, and annual revenues of companies supplying Machine Learning in Insurance across the globe.

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Market Research Inc is farsighted in its view and covers massive ground in global research. Local or global, we keep a close check on both markets. Trends and concurrent assessments sometimes overlap and influence the other. When we say market intelligence, we mean a deep and well-informed insight into your products, market, marketing, competitors, and customers. Market research companies are leading the way in nurturing global thought leadership. We help your product/service become the best they can with our informed approach.

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Machine Learning in Insurance Market: Indoor Applications Projected to be the Most Attractive Segment during 2021-2029 KSU | The Sentinel Newspaper -...

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Carin Meier Using Machine Learning to Combat Major Illness, such as the Coronavirus –

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00:22 Introduction

00:22 Wes Reisz: Worldwide, there have been 96 million cases of the coronavirus, with over 2 million deaths attributed to the disease. In particular, places like the US, India, and Brazil have been some of the hardest areas hit. In the US alone, 400,000 people have been attributed as dying from this disease, roughly the same number of American soldiers that died in World War II. Today, I thought we'd talk about how tech is combating major diseases--such as the coronavirus. While the coronavirus certainly has our attention, it won't be the sole focus of what we talk a bit about today. We'll talk about things like cancer and heart disease. We'll also talk about some of the challenges when working with private health care data and some of the techniques and things that still need to be solved when dealing with this type of data, things like safety and ethics. We'll be talking about ways of using this data in a responsible and effective way.

01:08 Wes Reisz: Hello and welcome to the InfoQueue podcast. I'm Wes Reisz, one of the hosts for the podcast. Today's guest is Carin Meier. Carin is a data engineer at Reify Health. Reify Health develops software that accelerates the development of new and lifesaving therapies. Carin is an avid functional developer. She's a committer, PPMC member for Apache MXNet, and you've seen her keynote at places like OSCon, Strangeloop, and most recently at QCon Plus (held towards the end of last year).

01:34 Wes Reisz: The next QCon plus, which is an online version of the QCon you know and love, will be taking place over two weeks between May 17th and 28 of 2021. QCon Plus focuses on emerging software trends and practices from the world's most innovative software shops. All 16 tracks are curated by domain experts to help you focus on the topics that matter the most in software today. Tracks include leading full-cycle engineering teams, modern data pipelines, and continuous delivery: workflows and platforms. You'll learn new ideas. You'll learn new insights from over 80 software practitioners and innovator/early adopter companies, all across software. Spaced over two weeks, just a few hours a day, these expert-level technical talks provide real time interactive sessions, regular sessions, async learning, and additional workshops to help you validate your software roadmap. If you're a senior software engineer, architect or team lead and want to take your technical learning and personal development to a whole new level this year, join us at QCon plus this May 17th to 28th. You can visit for more info.

02:34 Wes Reisz: With that, let's jump in. Carin, thank you for joining us on the podcast.

02:37 Carin Meier: Thank you for having me. I'm excited to be here.

02:40 Wes Reisz: Yeah. I'm excited to work this out. I thought we jumped right in and start with Apache MXNet. It seems like a good way to bridge right into this topic. As way of an introduction, you're committer PPMC on the project. What is it? What is the MXNet?

02:53 Carin Meier: Yeah, so Apache MXNet is a machine learning library, and we're all very familiar with that. The thing that I really enjoy about it is that it's an Apache model. I was able to come there as just an interested party wanting to use this and realizing there was a gap. There were no Clojure bindings as a language for the library. I was able to get involved and commit and contribute that binding so I could bring the Clojure community to it. Then also help cross-pollinate ideas between the functional communities and the regular view Python developers. Just regularly, I think that the Apache model is a great one for openness across not only different programming languages but across different cultures and different nations in the world. I think it's a great place.

03:47 Wes Reisz: There's a bunch of deep learning libraries, for example, out there. What is Apache MXNet's focus?

03:52 Carin Meier: It's incubating, so it's not fully graduated. I'll put that in there. That's something that you've always got to say until you graduate that you're incubating Apache. The focus is ... It's a full-fledged machine learning library. You can do deep neural networks with it, but it really focuses on being efficient and fast, as opposed to some of the other ones.

04:13 Wes Reisz: On this podcast, I wanted to see how tech in particular ML and AI is affecting and just being involved with the fight against the coronavirus. That was the original premise. What are you seeing in the machine learning space as ways that the disease is being combated?

04:30 Carin Meier: Yeah, everybody knows where we are in the pandemic. It's like a big spotlight has been shown on this area. I think it's to great effect that we've seen great strides with the Google AlphaFold and just many people using machine learning to generate possible solutions that we've come up with with our vaccines, which is fantastic. Also, in all the little supporting ways. There's been machine learning applied to just about every other way that you can accelerate, looking at the results of the trials and results, using machine learning to sift through papers, to find possible correlations between symptoms and bring stuff to the forefront that we couldn't discover in any sort of timely fashion. Then of course, you think about the machine learning and just every other supporting way. Amazon could still ship my things to me, even though the whole supply chain had been disrupted. There's ways that we can definitely point to we have a vaccine now, but just everything that's supporting that and accelerating us. How Zoom was able to scale, how schools were able to be able to move to online learning.

05:47 Carin Meier: All of that has been accelerated in ways that we just can't count by these techniques and our technology today.

05:55 Wes Reisz: Talking about Zoom, I was at a friend's, and they have a son. I think he's six years old, and (I was there for just a few minutes, keeping my social distancing, of course) off at the table, I could see him with eight other six-year-olds on the Zoom meeting. It was just the craziest thing to watch a group of six-year-olds doing a reading exercise or a writing exercise on Zoom. It's just amazing how the whole human experience had to change with this pandemic.

06:21 Carin Meier: Yeah. We're in it. Machine learning is around us so much now that it's like water and air.

06:27 Wes Reisz: Yeah, totally. Are there any specific cases? I know you can't mention very specifics, but are there any specific cases that you can point to, that we can talk about?

06:36 Carin Meier: There is a CORD-19 Open Research Dataset that the Semantic Scholar team at the Allen Institute for AI. They developed to partner with the global research community by accelerating finding insights in all the related published papers. That was an interesting one. The one that I'm interested in right now is... We can talk about it later, but it was from Google AI. It's a paper that came out when they're talking about bringing concept-model explanations for Electronic Health Records. Actually, there's been all sorts of ... We'll get into this later about how to make machine learning more trustworthy and reliable, but there's been exciting breakthroughs in that area as well.

07:21 Wes Reisz: I looked at when we were collaborating on some notes, this was one of the ones that was down there, but what is a concept-based model explanation? I went through there and checked a little bit at it, but I guess I didn't quite follow what is exactly meant by concept-based model explanations?

07:36 Carin Meier: I guess they always have abbreviations from this. That's TCAV, and this is out from Google, of course, doing a lot of great research in this area. It's bridging the gap between interpretability. In your traditional models, you'd have this person who has high blood pressure. We could point to all the little factors and then follow them through in a big decision tree. If/then. Here you get the answer at the end. You could really point the way and follow it like a ball through a maze to the end.

08:11 Carin Meier: In these deep learning models, of course, you've just got this huge black box full of billions or trillions of connections. You ask when you get the model out at the end, how could you possibly get to this answer? This approach, as I understand it has these concepts like high blood pressure, being an additional concept vector that's added to the input that then makes it easier to interpret it and be able to follow through those decisions. It's an approach to the interpretability to vectorize it and blended in to almost like a symbolic blend, but people would probably argue with that.

08:55 Wes Reisz: It's using the domain to actually explain the model itself right?

08:59 Carin Meier: Right.

09:01 Wes Reisz: I mentioned in the intro that you work at Reify Health. What are some of the things that you all are doing there?

09:05 Carin Meier: Yeah. Reify Health, we focus on a particular bottleneck to the clinical trial industry. We're all very interested in how fast things get through clinical trials and not only the COVID vaccines but lifesaving cancer therapies for breast cancer, all sorts of horrible diseases. There's potential lifesaving treatments out there. The faster we can get it through clinical trials and understand if they're going to work or not, the better for everybody. Our company works on the particular bottleneck of enrollment. Before you get the trial and try the drugs on the patients, this is actually getting enough people enrolled in the trial.

09:52 Carin Meier: There's a lot of opportunity in speeding up that whole process and making it more effective so you can get the trial actually going. That's where we put all our resources. Right now, our team is building out a data pipeline, which is interesting in itself and the healthcare domain, because you have a lot of data privacy and sensitive information. Then you have of course, different countries involved that have different rules about things. Being able to use that data and route it and protect it and being able to leverage it in an analytical fashion with machine learning ... There's a lot of interesting technical challenges. That's where we are. We're working with accelerating enrollment in this area.

10:39 Wes Reisz: As you were talking a bit about the concept-based model explanations and some of the challenges like regions with data, particularly in cases like things like GDPR, there's a lot of challenges with using data in machine learning--accuracy, safety, ethics, all these kinds of things. I thought we'd shift a bit and talk about some of the challenges that exist in working with this data. Let's start off. You mentioned already explainability. The ability in simple English, rather than just weighted numbers through thousands of ... This Plinko board going through, building a machine learning model, but in a way that you can explain it, maybe not simple English, but the domain of the business, be able to explain how a decision is made. Why is that a problem? Why has that traditionally been a problem with deep learning machine learning?

11:26 Carin Meier: I think it's just the scale. We've got random forests too, that might have this problem as you get to scale as well. I think it's anything where you get beyond somebody being able to sit down and look at a computer programming model or a flow sheet, or however, you want to describe it. Being not able to fully understand how a computer program got to the answer. Certainly with the deep learning models where you've got everything vectorized, you've got nonlinearity flowing through huge parameters and you get to the end, and it says, hey, that's a cat.

12:06 Wes Reisz: The way that I like to always envision it is back in my software experience, I remember building rules engines. Rules engines, you could retrace the path and be able to say because of this decision, we had the next decision, we had the next decision. Those were great and we built them larger and larger and larger. Then all of a sudden convolutional neural networks came along and we could replace this massive rules engine with all these different, again, Plinko boards on how things bounce through the system with something like the convolutional neural network, which was great. It was a lot less code and it was a lot easier to manage from the rules engine, but how it got to that result was lost. The things like what you talked about with explainability with that concept-based model explanation seemed like a way of addressing that. It's not just a nice to have anymore. It's legally required by things like GDPR in the European Union.

13:00 Carin Meier: There's a great conference that goes on every year called NeurIPS. They just had a really great tutorial on interpretability and on these machine learning models. That's actually free out there. I encourage everybody to go out there, especially if you're using machine learning models and interested in this. They went into ... Basically with simple models ... Like you said, with the rules engine, you can trace it through, but once something gets big enough that you can't, you have to move to a post-hoc explainability. You can't trace it from the beginning. You can only look afterward with a percentage. This is why it did what it did. You can see this, they have some nice tools out there, especially with text-based models. When you have a snippet of text and you ask it a question based on that, like who was the President in year X, then it'll light up the highlighted words of how relevant each word was to the answer that it derives. That's post-hoc explainability.

14:03 Carin Meier: You can look afterward and say, this word doesn't look like it's quite right. Then of course you'd have to go through the whole bother of trying to debug it. That's a whole different thing, if you didn't like the answer that it got. It's interesting. If you have that insight into seeing how the model is working, then you can start to address other balances like accuracy and safety. How accurate do you need it to solve your problem? Maybe a machine learning model isn't even worth it to you, if you don't need to be that accurate that you don't need that trade-off. If you do need that accuracy, how can you safely use it? If you have an explanation, can you insert human into the process and have them double-check the answer? I think going down to the core of this, we have wonderful tool machine learning, but it definitely doesn't replace thinking. Thinking just pushes to a broader picture of how can you incorporate this in this process? Do you need to incorporate this in the process? Do you understand your problem? What is your problem? That's the hard stuff.

15:19 Wes Reisz: I like that bit about human and the AI loop because I think a lot of times people think about AI and machine learning is just making all these decisions. Certainly, they do, but in many cases, it's augmenting a human's ability to, I guess, react on data more appropriately. I can remember talking about Stitch Fix, for example. Stitch Fix, it's not in the healthcare space, but it does clothing recommendations for people. There's still an individual there. They use machine learning extensively to give recommended sets of clothes and patterns of things to a person who then makes that final recommendation to the subscriber and the person. I think that's a really good way of thinking about how machine learning and AI is being used. It helps, it augments the person's ability to get to a set of data where the real decision can be made faster, I think.

16:11 Carin Meier: Exactly. I think the analogy earlier on was, we want machine learning to be like an Iron Man suit.

16:19 Wes Reisz: I like that. I like that. Yeah. Let's talk about the Iron Man suit. What are some of the challenges with creating this Iron Man suit? Things like you mentioned, accuracy, safety, you've already talked about explainability. What are some of the core challenges on being able to leverage machine learning, deep learning in this healthcare space?

16:36 Carin Meier: I think those are the key things that are holding us back trust, basically. Healthcare and the medical environment is a high-trust environment. Whatever tools that we use to leverage, we need to understand them and to be able to trust them because they're making deep impact on people's lives. The amount of trust that you need to pick a sweater for a person is not the amount of trust that you need to decide whether a person should get a life-saving treatment or not. Google and other big companies are tackling this problem. We need to find ways that we can make sure that privacy on the individual level is being preserved on these models, that they're explainable in some way that we can trust them, and that we can find best practices. I don't like to say best practices in a lot of ways that we can incorporate them into our businesses and our models.

17:41 Carin Meier: I'll just expand on that. The reason I don't like to say best practices it's because people use that as an excuse not to think. They're just like, I don't need to think about this. The best practice is the way we do it. This is our purpose. Our purpose is to be here and to think about our problems and to think about the trade-offs of every solution, and come to the best possible solution. Just taking an off the box answer and saying, we can use this and not thinking about it is doing a disservice to everyone.

18:09 Wes Reisz: Yeah. It leads us into some of the problems we've heard where ML models have gone wrong, for example. That reminds me of a cartoon I remember seeing years ago about design patterns. It was before a developer hears about design patterns, after a developer hears about it, and then after they have more experience leveraging design patterns. The first one, their code's going all over the place. Then the second one everything's a design pattern. Every single design pattern that they could possibly imagine is implemented end to end. Then at the end of it, it's like, here's just some simpler code that may happen to use a pattern. Once you learn about these things... Oh, I have to try to put them everywhere. It's not always the best approach.

18:45 Carin Meier: Right. I think that's led to some of the problems that we've had with machine learning models lately.

18:51 Wes Reisz: Let's talk about safety. In particular, one in the healthcare space that I think seems like a real challenge. I remember a couple of years ago, a few years back, there was a book by Cathy O'Neil Weapons of Math Destruction, that talk about just systemic bias data and reinforcing pre-existing inequity with machine learning models. That went to things like removing things like race, for example, from data sets, when decisions are being made. In healthcare, race can be very important. People with certain ethical backgrounds may be more inclined to having certain diseases like heart disease, for example, or high blood pressure or things like that. How do you balance privacy, safety with things like race when it comes to machine learning models, when it may be important to the decision, but it's been used for reinforcing pre-existing inequity? How do you balance?

19:44 Carin Meier: I think the first step is recognizing that there is a problem with this, and then you have to approach it carefully. Luckily now, it's been circulated that there is a problem in datasets. Just because you put it all in a model doesn't mean that the answer is perfectly free of human bias because we fed it this data. Data in, data out. It doesn't go away just because it's a machine learning model, that's our core truth. Making sure that your data is a good representative set to begin with is your fundamental thing. Of course, with sensitive data like race and ethnicity, you have the additional thing of this has got individuals' very sensitive data that you need to protect this. This is ways that differential privacy... If people haven't heard of it. It's a technique that you can protect an individual person's information while still gathering statistical insights on the whole. You can still get the core learnings that you need, but without compromising the individual's privacy.

20:53 Wes Reisz: The way that I understand differential privacy and correct me if I'm wrong cause I'm sure it's not accurate, but it's like rather than showing someone in individual, you show it in an aggregated set? That way, the privacy of the individual is respected, but the data is still presented. Is that accurate?

21:08 Carin Meier: It's got more math behind it. I'm not a math expert, but it's statistical fuzzing method I guess, is an appropriate way to think about it. There's also ways that you can use this in training the deep learning models in a distributed fashion as well. That way, the machine learning model is trained on that fuzzed data itself. The individual data never actually reaches the final model, which is an important thing as well. I don't want to get too far down in the differential privacy, but that's another technique that's used to be able to safely extract insights into race and ethnicity. That is an important component to making sure that it is not biased. Again, so then there's another process at the end, evaluating your model. Does your model have any bias in either direction? It's all throughout the process. It's at the beginning, looking at your data coming in, how you actually train the model, how you evaluate the model, and then a circular feedback loop. Let's get humans in it and make sure that it's doing the things that we want it to do in a safe manner.

22:24 Wes Reisz: Tying back to what we were talking about, that human in the AI loop before. I think what I've just heard is it's important for humans to audit the decisions that are coming out to make sure that they make sense. Is that accurate?

22:35 Carin Meier: Yeah. Just like any sort of code. You need to test it to make sure that your code is right. That's at the lower levels of, did it actually get the right answer that you wanted. Is the model accurate? Then the higher level, is it trustworthy? Can you explain how did he get this answer? Why did it say that this person should get this treatment? Then how do we make sure that that isn't biased? That's another question. It goes up and up in scope, and how do we safely incorporate this into our business practice? What happens if it's wrong? I think that's one of the reasons why so many people are attracted to this area because the problems are tough and they're important, and they're changing. A lot of people are attracted to computer science in our industry because we like solving problems, and there's a lot of problems to solve in this domain that directly impact everyone.

23:29 Wes Reisz: We seem only to be creating more problems with our society that have to be solved. We talked about privacy, we talked about explainability, we talked about safety, but one we haven't talked about is ethics. Just because we can doesn't mean we should. What are some of the ethical questions that at the heart of machine learning today, deep learning today?

23:48 Carin Meier: Wow. Yeah.

23:51 Wes Reisz: I don't know how I'd answer that question, so go.

23:55 Carin Meier: I think you ask broad questions, you can get broad answers.

24:00 Wes Reisz: Good response. As soon as I said it, I thought that was an unfair question to ask.

24:04 Carin Meier: The answer is, should we?

24:05 Wes Reisz: It depends. Maybe.

24:09 Carin Meier: That's the thing. There was a good example of it in the news. I think it was in England when the whole pandemic hit and people couldn't take their end exams. I think they just said, let's just put a machine learning model on all your prior test exams, and we'll just predict what you would have gotten on this test.

24:29 Wes Reisz: That's a great idea.

24:32 Carin Meier: It's pretty much the same as what you would have gotten. So what? You can't go to college now.

24:40 Wes Reisz: Yeah. I definitely would not have gone to college under that arrangement.

24:44 Carin Meier: Yes, in that case, you can, and we did, but should we, sort of thing.

24:51 Wes Reisz: Do no harm. I think that's a good answer. That's the best way to end it.

24:55 Carin Meier: I know people at various points, people are always like, "Computer science people should be a guild. We should have an ethics statement just like doctors." It's interesting not even getting into whether we should have a guild, but if we had an ethics statement, like the Hippocratic Oath for doctors, what would it be? What would our oath be?

25:16 Wes Reisz: There's a lot of discipline. There's a lot of fields that are in machine learning. I think that there's a perception that to be involved with these data pipelines that do the work that you're doing requires a PhD to be able to... Is that true? Does it require a PhD to be able to get involved and contribute in a meaningful way to things like deep learning solutions to the coronavirus?

25:38 Carin Meier: I would say definitely not. PhDs are helpful. If you have a PhD, please come and help us, but it's not required. I think data engineering as a field is one of the fastest-growing fields, just because we need good engineers. We need good engineers to build out our pipelines and to apply engineering practice to building models, maintaining them. The whole of our software industry has trained us to do, and we need it applied to this. Also we need just curious people generally to innovate. I think you were saying before about design patterns. Once you learn about design patterns, everything's design patterns until... I think a lot of that is with deep learning and deep learning models right now. We've got one dominant model and one dominant way that we're thinking about intelligence. That's not necessarily the best or the only way. We need more people to come to this with curious minds, bring their backgrounds, whether it's philosophy, whether it's game development, whatever it is, so we, as humanity, can press forward and look at all these different solutions and find the best ones.

26:55 Wes Reisz: Absolutely. I come at it from a web developer background. I'm a Java developer who comes from a web environment. These models still have to run. It still takes someone to be able to take that machine learning model, wrap it into a service and be able to operationalize it into a platform. There's so many roles that are needed to be able to tackle the problems that machine learning can help solve. We're at the very beginning of the year, 2020 is in our rearview mirror, thankfully. What do you hope that we're going to solve in 2021? What do you think are the big things we're set to solve?

27:29 Carin Meier: I'm going to bring it down to the scope of machine learning.

27:32 Wes Reisz: Yeah, there you go. Sorry. Let me qualify that. What are some of the things in the machine learning and deep learning space that you think we're poised to solve in 21?

27:39 Carin Meier: Trust. Building trust in these techniques and in the models so we can use them responsibly and effectively in our healthcare and other areas that we need high trust. That's a big, big gap right now for us.

27:57 Wes Reisz: Carin, thank you so much. I think we've been working on this since the end of last year. Thank you for working on this with me through the holidays and into the New Year. It was fun to sit down and chat finally.

28:07 Carin Meier: Thanks again.

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Carin Meier Using Machine Learning to Combat Major Illness, such as the Coronavirus -

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Moffitt Cancer Center: Why we are building the first machine learning department in oncology – The Cancer Letter

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publication date: Feb. 19, 2021

Issam El Naqa, PhD

Chair, Department of Machine Learning,

Moffitt Cancer Center

Dana Rollison, PhD

Vice president, chief data officer,

Associate center director of data science,

Division chief, quantitative science

Moffitt Cancer Center

Imagine a future where cancer patients are able to receive the treatment they precisely need and not the treatment that is prescribed to all.

Personalized medicine has advanced over recent decades, but is still limited by the ability to navigate the complex landscape of patient information and tumor-specific parameters that need to be incorporated into optimizing treatment decisions.

Eliminating cancer mortality will only be possible through cancer prevention and treatment tailored not only to molecular aspects of a (pre-)malignancy, but also to the microenvironment, genetics and physiology of the patient, lifestyle factors, environmental exposures, social determinants of the patients health and the dynamic changes of these parameters over time.

Such a future is possible at the intersection of recent advances in computer engineering, data science and cancer research.

Conventional approaches to unraveling the complexity of tumor biology have historically focused on simple factors operating within a single scale. Advances in imaging and biotechnology have Continue reading Moffitt Cancer Center: Why we are building the first machine learning department in oncology

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Moffitt Cancer Center: Why we are building the first machine learning department in oncology - The Cancer Letter

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Machine Learning and where is it used? – Tech Guide

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Lately, there has been a lot of discussion about the direction in which artificial intelligence is going and how much good and bad it brings to us as humans. It is questionable if machine learning will, eventually, reach a stage where human minds might become obsolete.

But, there is one thing that we can say with certainty; there has been an exponential growth regarding artificial intelligence in the last couple of years. And, one of the main aspects of said intelligence is machine learning.

Machine learning can be found in many things today:

And, it is always with us. If you have ever allowed cookies on your device when visiting a website you have fed some sort of AI to engage in machine learning. For such a reason, this technology is bound to improve exponentially in the future.

What is Machine Learning?

Machine Learning is a subcategory of artificial intelligence whose goal is that the system itself. It is meant to recognize patterns, learn as much as possible from data, and do all that and provide solutions with minimal human intervention.

Like with everything else in life, there are certain steps that must be followed in order to perform a particular task. When it comes to this form of artificial intelligence, there are 5 basic steps:

And, unlike with human learning, the computer will cycle the steps tirelessly, providing sets of data for developers to know how to tweak performance or introduce new data.

Regretfully, without human input of contextualizing data and forming algorithms the AI itself wont know what to do with the resources it has gathered.

Forms of Machine Learning?

Generally, there are 2 main forms of machine learning.

Primarily, there is supervised learning. This is a subcategory of ML where the main goal for the algorithm is to provide adequate solutions for certain data. Most types of service software use this form to improve customer experience.

Then, there is unsupervised learning, where the algorithm is provided with only data and with no solutions. Such a system doesnt yet have direct market applications but is used to research AI and to understand how something can be developed natively.

Recommender Systems

This system functions by learning and collecting data, preferences, and interests of individual users, and thus offers services, ads, or products that are similar to what we have apparently liked before. Such automated action is saving us a lot of time and nerves, or at least is meant to.

For instance, Netflix is universally one of the most popular applications in the world for watching TV shows and movies. So if you are a user of this application, you have definitely seen a category called recommendations.

This category basically recommends you a variety of movies and shows that you would possibly enjoy based on what types of movies or shows you watched and liked. previously

Similarly, in the musical field ML is integrated by offering us new choices based on the genre we listened to before. With that information, it can recommend new songs and even entire playlists and mixes which we would potentially adore.

Another thing that we are all familiar with are games. Whether you are 7 or 77 years old, games will never go out of style.

Online gaming has been on such a rise lately and is one of the top activities in free time. So lets say you played wolfs treasure for a really long time. The machine learning algorithm will pick up on this and will recommend games of similar feature sets and volatility levels.

How is ML Present in Our Everyday Life?

There is no denying that artificial intelligence is basically everywhere and is one of the biggest innovations that made our lives better, happier, and more productive. Using machine learning is very popular in custom software development, as well as in a wide range of service software.

It is almost impossible to divide our smartphones from machine learning as it seems to be ever-present. We can find traces of the system from simple features like alarms and messaging, to very complex apps like navigation and entertainment recommendations.

Additionally, AI is also integral to both device and cybersecurity as well as for the use of AI assistants. Without the software knowing who we are and how we sound most wont be able to unlock our phones, let alone tell Siri to find us a burger joint.

With that being said, the chance that we use it in almost every aspect of our daily life without even realizing it is undeniable.

Stephen is the Tech Guide editor and one of Australia's most respected tech journalists. He is a regular on radio and TV talking about the latest tech news, products and trends.

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Machine Learning and where is it used? - Tech Guide

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Artificial Intelligence and Machine Learning for Insurance Technology from Johnson Controls Available on the Ocean Tomo Bid-Ask Market – Yahoo Finance

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There are two types of dividend stocks: those that increase their annual dividend payments year after year, often referred to as Dividend Aristocrats, and those that grow their annual dividends by double-digit percentages every year. In early January, Rob Carrick, one of Canadas best personal finance columnists, wrote an article about dividend stocks that doubled their payouts over the past 10 years. With an assist from Tom Connolly of, theyve put together a list of stocks that have delivered 10-year annualized dividend growth of 7.2%. InvestorPlace - Stock Market News, Stock Advice & Trading Tips Why 7.2%? Thats the amount of growth youd need based on the Rule of 72 72 divided by 7.2% equals 10 the number of years growth required to double a companys dividend payout. Now the names on the list, while excellent businesses, are mostly traded exclusively on the Toronto Stock Exchange. Thus, some of them are only available over-the-counter, through a broker that has access to the TSX, or not at all. Of the 15, eight trade on a U.S. exchange. Id check them out. Masco (NYSE:MAS) Rollins (NYSE:ROL) Moelis & Company (NYSE:MC) Tractor Supply (NASDAQ:TSCO) Victory Capital (NASDAQ:VCTR) GlaxoSmithKline (NYSE:GSK) T. Rowe Price (NASDAQ:TROW) S&P Global (NYSE:SPGI) Open Text (NASDAQ:OTEX) FirstService (NASDAQ:FSV) 7 Overvalued Stocks Investors Just Dont Get Tired Of In the meantime, here are 10 dividend stocks that are likely to do the same. Dividend Stocks to Buy: Masco (MAS) Source: Africa Studio / Business has been good for the global leader in home improvement and building products whose brands include Behr Paint, Delta faucets, and Endless Pools. On Feb. 9, the Michigan-based company announced that it was increasing its annual dividend by 68% from 56 cents to 94 cents, starting with the Q2 2021 payment. Also, the company announced a new $2 billion share repurchase program effective immediately. The anticipated dividend increase weve announced today, along with the new $2B share repurchase authorization, underscores our strong financial position and the Boards confidence in our future, stated Chief Executive Officer Keith Allman. In recent years, Masco has ridden the home improvement boom to deliver a five-year annualized total return of 17.3% through Feb. 12. With a trailing 12-month (TTM) free cash flow (FCF) of $840 million, it has an FCF yield of 5.3% based on an enterprise value of $15.8 billion. Rollins (ROL) Source: Shutterstock Ive always liked Rollins, one of the worlds largest providers of pest control services. In May 2016, I included ROL in a group of 10 top stocks that ought to be in every retirement portfolio. Its up 224% since then, and that doesnt even include the dividends. Over the long haul, it hasnt disappointed delivering 18 consecutive years of earnings growth and 14 consecutive years of dividend increases averaging 12%, I wrote on May 18, 2016. In fiscal 2020, Rollins increased sales and earnings by 7.2% and 13.3%, respectively. Accounting for the 3-for-2 split on Dec. 10, 2020, Rollins board announced on Jan. 26 that it would increase its quarterly dividend by 50% over Q4 2020 to 8 cents starting with its February 2021 payment. The company paid out $161 million in dividends in 2020, up from $154 million in 2019. The company repurchases very little of its stock. Between 2017 and 2019, it repurchased just $28 million of its shares, opting to use most of its free cash for dividends and acquisitions. Rollins has a trailing 12-month free cash flow (FCF) of $380 million. That works out to an FCF yield of 2% based on an enterprise value of $18.8 billion. Its not cheap at current prices, but it will deliver an above-average total return [dividend income plus capital appreciation] over time. 7 Blue-Chip Stocks That Arent a Gamble Definitely buy this one on the dips. Moelis & Company (MC) Source: PopTika/ Moelis & Company is an independent investment bank based in New York City that went public in April 2014 at $25 a share. If you bought some of its initial public offering (IPO) and still hold it today, youre sitting on a 119% return. There is no question the investment bank has had its ups and downs. In June 2018, it flirted with $70 before falling gradually to its 52-week low of $22.11 during the March 2020 correction. On Feb. 10, 2021, the company reported record Q4 2020 revenues of $422 million, up 89% from a year earlier. On the bottom line, its adjusted net income was $146 million, up considerably from $26 million in Q4 2019. In 2020, the company paid out dividends and executed share repurchases totaling almost $275 million. It included a $2 a share special dividend paid out in December while also increasing the regular quarterly dividend by 44% from the previous quarter and 8% from pre-Covid-19 levels. Tractor Supply (TSCO) Source: James R. Martin/ Tractor Supply is one of my all-time favorite companies, retail or otherwise. Its business model servicing the rural lifestyle makes abundant sense. In 2001, Tractor Supply made a transformative acquisition, acquiring bankrupt Michigan-based Quality Stores, a competitor with 85 stores at the time. It had 323 stores. Today, it has almost 2,000. It hasnt been as lucky with another acquisition it made. In 2016, it acquired Petsense, a retailer of pet supplies, for $116 million. Petsense had 136 stores at the time. In Q4 2020, the company had non-cash pre-tax impairment charges of $74.1 million related to its Petsense operations. As a result of the charge, Tractor Supplys operating income in the fourth quarter was $184.5 million, 3.1% lower than a year earlier. On the plus side, fourth-quarter sales were 31.3% higher over Q4 2019. The board announced on Jan. 28 that it would increase its quarterly dividend by 30% to 52 cents a share. 7 Stocks That Are Ready for a Retail Rebound Tractor Supply is an outstanding retail stock to own for the long haul. Victory Capital (VCTR) Source: kan_chana/ I thought Victory Capital was the only firm on my list of 10 dividend stocks that Im unfamiliar with. Then it dawned on me that its the company behind VictoryShares and ETFs such as the VictoryShares Nasdaq Next 50 ETF (NASDAQ:QQQN). However, if the San Antonio-based asset management firm keeps delivering quarterly results as it did in Q4 2020, Ill have to get a lot more acquainted with it in a real hurry. The company finished fiscal 2020 with $136.4 billion in assets under management (AUM), 33% higher than a year earlier. A part of the increase was due to its 2019 acquisition of USAA Asset Management. On the bottom line, Victory generated record adjusted net income of $285.5 million in 2020, 48% higher than a year earlier. As part of the Feb. 10 press release of its fourth-quarter earnings, Victory Capitals board announced a 29% increase in its quarterly dividend to 9 cents a share. It is the companys third increase in a year. GlaxoSmithKline (GSK) Source: Willy Barton / Anyone who suffers from back or joint pain is likely familiar with Voltaren, one of GlaxoSmithKlines many products made by its consumer healthcare products division, merged with Pfizers (NYSE:PFE) consumer healthcare business in August 2019. It plans to separate the joint-venture into its own separate company. In addition to Voltaren, it makes Polident, Otrivin, Advil, Tums, and Centrum, and many others. Once GSK separates its consumer healthcare products business, it will focus on pharmaceuticals and vaccines. On Feb. 3, GSK reported its full-year results. They included a 3% sales increase year-over-year of 34.1 billion euros ($41.3 billion) and an FCF of 5.4 billion euros ($6.6 billion), 7% higher than in 2019. As for the dividend, its a bit of a mixed bag. Although the company increased its quarterly payment by 15% from $0.1746 a share to $0.2008 starting with the December 2020 payment, it also said that it wouldnt increase the total dividend payments in 2021 from what it paid out in 2020. These 7 Semiconductor Stocks Are Actually Winning From the Chip Shortage Ive put it on the list of dividend stocks because it should provide investors with a much better entry point to buy its stock. Its a definite value play at this point. T. Rowe Price (TROW) Source: Pavel Kapysh / One of the four financial services companies on my list of dividend stocks, the Baltimore-based company announced on Feb. 10 that it was raising its quarterly dividend by 20% to $1.08 per share, the 35th consecutive year it has done so. That makes it a Dividend Aristocrat. The $4.32 annual payout yields a reasonable 2.7%. On Jan. 28, T. Rowe Price reported Q4 2020 revenues of $1.73 billion, 18% higher than a year earlier, while adjusted earnings per share were 42% up over a year earlier. It was a good year for the investment manager in terms of asset gathering. In 2020, it had net client inflows of $5.6 billion, finishing the year with $1.47 trillion in assets under management. Its average assets under management in 2020 increased by 12.5%. The company finished 2020 debt-free with $6.2 billion in cash and investments in T.Rowe Price products. Thats up from $5.6 billion a year earlier. As long-term investments go, income investors ought to like T. Rowe Price. S&P Global (SPGI) Source: Shutterstock This isnt the first time Ive picked S&P Global as a stock to buy because of its increasing dividend. In April 2020, I picked SPGI stock along with nine other S&P 500 dividend stocks. Since then, its up 26%, a respectable, if not spectacular, return over 10 months. On Jan. 27, it announced that it was increasing its quarterly dividend by 15% to 77 cents from 67 cents. S&P Global has paid a dividend each year since 1937 and increased its annual dividend for 48 consecutive years. In 2020, SPGI returned $1.8 billion to shareholders, including $645 million for dividends and $1.16 billion in share repurchases, no mean feat during a pandemic. Increasing the dividend demonstrates our confidence and optimism in the continued strength of our cash flow generation and financial position, said Douglas L. Peterson, CEO of S&P Global. Returning cash to shareholders remains a cornerstone of our shareholder value proposition. 8 Electric Vehicle Stocks That Are More Than Just a Fad It has a very attractive FCF yield of 4.2% based on TTM FCF of $3.49 billion and an $82.34 billion enterprise value. Open Text (OTEX) Source: Shutterstock One of two Canadian companies that Ive chosen for this article, Open Text is a cloud-based software company whose products and solutions help manage and utilize their information. The last year has not been kind to shareholders. Open Text stocks generated a 52-week total return of just 2.5%, well below its software application peers, who gained 55.8% over the past year. However, its latest earnings report delivered hope. Excluding currency, the company reported recurring revenue of $674 million in Q2 2021, 19.5% higher than a year earlier. At the same time, its free cash flow was 46.5% higher to $275 million. Its free cash flow on a TTM basis is $1.07 billion for an FCF yield of 6.9% based on an enterprise value of $15.5 billion. That FCF yields approaching value territory. On Feb. 4, Open Text announced its March 2021 dividend would be $0.2008 a share, 15% higher than a year earlier. FirstService (FSV) Source: Shutterstock FirstService is the second of my Canadian picks of dividend stocks. The provider of residential property management and property services has been on my favorites list for some time. In December, I put FSV on my list of Canadian stocks to own that make money from America. On Feb. 4, FirstService announced it was increasing its quarterly dividend by 10% from $0.15 to $0.165. The annual payment of 66 cents yields a meager 0.4%. However, you wont be sorry for owning its stock. Its got a five-year annualized total return of 32.8%, almost three times the return of the U.S. markets as a whole. The dividend increase is FirstServices fifth consecutive year upping it by 10% or more. Highlights of fiscal 2020 include a 15% increase in revenues to $2.77 billion, while its adjusted earnings per share were up 15% year-over-year to $3.46. We capped off the year with a very strong fourth quarter, largely driven by organic growth, said CEO Scott Patterson. We are proud of our performance throughout 2020, demonstrating strength and stability in the face of the pandemic, and we look forward to capitalizing on our growth opportunities as the environment improves. This could be the best fly-under-the-radar dividend stock available. On the date of publication, Will Ashworth did not have (either directly or indirectly) any positions in the securities mentioned in this article. Will Ashworth has written about investments full-time since 2008. Publications where hes appeared include InvestorPlace, The Motley Fool Canada, Investopedia, Kiplinger, and several others in both the U.S. and Canada. He particularly enjoys creating model portfolios that stand the test of time. He lives in Halifax, Nova Scotia. At the time of this writing Will Ashworth did not hold a position in any of the aforementioned securities. More From InvestorPlace Why Everyone Is Investing in 5G All WRONG Top Stock Picker Reveals His Next Potential Winner It doesnt matter if you have $500 in savings or $5 million. Do this now. #1 Play to Profit from Biden's Presidency The post 10 Dividend Stocks Increasing Their Payouts appeared first on InvestorPlace.

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Artificial Intelligence and Machine Learning for Insurance Technology from Johnson Controls Available on the Ocean Tomo Bid-Ask Market - Yahoo Finance

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Identifying COVID-19 Therapy Candidates With Machine Learning –

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Study pinpoints the protein RIPK1 as a promising target for SARS-CoV-2 treatment.

Investigators from the Massachusetts Institute of Technology, in collaboration with Harvard University and ETH Zurich, have developed a machine learning-based approach that can identify therapies that are already on the market that have potential for repurposing to help fight the coronavirus disease 2019 (COVID-19). Results from the study were published in the journal Nature Communications.

As the COVID-19 pandemic continues to surge across the globe and investigators rush to find treatments, the information provided from the approach may have a significant impact.

The target population for the study is the elderly, as the virus impacts them more severely than younger populations. The approach accounts for gene expression changes in lung cells caused by COVID-19 as well as aging. The hope is that this would allow medical experts to find therapies for clinical testing faster.

"Earlier work by the Shivashankar lab showed that if you stimulate cells on a stiffer substrate with a cytokine, similar to what the virus does, they actually turn on different genes," Caroline Uhler, a computational biologist in MIT's Department of Electrical Engineering and Computer Science and the Institute for Data, Systems and Society, and an associate member of the Broad Institute of MIT and Harvard said. "So, that motivated this hypothesis. We need to look at aging together with SARS-CoV-2 -- what are the genes at the intersection of these two pathways?"

The investigators took 3 steps to identify the most promising candidates for repurposing. They first generated a large list of possible candidates using the machine-learning technology and then mapped the genes and proteins involved in the aging process and in a SARS-CoV-2 infection. They then employed algorithms to pinpoint genes that caused cascading effects through the mapped network which narrowed the list of therapies. The overlap caused by the 2 maps is where the team found the precise gene expression network of therapies that would target COVID-19.

The team plans to share the findings with pharmaceutical companies to aid in finding more therapies that can be repurposed for COVID-19. However, they emphasize that any of the therapies identified must undergo clinical testing before they can be approved for use in elderly populations.

"Making new drugs takes forever," Uhler said. "Really, the only expedient option is to repurpose existing drugs."


Identifying COVID-19 Therapy Candidates With Machine Learning -

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