Riiid claims students can increase their scores by 20 percent or more with just 20 hours of study. It has already incorporated machine-learning algorithms into its program to prepare students for English-language proficiency tests and has introduced test prep programs for the SAT. It expects to enter the United States in 2020.

Still more transformational applications are being developed that could revolutionize education altogether. Acuitus, a Silicon Valley start-up, has drawn on lessons learned over the past 50 years in education cognitive psychology, social psychology, computer science, linguistics and artificial intelligence to create a digital tutor that it claims can train experts in months rather than years.

Acuituss system was originally funded by the Defense Departments Defense Advanced Research Projects Agency for training Navy information technology specialists. John Newkirk, the companys co-founder and chief executive, said Acuitus focused on teaching concepts and understanding.

The company has taught nearly 1,000 students with its course on information technology and is in the prototype stage for a system that will teach algebra. Dr. Newkirk said the underlying A.I. technology was content-agnostic and could be used to teach the full range of STEM subjects.

Dr. Newkirk likens A.I.-powered education today to the Wright brothers early exhibition flights proof that it can be done, but far from what it will be a decade or two from now.

The world will still need schools, classrooms and teachers to motivate students and to teach social skills, teamwork and soft subjects like art, music and sports. The challenge for A.I.-aided learning, some people say, is not the technology, but bureaucratic barriers that protect the status quo.

There are gatekeepers at every step, said Dr. Sejnowski, who together with Barbara Oakley, a computer-science engineer at Michigans Oakland University, created a massive open online course, or MOOC, called Learning How to Learn.

He said that by using machine-learning systems and the internet, new education technology would bypass the gatekeepers and go directly to students in their homes. Parents are figuring out that they can get much better educational lessons for their kids through the internet than theyre getting at school, he said.

Craig S. Smith is a former correspondent for The Times and hosts the podcast Eye on A.I.

See the original post here:

The Machines Are Learning, and So Are the Students - The New York Times

QStride Inc., a Detroit-based provider of IT staffing, struck a deal to be acquired by Drisla Inc., a provider of consulting in blockchain, analytics, machine learning and artificial intelligence.

Drisla is based in Hyderabad, India, with a US office in Princeton, New Jersey.

In the deal, Drisla will acquire QStrides assets and customer contacts; it intends to fund the transaction with cash and debt financing.

QStride founder and CEO Shane Gianino said the deal represents an important building block for the company. It will also allow us to better serve our customers in the tristate area of New York, New Jersey, Connecticut, and Pennsylvania, where we already have a client base, Gianino said.

Gianino will remain with the company as operating CEO and equity shareholder and the company will continue operating under the QStride brand.

QStride was founded in April 2012 in Troy, Michigan, and reported it reached revenue of $1.5 million the following year.

Shane and his team have done a great job over the years positioning QStride for long-term success, said Pavan Kuchana, Drisla founder and CEO. Their niche in business intelligence, analytics, data warehousing and software engineering align very well with our expertise in innovative technology offerings such as AI, ML, and blockchain.

The rest is here:

QStride to be acquired by India-based blockchain, analytics, machine learning consultancy - Staffing Industry Analysts

Although technology has come a long way in recent years, not least in terms of the immense power and resources available through cloud computing services let alone the vast amount of data that can be allocated to cloud storage, computers and machines still cant match the power of the human brain.

What makes humans so unique is that we can learn as we go, drawing on our memories and experiences. That means taking in data from the world around us and forming ideas about how to optimally perform tasks or understand new information.

Deep learning, which is a branch of artificial intelligence, aims to replicate our ability to learn and evolve in machines. At the end of the day, deep learning allows computers to take in new information, decipher it, and produce an outputall without humans needing to be involved in the process. This field has enormous implications for the technologies of the future, including self-driving vehicles, facial recognition software, personalized medicine, and much more.

The end goal of deep learning is to teach a computer how, given a set of unstructured data, to recognize patterns. A simple example of unstructured data is an image of a real-world scene, in which things like the sky, trees, and people arent marked for the computer by a human supervisor. An algorithm trained by deep learning should be able to identify those individual components. That is, it should be able to tell you which pixels in the image make up a person, which make up a tree, and which are part of the sky.

On a broader scale, this capacity for pattern recognition can be applied to almost anything. For example, in a self-driving car, the computer should be able to recognize a stop sign and then trigger the car to stop appropriately. In medicine, a deep learning algorithm should be able to look at a microscope image of cells and decide if those cells are cancerous are not.

Deep learning has essentially the same goal as machine learning, which plays an increasingly large role in modern technology. However, machine learning is limited in how much data it can take in. It may be good at recognizing features in a set of images, for example, but machine learning doesnt have the capacity to adapt to a 3-dimensional scene like a self-driving car must be able to do.

Deep learning, on the other hand, offers a virtually unlimited capacity for learning that could theoretically exceed the capacity of the human brain someday. Thats because of the family of algorithms that underlie deep learning, known as neural networks.

Neural networks are so-named because they essentially aim to mimic the functioning of neurons in the human brain. These networks are made up of three layers of digital neurons: the input layer, the hidden layer, and the output layer.

The input layer is a series of digital neurons that see the information the computer is being given. One neuron might fire when the color green is present in an image, for example, while another might fire when a particular shape is present. There can be thousands of input layer neurons, each firing when it sees a specific characteristic in the data.

The output layer tells the computer what to do in response to the input data. In a self-driving car, these would be the digital neurons that ultimately tell the computer to accelerate, brake, or turn.

The real magic of a neural network happens in the hidden layer. This layer takes the neuron firings from the input layer and redirects them to fire the appropriate output layer neurons. The hidden layer consists of thousands or millions of individual rows of neurons, each of which is connected to all of its neighbors within the network.

Training a deep learning model involves feeding the model an image, pattern, or situation for which the desired model output is already known. During training, each connection from one neuron to another is strengthened or weakened based on how close the networks actual output is to the intended output. If it was very closeour self-driving car stopped at the stop signthe connections might not change much at all. But if the model result is far from the intended result, the connections between neurons are tweaked slightly.

Doing this millions of times allows the network to strengthen connections that do a good job of producing the desired model output and weakening connections that throw off the model results. The final model, then, has learned how to take in new data, recognize patterns, and produce the desired outcome based on those patterns without human supervision.

Deep learning holds a lot of promise for new automated technologies. Self-driving cars are perhaps the most prominent potential use of deep learning algorithms, but there are far more applications in the business world and beyond.

For example, deep learning could have major implications for the finance industry. Banks could use deep learning to help protect your online accounts by teaching a model to determine whether your latest sign-in attempt is similar to your usual sign-ins. Or, banks can apply deep learning algorithms to better pick up on fraudulent activities like money laundering. Yet another possibility is that banks and investment firms use deep learning to predict when stock prices are about to go up or down.

Another application of deep learning technology is facial recognition. For facial recognition to work on a wide scale, the computer needs to be able to recognize you whether you get a haircut or a tan, or put on makeup. A deep learning algorithm trained on images of your face would allow facial recognition software to recognize you no matter what you look like on a given day, while keeping others out of your accounts.

Interestingly, deep learning can also help scientists predict earthquakes and other natural disasters. In earthquake-prone areas, the ground is almost always trembling a little bit. Deep learning models can be trained on what kind of shaking patterns preceded earthquakes in the pastand then sound the alarm when these same patterns are detected in the future.

As deep learning technology continues to improve, the list of potential applications is only likely to get longer and more impressive. We may be able to teach computers to recognize patterns, but human creativity will be essential in figuring out how best to put deep learning to work for society.

See more here:

What is Deep Learning? Everything you need to know - TechRadar

Blackberry Limited Chairman & CEO John Chen, right, watches as company employees take pictures with ... [+] their phones after Chen rang the opening bell to mark his company's stock transfer from Nasdaq to the New York Stock Exchange, Monday, Oct. 16, 2017. (AP Photo/Richard Drew)

The markets have largely remained divided on BlackBerry stock. While the companys revenues have declined sharply over the last few years, driven by its exit from the smartphone business and the decline of its lucrative BlackBerry services business, it has been making multiple bets on high-growth areas ranging from cybersecurity to automotive software, although they have yet to pay off. This uncertainty relating to BlackBerrys future has caused the stock to remain very volatile.

Considering the significant price movements, we began with a simple question that investors could be asking about BlackBerrys stock: given a certain drop or rise, say a 10% drop in a week, what should we expect for the next week? Is it very likely that the stock will recover the next week? What about the next month or a quarter? You can test a variety of scenarios on the Trefis Machine Learning Engine to calculate if the BlackBerry stock dropped, whats the chance itll rise.

For example, if BlackBerry Stock drops 10% or more in a week (5 trading days), there is a 27% chance itll recover 10% or more, over the next month (about 20 trading days). On the other hand, after a 5% drop over a week (5 trading days), the Trefis machine learning engine says chances of an additional 5% drop over the next month, are about 36%. This is quite significant, and helpful to know for someone trying to recover from a loss. Knowing what to expect for almost any scenario is powerful. It can help you avoid rash moves.

Below, we also discuss a few scenarios and answer common investor questions:

Question 1: Does a rise in BlackBerry stock become more likely after a drop?

Answer:

The chances of a 5% rise in BlackBerry stock over the next month:

= 37%% after BlackBerry stock drops by 5% in a week

versus,

= 41% after BlackBerry stock rises by 5% in a week

Question 2: What about the other way around, does a drop in BlackBerry stock become more likely after a rise?

Answer:

Consider two cases

Case 1: BlackBerry stock drops by 5% in a week

Case 2: BlackBerry stock rises by 5% in a week

Turns out the chances of a 5% drop after Case 1 or Case 2 has occurred, is actually quite similar, both pretty close to 35%.

Question 3: Does patience pay?

Answer:

According to data and Trefis machine learning engines calculations, only to an extent.

Given a drop of 5% in BlackBerry stock over a week (5 trading days), while there is only about 24% chance the BlackBerry stock will gain 5% over the subsequent week, there is a 45% chance this will happen in 6 months, and 41% chance itll gain 5% over a year (about 250 trading days).

The table below shows the trend:

Trefis

Question 4: What about the possibility of a drop after a rise if you wait for a while?

Answer:

After seeing a rise of 5% over 5 days, the chances of a 5% drop in BlackBerry stock are about 44% over the subsequent quarter of waiting (60 trading days). This chance increases to about 53% when the waiting period is a year (250 trading days).

Whats behind Trefis? See How Its Powering New Collaboration and What-Ifs ForCFOs and Finance Teams|Product, R&D, and Marketing Teams More Trefis Data Like our charts? Exploreexample interactive dashboardsand create your own

Continue reading here:

Machine Learning Answers: If BlackBerry Stock Drops 10% A Week, Whats The Chance Itll Recoup Its Losses In A Month? - Forbes

As we draw closer to 2020, Rick Rider, Senior Director, Product Management, Infor shares predictions on AI.

Moving to Intellectual Digital Assistants. To meet growing enterprise user expectations, AI Digital Assistants will evolve into Intellectual Digital Assistants. Users no longer are satisfied with just telling Digital Assistants what to do and having them automatically execute certain tasks or basic configurations. 2020 will be the year when these digital assistants, using AI and machine learning (ML), start to understand the context of what users are doing, recommend potential next steps (based on completed actions), identify mistakes and auto-correct inputs, and start to engage with users in dynamic, on-the-fly conversations.

AI helps define a new normal. In 2020, AI and machine learning platforms will start to challenge conventional thinking, when it comes to enterprise business processes and expected outcomes. In other words, these systems will re-define our default assumptions about what is normal. This will make business process re-engineering and resource training more efficient. When examining supply chain processes, for example, AI platforms have observed that default values related to expected delivery dates and payment dates typically are used only 4 percent of the time. Users almost always plug in their own values. Therefore, AI and machine learning systems will start enabling us to disregard default values, as we understand them today, and act more quickly through trust in our data. We no longer will be beholden to predefined rules, defaults, or assumptions.

Operationalizing AI. Industry-specific templates will make AI easier to use and deploy in 2020. In manufacturing, AI and machine learning systems, will take advantage of templated processes to help enterprises better manage their parts inventories, improve demand forecasting and supply chain efficiency, and improve quality control and time-to-delivery. In healthcare, organizations will leverage AI and machine learning to better integrate data thats segregated in application silos, exchange information with partners across the care continuum, and better use that data to respond to regulatory and compliance requirements. And, in retail, companies will use AI and ML to better predict demand patterns and shipment dates, based on defined rules, and improve their short- and long-term planning processes.

Link:

AI and machine learning platforms will start to challenge conventional thinking - CRN.in

One of Amazons most recent announcements was the release of their new tool called Amazon Rekognition Custom Labels. This advanced tool has the capability to improve machine learning on a whole new scale, allowing for better data analysis and object recognition.

Amazon Rekognition will help users train their machine learning models more easily and allow them to understand a set of objects out of limited data. In other words, this capability will make machines more intelligent and capable of recognizing items with far less data sets than ever before.

Employees stand near an The Amazon Inc. logo is displayed above the reception counter at the ... [+] company's campus in Hyderabad, India, on Friday, Sept. 6, 2019. Amazon's only company-owned campus outside the U.S. opened at the end of August on the other side of the globe, thousands of miles from their Seattle headquarters. The 15-storey building towers over the landscape in Hyderabad's technology and financial district, signaling the giant online retailer's ambitions to expand in one of the world's fastest-growing retail markets. Photographer: Dhiraj Singh/Bloomberg

The Benefits of Machine Learning with Amazon Rekognition

Machine learning includes a scientific study and adoption of algorithms that allow computers to learn new information and functionalities without needing direct instructions. In other words, machine learning can be understood as the capability of computers to learn on their own.

Thus far, machine learning models required large data sets in order to learn something new. For instance, if you wanted a device to recognize a chair as a chair, you would have to provide hundreds, if not thousands of pieces of visual evidence of what a chair looks like.

However, with Amazons new recognition tool, machine learning models will be able to work with very limited data sets and still effectively learn the difference between new objects and items.

Computers will now be able to recognize a group of object based on as little as ten images, which is a significant improvement compared to previous requirements. Amazon is slowly but surely stepping on a fresh and untrodden path of machine learning development.

Why Amazon Rekognition Matters

Having limited data to work with used to be a challenge in machine learning. Today, new models will be able to learn efficiently without large sets of data all thanks to Amazons recently announced tool.

Instead of having to train a model from scratch, which requires specialized machine learning expertise and millions of high-quality labeled images, customers can now use Amazon Rekognition Custom Labels to achieve state-of-the-art performance for their unique image analysis needs, announced Amazon in their blog post.

The new Amazon Rekognition featured on December 3rd and it is expected to bring significant changes to machine learning all throughout 2020. The release of the new tool also took place in the AWS re:Invent conference that was held in Las Vegas.

Read more:

Amazon Releases A New Tool To Improve Machine Learning Processes - Forbes

Our country is deeply divided into two camps.

From coast to coast, people are eager to know the answer to one simple question: Who will come out on top Michael Jordan or LeBron James?

It might seem like a moot point. NBA legend Michael Jordan is now well into retirement while LeBron James is still able to continue building his case with the Los Angeles Lakers. Thanks to the laws of time and space, theres no way to accurately compare their talent in a conclusive way.

Or is there?

AutoStats, a product of Stats Perform, is using artificial intelligence and computer vision to unlock secrets of seasons past and predict seasons future.

The goal of AutoStats is to collect tracking data from every sports video that has ever existed which essentially enables us to travel back in time and compare players and eras in a way that we havent been able to do previously, said Patrick Lucey, chief scientist at Stats Perform. Using this technology, we can start to make the impossible possible.

The implications of these statistics are a real game-changer in the sports world, the effects of which can be seen in betting, team drafting and recruitment, professional commentary, fantasy football and how well your opinions on all-star players hold up.

Sujoy Ganguly, Ph.D.

Director of Computer Vision

I am the director of computer vision, which means I teach computers to watch sports. Specifically, we extract the positions of the players, their limbs and actions directly from the broadcast video you get in your home.

Patrick Lucey, Ph.D.

Chief Scientist

Im the chief scientist, and my role is to set the AI strategy to maximize the value of our deep treasure troves of sports data using AI technology.

Patrick Lucey: AI not only emulates what a human can do, but surpasses what even the best human expert can do. The reason why artificial intelligence has reached this superhuman capability is that it has utilized an enormous amount of data. The more data you have, the better your AI technology will be simple as that.

When it comes to the sheer volume of sports data, no other company has the amount that we have. We cover any sport you can think of, and we capture it at a depth that no other company does.

Sujoy Ganguly: The goal of our team is to create the most in-depth data at the broadest breadth. We do this by extracting player tracking, pose and event data everywhere there is broadcast video. To accomplish this, we have three streams:one that focuses on model development, the second that focuses on the deployment of these models to the cloud, and a third that focuses on implementation at the edge for in-venue deployment.

How does Stats Perform get its data?

Stats Perform collects data through raw video. Its collected via the companys in-venue hardware or snapped up from broadcasts.

Lucey: Well, its like teaching a child how to read. First, they have to learn the alphabet and words before being able to understand a sentence, then onto a paragraph only then they can understand the whole story. Once they have read a lot of books and seen similar stories in the past, then they can actually start to predict how the story will unfold.

Its similar for sport, where we first have to create a sports-specific alphabet and words from which to form sentences that represent gameplay that a computer can understand. Instead of using characters and textual words, we use spatial data and event sequences. From this sports-specific language we have built, we can then get the computer to learn similar gameplay from the data we have, which enables us to predict plays and player motion. The main reason why I believe AI has so much hype around it is that it is the ultimate decision analysis tool every decision and action can be objectively analyzed.

Ganguly: Teaching a machine to interpret sports is a complex and evolving problem. At a high level, we start with a clearly defined question. For example, what is the likelihood that a team will win a game, and how does this depend on the players on that team? Then we ask what information we have: We have results of thousands of games and data about the players who played in those games. From there, we can start the process of conducting experiments and converging to a high-performing model. Generally, this process requires an open and honest conversation about the results of each test and what we have learned.

Ganguly: Many of the challenges we face with machine learning are the same as in other industries, like how we collect and maintain data sets or how we manage training and deployment workloads. However, most companies that work on prediction are doing so on strictly temporal data. In contrast, we have spatial and temporal information. Unlike the autonomous vehicle companies that also deal with spatial-temporal data, we dont control all of the sources of video. This presents unique challenges in data collection but also allows us to use predictive models that allow for noise and are therefore robust.

Different kinds of data

Temporal data is data relating to time and spatial data refers to space. As Ganguly alluded to, combining the two is necessary in the tech behind self-driving cars. This data helps determine whats another moving object, like another car, and whats stationary, say, a tree. For Stats Perform, they data scientists are looking less at a deer in the road, and more how a player moves on the field, and at what speed. The result is the ability to pinpoint the specific motions of a player depending on the context of the game and play, and to anticipate how theyd react in a similar situation.

Lucey: The example I like to talk about is our work in soccer. Soccer is a hard sport to analyze because it is low-scoring, continuous and strategic. As such, the current statistics used, such as possession percentage, number of passes and completion rate, number of corners and tackles, do not correlate with goals scored and who won the match. Our AI-based metrics expected goals, quality of passes and playing styles correlate much higher with goals compared to standard statistics. These AI-metrics simply measure performance better. Using these AI tools, we were able to show how, against incredible odds, underdog Leicester City won the 2015-16 English Premier League title.

Ganguly: There are two significant ways that AI is and will continue to revolutionize sports. Firstly, AI is creating more complex and granular data at an unprecedented scale. For example, with our AutoSTATS technology, we can capture the motions of players in college basketball, where this data was never before available. The other way AI is revolutionizing sport is by allowing people to draw insights from our increasingly in-depth data. Using player tracking data, we can predict the motion of players. This allows us to see how a player will behave on their team after a trade, thereby allowing for better player recruitment.

Isolating a teams formation

Tools like Stats Performsunsupervised clustering method can quickly find a teams formation right down to the frame. When humans attempt to do this, their results fall just a few yards short.

Lucey: Even though we have the most sports data on the planet, to tell the best stories and provide the best analysis and products for our customers, we need even more granular data. Thats why I am so excited about our AutoStats work.

AI has so much hype around it is because it is the ultimate decision analysis tool every decision and action can be objectively analyzed. AI can not only capture data using computer vision and other sensors that couldnt be captured before, but it can help us transform that data into a form that can be used to make decisions. Given how popular sports are around the world and the importance they have on other sectors, theres potential for other industries to directly use the data and technology that we have generated to make future decisions.

See original here:

MJ or LeBron Who's the G.O.A.T.? Machine Learning and AI Might Give Us an Answer - Built In Chicago

The Twitter logo appears on a phone post on the floor of the New York Stock Exchange, Thursday, Oct. ... [+] 27, 2016. (AP Photo/Richard Drew)

Twitter stock has seen significant volatility over the last few years. While the stock is benefiting from an expanding international user base and improving monetization, slowing growth rates and concerns about its valuation have hurt the stock. Considering the recent price movements, we began with a simple question that investors could be asking about Twitters stock: given a certain drop or rise, say a 10% drop in a week, what should we expect for the next week? Is it very likely that the stock will recover the next week? What about the next month or a quarter? You can test a variety of scenarios on the Trefis Machine Learning Engine to calculate if the Twitter stock dropped, whats the chance itll rise.

For example, after a 5% drop over a week (5 trading days), the Trefis machine learning engine says chances of an additional 5% drop over the next month, are about 31%. This is quite significant, and helpful to know for someone trying to recover from a loss. Knowing what to expect for almost any scenario is powerful. It can help you avoid rash moves.

Below, we also discuss a few scenarios and answer common investor questions:

Question 1: Does a rise in Twitter stock become more likely after a drop?

Answer:

Not really.

Specifically, chances of a 5% rise in Twitter stock over the next month:

= 34% after Twitter stock drops by 5% in a week.

versus,

= 36.5% after Twitter stock rises by 5% in a week.

Question 2: What about the other way around, does a drop in Twitter stock become more likely after a rise?

Answer:

Yes, Slightly more likely. Specifically, chances of a 5% decline in Twitter stock over the next month:

= 30.7% after Twitter stock drops by 5% in a week

versus,

= 34.5% after Twitter stock rises by 5% in a week

Question 3: Does patience pay?

Answer:

According to data and Trefis machine learning engines calculations, largely yes!

Given a drop of 5% in Twitter stock over a week (5 trading days), while there is only about 23% chance the Twitter stock will gain 5% over the subsequent week, there is more than a 40% chance this will happen in 3 months.

The table below shows the trend:

Trefis

Question 4: What about the possibility of a drop after a rise if you wait for a while?

Answer:

After seeing a rise of 5% over 5 days, the chances of a 5% drop in Twitter stock are about 45% over the subsequent quarter of waiting (60 trading days). However, this chance drops slightly to about 42.5% when the waiting period is a year (250 trading days).

The table below shows the trend:

Whats behind Trefis? See How Its Powering New Collaboration and What-Ifs ForCFOs and Finance Teams|Product, R&D, and Marketing Teams More Trefis Data Like our charts? Exploreexample interactive dashboardsand create your own

Follow this link:

Machine Learning Answers: If Twitter Stock Drops 10% A Week, Whats The Chance Itll Recoup Its Losses In A Month? - Forbes

Pharmas desire to build direct relationships with patients isnt new. But even as rapidly changing technology makes those connections more possible than ever, it's also making them more important.

Opt-in health apps. 24/7 call centers that depend on machine learning. Voice-enabled artificial intelligence that helpsmanage chronic conditions. Digital therapeutics with automated reporting. They're just a few of the tech toolsbecoming indispensable in pharma marketingand not just because of the value those tools offer patients.

It's also because thedata and analytics those provide are important as pharma companiesshiftto more patient-centric businesses.

Simplify and Accelerate Drug R&D With the MarkLogic Data Hub Service for Pharma R&D

Researchers are often unable to access the information they need. And, even when data does get consolidated, researchers find it difficult to sift through it all and make sense of it in order to confidently draw the right conclusions and share the right results. Discover how to quickly and easily find, synthesize, and share informationaccelerating and improving R&D.

Astellas, for instance, hired its first senior vice president of patient centricity from Sanofi, where he spent eight years creating a system that integrates patient and physician perspectives into the drug discovery and development process.

Emerging digitaltools have also become important marketing devices that can convey pharma personality.

Take Reckitt Benckisers Mucinex Halloween TikTok videos. The brand translated its zombie-themed TV ad campaign for new product NightShift into a challenger TikTok promotion called #TooSickToBeSickand racked up more than 400 million views in just five days. Almost as importantly, it drummed up credibility with a young hip audience of influencers.

Another example is Eisais voice-enabled play and meditation skill called Ella the Jellyfish, created for children with Lennox-Gastaut syndrome and their families. The skill can sing, play games, tell stories and offer guided meditations and offers friendly support for a challenging rare disease.

And although the word relationship is often used in regard to pharmas emerging connections with patients, that may not be the exactly right term, said Syneos Health Managing Director of Insights and Innovation Leigh Householder.

Its not a relationship in that its what loyalty looks like in other categorieslike airlines, she said. In pharma, it looks more like what you see from really good health insurers who are able to know enough about you to find those moments when a nudge or reconnect or their next product would be very useful in your life. Instead of relationship, maybe we could just say person-level relevance.

Whatever its called, the successes creating those connections means the industry should expect even more digital tools and optimization from pharma in 2020.

Kendalle Burlin OConnell, chief operating officer at life science nonprofit MassBio, said, The rise of mobile apps has created a new age of patient engagement that I expect will grow in 2020. Well see increased app development from both providers and manufacturers to track medical adherence, relay updates between patients and physicians regarding care, and disseminate real-time data that captures the full patient journey.

Originally posted here:

Tech connection: To reach patients, pharma adds AI, machine learning and more to its digital toolbox - FiercePharma

Seagate Technology's hard disk drive assembly plant in Singapore, Monday, Feb. 5, 2007. ... [+] Photographer: Jonathan Drake/Bloomberg News

Seagate (NASDAQ: STX) stock has seen significant volatility over the last few years. While the demand for data storage is expanding, considering the growth of cloud computing and other technologies such as artificial intelligence and machine learning, the companys focus on hard-disk drive technology, which is cost-effective but slower and less power-efficient compared to newer solid-state drives has likely weighed on its valuation.

Considering the significant price movements, we began with a simple question that investors could be asking about Seagates stock: given a certain drop or rise, say a 10% drop in a week, what should we expect for the next week? Is it very likely that the stock will recover the next week? What about the next month or a quarter? You can test a variety of scenarios on the Trefis Machine Learning Engine to calculate if the Seagate stock dropped, whats the chance itll rise.

For example, if Seagate Stock drops 10% or more in a week (5 trading days), there is a 27% chance itll recover 10% or more, over the next month (about 20 trading days). On the other hand, after a 5% drop over a week (5 trading days), the Trefis machine learning engine says chances of an additional 5% drop over the next month, are about 31%. This is quite significant, and helpful to know for someone trying to recover from a loss. Knowing what to expect for almost any scenario is powerful. It can help you avoid rash moves.

Below, we also discuss a few scenarios and answer common investor questions:

Question 1: Does a rise in Seagate stock become more likely after a drop?

Answer:

The chances of a 5% rise in Seagate stock over the next month:

= 38% after Seagate stock drops by 5% in a week

versus,

= 45% after Seagate stock rises by 5% in a week

Question 2: What about the other way around, does a drop in Seagate stock become more likely after a rise?

Answer:

The chances of a 5% drop in Seagate stock over the next month:

= 31% after Seagate stock drops by 5% in a week

versus,

= 24% after Seagate stock rises by 5% in a week

Question 3: Does patience pay?

Answer:

According to data and Trefis machine learning engines calculations, absolutely!

Given a drop of 5% in Seagate stock over a week (5 trading days), while there is a 38% chance the Seagate stock will gain 5% over the subsequent week, there is more than 58% chance this will happen in 6 months, and 68% chance itll gain 5% over a year (about 250 trading days).

Question 4: What about the possibility of a drop after a rise if you wait for a while?

Answer:

After seeing a rise of 5% over 5 days, the chances of a 5% drop in Seagate stock are about 30% over the subsequent quarter of waiting (60 trading days). However, this chance drops slightly to about 27% when the waiting period is a year (250 trading days).

The table below shows the trend:

Trefis

Whats behind Trefis? See How Its Powering New Collaboration and What-Ifs ForCFOs and Finance Teams|Product, R&D, and Marketing Teams More Trefis Data Like our charts? Exploreexample interactive dashboardsand create your own

Originally posted here:

Machine Learning Answers: If Seagate Stock Drops 10% A Week, Whats The Chance Itll Recoup Its Losses In A Month? - Forbes