Category Archives: Machine Learning
Machine Learning Answers: If Caterpillar Stock Drops 10% A Week, Whats The Chance Itll Recoup Its Losses In A Month? – Forbes
We found that if Caterpillars (NYSE: CAT) stock drops 10% in a week (5 trading days), there is a solid 25% chance that it will rise by 10% over the subsequent month (20 trading days).
Caterpillar stock has seen significant volatility this year. While the company is being impacted by growing headwinds to the global economy, the uncertainty surrounding the trade war between the U.S. and China, relatively mixed quarterly earnings reports, as well as slowing sales, its relatively high capital returns, and strong balance sheet have supported the stock to an extent.
Considering the recent price swings, we started with a simple question that investors could be asking about Caterpillar 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 Caterpillar 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 23%. 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. Given the recent volatility in the market, the mix of macroeconomic events (including the trade war with China and interest rate easing by the U.S. Fed), we think investors can prepare better.
Below, we also discuss a few scenarios and answer common investor questions:
Question 1: Does a rise in CAT stock become more likely after a drop?
Answer:
Consider two situations:
Case 1: CAT stock drops by 5% or more in a week
Case 2: CAT stock rises by 5% or more in a week
Is the chance of say a 5% rise in CAT stock over the subsequent month after Case 1 or Case 2 occurs much higher for one versus the other?
The answer is absolutely!
Turns out, chances of a 5% rise over the next month (20 trading days) is meaningfully more for Case 1, where the CAT has just suffered a big loss, versus Case 2.
Specifically, chances of a 5% rise in CAT stock over the next month:
= 40% after Case 1, where CAT stock drops by 5% in a week
versus,
= 32% after Case 2: where CAT stock rises by 5% in a week
Question 2: What about the other way around, does a drop in CAT stock become more likely after a rise?
Answer:
Consider, once again, two cases
Case 1: CAT stock drops by 5% in a week
Case 2: CAT 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 23%.
Question 3: Does patience pay?
Answer:
According to data and Trefis machine learning engines calculations, absolutely!
Given a drop of 5% in CAT stock over a week (5 trading days), while there is only about 21% chance the CAT stock will gain 5% over the subsequent week, there is more than 50% chance this will happen in 6 months, and 62% 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 CAT stock are about 24% over the subsequent quarter of waiting (60 trading days). However, this chance drops slightly to about 23% 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
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3 questions to ask before investing in machine learning for pop health – Healthcare IT News
The goal of population health is to use data to identify those who will benefit from intervention sooner, typically in an effort to prevent unnecessary hospital admissions. Machine learning introduces the potential of moving population health away from one-size-fits-all risk scores and toward matching individuals to specific interventions.
The combination of the two has enormous potential. However, many of the factors that will determine success or failure have nothing to do with technology and should be considered before investing in machine learning or population health.
Population health software, with or without machine learning, only produces suggestions. Getting a team to take action, particularly if that action is different, is one of the hardest things to do in healthcare. You will not succeed without executive support. Executives will not support you without significant incentive to do so.
Here's an easy surrogate for whether there is enough of that incentive: whether those executives jobs are in jeopardy if too many people go to the hospital. If not, the likelihood that an investment will lead to measurable improvement is minimal.
If youve been ordered to "do" population health, your best bet is to install a low cost risk score or have your team write a query to identify the oldest sickest people with the most readmissions. Either will return the same results more or less and your team of care managers are used to ignoring said results without rocking the boat. If there is sufficient incentive, read on.
Henry Ford is credited with saying, "If I asked people what they wanted, they would have said faster horses." Its human nature to try to apply a new technology in an old way.
Economists have named this the IT Productivity Paradox and have studied the cost of applying new technical capabilities in old ways. There are signs that healthcare organizations are unknowingly walking this plank.
For decades, risk scores were designed to identify the costliest patients with little consideration of the types of costs, the diseases they suffer, whether or not those costs are preventable, etc.
As a result, according to a systematic review of 30 risk stratification algorithms appearing in the Journal of the American Medical Association, "most current readmission risk prediction models that were designed for either comparative or clinical purposes perform poorly." A recently published study in Science also showed that prioritizing based on cost discriminates against people of color. Applying more data and better math to solve the problem in the old way is an expensive way to propagate existing shortcomings.
The opportunity now made possible is the ability to match individuals to interventions. Patients with serious mental illness that are most likely to have an inpatient psychiatric admission are very different than those with serious illnesses that might benefit from home-based palliative care. Clinicians wouldnt treat them the same, neither should our approach to prioritization.
However, you will need to design for this and clinical teams should be prepared for the repercussions. Patients identified with rising risk (as opposed to peak utilization) will not seem as sick.
Clinical teams trained to triage may feel like theyre not doing their jobs if the patients arent as obviously acute. Its important to discuss these repercussions and prepare in advance of the introduction of new technology.
Using technology to send more of the right people into a program that doesnt have an impact only adds to the cost of an already failing program. Surprisingly, very few programs have ever measured the impact of their interventions.
Those that have, often rely on measuring patients before and after they enter into care management programs which is misleading and biased on many levels.
If you are not confident that the existing program makes a difference, invest in measuring and improving the existing programs performance before investing additional resources. A good read on the pros and cons of different approaches to measuring impact is here.
Starting with a program of measurement can create a culture of measurement, improvement, and accountability - a great foundation for a pop health effort. Involving the clinical team in the definition of measures that matter will go a long way.
Another important consideration is whether your intervention is costly to deliver. The more costly it is to steer resources toward the wrong people, the more likely your program is to benefit from smarter prioritization.
For both reasons above if your program is entirely telephonic and targets older people with chronic complex diseases, you may want to invest in program design and measurement before investing in stratification technology.
Youre in great shape, and your odds of success are exponentially higher. Youre also better informed, as you and the team shift focus to decisions such as whether to build versus partner, what unique data you collect that can be used to your advantage and how youll measure algorithm and program performance.
Leonard DAvolio, PhD is an assistant professor at Harvard Medical School and Brigham and Womens Hospital, and the CEO and founder of Cyft. He shares his work on LinkedIn and Twitter.
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3 questions to ask before investing in machine learning for pop health - Healthcare IT News
Here’s why machine learning is critical to success for banks of the future – Tech Wire Asia
HSBC Bank and others use machine learning to win big. Source: Shutterstock
MACHINE learning is a popular buzzword today, and has been heralded as one of the greatest innovations conceived by man.
A branch of artificial intelligence (AI), machine learning is increasingly embedded in daily life, such as automatic email reply predictions, virtual assistants, and chatbots.
The technology is also expected to revolutionize the world of finance. While it is slower than other industries in embracing the technology, the impact of ML is already visibly significant.
Most recently, HSBC said that the bank was using the technology to combat financial crime.
We have 608 million transactions every month. Hence, with AI and machine learning we are able to identify a good transaction done by an innocent person versus transaction conducted by criminals, said HSBC Hong Kong Financial Crime Threat Mitigation Regional Head Paul Jevtovic.
Like HSBC, several other banks are beginning to deploy ML at scale. Here are the top three use cases in the banking and financial services space:
Prior to the advent of ML, decisions were made on a rule-based system, where the same criteria are applied across a broad customer segment, subjecting them to a one-size-fits-all solution.
With ML, bankers can approach customers in a more personalized way. ML algorithms can analyze volumes of consumer data in banks, tracing each customers digital footprint with a unified, omni-faceted view.
This footprint includes their financial status across multiple accounts, financial investments, and banking transactions.
With the relevant data and armed with the right analytical tools, ML can provide valuable insights that allow banks to create tailor-made solutions based on a specific customers behavior, preferences, and requirements.
With the wide tracking of a customers digital footprint that ML offers, banks can quickly and accurately assess a potential borrowers ability to repay better than with traditional methods.
Leveraging ML can eliminate biases, and can quickly help differentiate between applicants who are more credit-worthy from those who have a higher default risk even without an elaborate credit history. ML can also help banks forecast potential issues and rectify them before they occur.
With the assurance that risks are being mitigated, banks can focus on issues that can add value to their customers, increase productivity, and provide greater support to their employees.
ML can be greatly leveraged for fraud detection. Fraud is a pain point for many financial institutions, one which could potentially cause a bank to go out of business.
With ML, anomalies in customers behaviors can be quickly detected. By flagging and blocking transactions that are suspicious, banks can catch fraud in real-time, protecting customers and themselves.
ML is undoubtedly one of the greatest technological feats of the 21st century. With its laser precision in predicting behaviors and anticipating risks, we can be sure that the role of ML will only be more prominent in the future of banking.
Regardless of size, financial institutions or businesses looking to engage financial services must be aware of the uses of ML in banking. Should they wish to stay relevant, they must start exploring the technology now.
See the article here:
Here's why machine learning is critical to success for banks of the future - Tech Wire Asia
Startup jobs of the week: Marketing Communications Specialist, Oracle Architect, Machine Learning Scientist – BetaKit
Every day, Canadas tech startups post their latest and greatest job opportunities on Jobs.BetaKit, powered by Jobbio. From early-stage to Series B and beyond, Jobs.BetaKit helps startups from all over the country hire Canadas top tech talent.
Each week, BetaKit will highlight a selection of the job roles posted to Jobs.BetaKit. If youre a candidate looking for a position at a tech Canadian startup, survey the selection below or view all the posted positions here. For companies in need of top candidates, scroll to the bottom of this post to learn how to get your roles posted to Jobs.BetaKit!
TalentMinded Controller
The companys client, a leader in their industry, is adding a Controller to their global Finance organization. Reporting to the VP Finance, the Controller is an individual who takes initiative, capitalizes on opportunities, and wants to build and maintain processes and guidelines. This is a role for someone who wants to manage relationships with external investors, take a hands-on approach with reporting and analysis, and oversee the milestones and monthly activities while leaving the day-to-day with your team. Our client is seeking an insightful influencer who can inspire, find solutions and motivate resources toward goals; someone who understands this is an active individual contributor role in a dynamic environment working closely with the VP Finance.
iNTERFACEWARE Marketing Communications Specialist
You are a Marketing Coordinator looking for a chance to step up or you are currently a marketing Communications Specialist looking to move to a company with purpose. You have B2B experience from working in a corporate environment, PR company or marketing agency. You are looking for a new challenge at a software company building technology with a purpose, occupying a powerful market niche; a place that needs your creative writing skills to further propel us along an impressive growth trajectory.
Sensibill Demand Generation Specialist
This role will create, test, deploy and measure demand gen campaigns and programs to acquire and nurture sales qualified leads that will contribute to revenue growth for Sensibill. The Demand Generation Specialist is responsible for marketing campaign elements including but not limited to: email blasts, email nurture campaigns, automation workflows, webinar campaigns, paid media, landing page and website optimization, A/B testing, SEO, list segmentation and data cleanliness. The role requires strategic thinking and a data-driven mindset to create campaigns that convert and add to the bottom line.
Chisel AI Machine Learning Scientist
Reporting to the Data Science Lead, you will collaborate with a team of Machine Learning Scientists to explore and understand the latest in AI, NLP, and ML; working together to implement best practices and further iterate on the companys core AI information extraction competency. This is a unique opportunity to get in at the ground level as the company scales; to define AI models for the entire organization and to revolutionize an industry with data thats actionable.
ventureLAB Director, Business Development
Reporting to the Vice President, Partnerships, this role is responsible for the development and end-to-end delivery of a robust private sector, industry pipeline and revenue stream. The successful candidate will possess a hunter mentality, and have the capability to switch seamlessly between strategic thinking, planning and tactical work. They will also be a strong communicator with the ability to clearly articulate the ventureLAB value proposition to prospective partners, and strategically align partnership opportunities.
CBC/Radio Canada Android Developer, Digital Audio
You are an Android Developer looking to apply and build upon your skills, creating end-to-end user-facing products with an emphasis on discoverability, engagement, and personalization. You understand the importance of accessibility and know what it takes to meet the needs of all users. You are adaptable and willing to jump into different areas, features, products wherever you are needed, you are happy to contribute. Whether you are currently working in a startup, the corporate world or somewhere in between, you want to be part of a fun team, engaged in a continuous learning culture, where you can take on new challenges and be a significant contributor to engaging a national audience.
HiMama Marketing Operations Specialist
Are you an analytical, technical, and metrics-obsessed marketing ops professional? Have you helped scale tech stacks for marketing teams, implemented rigorous reporting frameworks, and built funnels from scratch? HiMama is looking for amazing people like you to join their RevOps team and act as a key business partner to our Marketing team!
Vidyard Director of Growth and Digital
As the Director Growth and Digital you are responsible for defining and executing Vidyards growth and digital strategy. You thrive working in the middle of product, marketing and operations to build and maintain the industrys best web experience as well as define and execute high velocity experiments, both on web properties and within product to achieve business growth objectives. The successful candidate will be curious in nature, not afraid to push the limits of the possible and advocate for the benefits of working within high performing cross functional teams to drive results.
Motion Mobility Accessibility Consultant
Right now Motion is adding a Mobility & Accessibility Consultant to their team in Comox. They are looking to hire a Mobility & Accessibility Consultant who shares their passion for enriching the lives of the clients they work with. They need a compassionate and innovative self-starter looking to make an impact. You are an extraordinary salesperson. People trust you. You seek opportunities to enhance the lives of your clients and find satisfaction in solving their needs. You want to be part of a team. A small, tight knit community thats more like a family but with the backing and the support and reputation of a recognized, national brand.
Viziya Oracle Architect
VIZIYA is a quickly expanding software company that thrives on innovation to solve challenges. VIZIYA needs an Oracle Architect Consultant to join our team. As the Oracle Architect you will plan, direct, co-ordinate and implement Oracle Software Applications. You have an M.S. or equivalent or Bachelors Degree or equivalent plus five years of experience in Computer Science, Engineering or a related field, plus hands on experience with Oracle SQL plus, PL/SQL,RDBMS (11G), XML Publisher, and others.
BetaKit and Jobbio have joined forces to create a digital careers marketplace targeting BetaKits 1.8 million annual visitors.
For tech companies, that means access to an incredible audience of engaged passive and active candidates. Powered by Jobbios innovative technology, your jobs are delivered directly to a targeted audience of Canadian tech professionals.
Follow this link to post your job on Jobs.BetaKit today.
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The 10 Hottest AI And Machine Learning Startups Of 2019 – CRN: The Biggest Tech News For Partners And The IT Channel
AI Startup Funding In 2019 Set To Outpace Previous Year
Investors just can't get enough of artificial intelligence and machine-learning startupsif the latest data on venture capital funding is any indication.
Total funding for AI and machine-learning startups for the first three quarters of 2019 was $12.1 billion, surpassing last year's total of $10.2 billion, according to the PwC and CB Insights' MoneyTree report.
With global spending on AI systems set to grow 28.4 percent annually to $97.9 billion, according to research firm IDC, these startups see an opportunity to build new hardware and software innovations that can fundamentally change the way we work and live.
What follows is a look at the 10 hottest AI and machine-learning startups of 2019, whose products range from new AI hardware and open-source platforms to AI-powered sales applications.
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Global Director of Tech Exploration Discusses Artificial Intelligence and Machine Learning at Anheuser-Busch InBev – Seton Hall University News &…
Adam Spunberg, Global Director of Tech Exploration
On November 19, APICS (American Production and Inventory Control Society, now known as ASCM, Association for Supply Chain Management) hosted a representative from Anheuser-Busch InBev who specializes in artificial intelligence (AI) and machine learning innovation. The representative, Adam Spunberg, works out of the Newark office and is the global director of tech exploration.
In his position Spunberg monitors and oversees innovation in the supply chain area of the company. Additionally, he focuses on bringing the company together through new technology and using AI to do something spectacular that couldn't be done before. Through his experience, he has learned that innovation is a mixture of having great ideas and then generating support for those great ideas. Anheuser-Busch InBev has four main checkpoints for filtering these innovative ideas: idea prioritization, quality check, zone demand and direct sponsorship.
Idea prioritization focuses on filtering through ideas to find the most prominent and useful for the industry. Quality check ensures that the innovative idea doesn't exist in another company or at another Anheuser-Busch InBev location. Zone demand is analyzing which areas or satellite locations have the need for this innovation. Lastly, direct sponsorship refers to getting the support from the appropriate people needed within the company to move forward.
Building upon these checkpoints, Spunberg was able to share a variety of projects that Anheuser Busch InBev has been pursuing with the use of AI and machine learning. One project has included the use of AI video training. This project uses an online video library that has videos on how to complete every necessary task in the breweries. Using AI, the words spoken in these videos can be broken down into written text that becomes the captions in the video. Additionally, this AI software can translate both the audio and captions into another language.
Additionally, AI is being used to identify packaging defects within the factory assembly lines. This is achieved through a model that quickly snaps pictures of cans flowing through the assembly line. The software is then able to compare these pictures to existing pictures in order to determine if the individual can is in either good or bad quality. This allows the quality checking process for packaging defects to shift from manual labor to a technological feat.
Another use of AI is the advanced process control project, which offers a digital version of a production environment. More specifically, Anheuser Busch InBev replicates the environment of steam generation from a boiler in a model that accounts for the many variables expressed in the real-life environment. Once the digital environment is proven to be accurate to the real-life environment, then the proprietor can test different situations and events in this digital environment.
Spunberg also spoke about AI filtration optimization, which is not only applicable to Anheuser Busch InBev, but also many other companies and students. Anheuser Busch InBev utilizes Microsoft as their cloud computing basis. However, this prevents them from being able to utilize Google cloud and the services Google offers. In order to remedy this, AI has been used to develop new, cutting edge technology that creates an extra gateway layer that can process Google documents and data into Microsoft outputs.
As Spunberg concluded his presentation he emphasized, "Find your humanity in AI" -- highlighting the importance of giving back to less fortunate communities with the power that AI can bring. Using geo systems, Spunberg hopes to be able to optimize routes for the distribution of necessary supplies in third world countries. "Try to think about what you can do to leave your mark on the world and make life better for others."
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The role of machine learning in IT service management – ITProPortal
The service desk acts as the go-to place for all IT-related needs and issues, typically managing incidents or service disruptions, requests, and changes. The service desk scope of work can be enormous and wide-ranging, depending on the nature and size of the organisation in question. As a critical function used by employees across a company, it needs to be managed appropriately.
Technology has upended the way business is done across all industries around the world. At the same time, traditional IT service management (ITSM) solutions have become inefficient in maintaining customer satisfaction levels and meeting increasing customer expectations in a fast-paced digital world.
According to the SolarWinds IT Trends Report 2019: Skills for Tech Pros of Tomorrow, 79 per cent of IT managers werent able to spend sufficient time on value-added business activities or initiatives due to interruptions with day-to-day support-related issues. This resulted in misleading or incorrect manual entries into a problem log, which caused misinformed decision-making. With managers inundated with work, its easy for them to accidentally become the victim of manual or human errors.
With IT environments changing at an accelerating rate, its crucial IT service desks adopt emerging technologies. An explosion of data in recent years has intensified the pressure for IT professionals, but automated processes and machine learning (ML) can alleviate this pressure significantly. Artificial Intelligence (AI) and ML arent just buzzwords anymore. Enterprises worldwide are incorporating these technologies to enhance and improve operational efficiencies.
Whether for their use in predictive analytics, providing business intelligence, performance monitoring of networks, applications and systems, or even for its importance in self-driving cars, AI and ML are transforming the IT space. So, what are the applications of ML when it comes to ITSM? As an essential driver of how a business operates, a service desk solution can employ ML to streamline processes, and reduce manual, time-intensive tasks, which will ultimately free up time for additional projects and training to deliver business-wide transformation.
Incident resolution time has the potential to be cut in half. ML will enable self-resolution of incidents without the involvement of technicians and users will be able to search for solutions by themselves. Chatbots (like Google Assistant, for example) will be able to give information to end users without them having to log a ticket by providing easy access to relevant knowledge base articles based on their queries. Through ML, help desks could learn from past incidents and data to route tickets to the appropriate technician or support group. This can considerably increase efficiencies. Even better, automated help desks can run 24/7, making services available to employees at all hours at their own convenience.
Old IT assets can cause performance degradation for employees who rely on technology assets to do their jobs. In turn, this can result in a sizeable number of incidents in an organisation. Businesses spend a lot of money on hardware and software because of asset management solutions with poor transparency. This can be turned around using asset management solutions with ML technology to help track their performance based on insights from performance levels or incidents associated with a given asset. If incidents about a specific technology asset come into the system frequently or en masse, ML can recognise these as being associated and therefore indicative of a broader problem to be addressed.
ML can consume large datasets of past performance data to enable an analysis of incidents to predict future problems. Predictive capabilities can help save time, money, and effort for the entire organisation as steps can be taken before the severity or impact of the incident increases.
When end users submit a ticket, automation rules rely heavily on data like categories and subcategories to ensure accurate routing. ML helps facilitate this process by providing end users with suggestions for the most relevant categories and subcategories for a given ticket.
Service desk reporting can show trends about seasonality. Predictive models, however, take into consideration rate of change, frequency of problems, and other key factors helping predict service degradation and likely resulting in increased incident flows. This can help determine when more coverage is needed to maintain service levels.
ML, while being versatile as-is, demonstrates some critical applications when it comes to ITSM. Increasingly, organisations are taking leaps and bounds in their digital journeys, and it is only right their IT services evolve with them.
Now is a critical time for the Information Technology service management industry. The market is growing at a double-digit figure each year and is forecasted by analyst house IDC to reach over $8.5 billion by 2023.
Today, organisations need to re-examine how they can use new IT management software incorporating machine learning capabilities. Only this can change the course of IT service management which has historically been a cumbersome function of every business IT department.
Just as with huge transformative initiatives, software and machine learning can help streamline processes and increase employee productivity to drive better business outcomes. Service desk software will let IT pros consolidate asset information from multiple sources and provide real-time asset intelligence, thus improving service delivery while enhancing flexibility for collecting and managing data. By removing the manual burden of tasks like ticketing and tracking of assets and their performance, this will enable IT professionals to focus on critical projects and business transformation.
Steve Stover, Vice President of Product and Strategy, SolarWinds
Link:
The role of machine learning in IT service management - ITProPortal
Siri, Tell Fido To Stop Barking: What’s Machine Learning, And What’s The Future Of It? – 90.5 WESA
Machine learning is an integral part of Pittsburgh's tech economy, thanks to Carnegie Mellon University's position as one of the nation's foremost research centers on the topic. That's enticed tech giants such as Google and Uber to set up shop in the Steel City.
Pittsburghers have varied knowledge on what machine learning is.
On a crisp afternoon on Carnegie Mellon University's campus, Adeline Mercier of Squirrel Hill was walking with her young daughter on campus. She said her husband works in machine learning.
"It's when you train a computer, for example, or a program to learn something," Mercier said. "To optimize something or to automize something."
Alex Xu of Oakland was a little more specific.
"It's like applied statistics used for understanding how patterns can get recognized," Xu said.
A handful of people, including CMU student Karen Abruzzo, were unfamiliar.
"I've heard of it, but I don't really know what it is," said Abruzzo.
Tom Mitchell, a professor of machine learning at Carnegie Mellon University, said machine learning aims to answer the question of how to make computers improve automatically with experience.
"Humans are the best learners, much better than machines are these days, but the idea is similar," Mitchell said. "For example, if you learn to play chess you start out knowing the rules but not the strategy, and you make mistakes and learn from those and become better."
Mitchell said computers learn similarly. For example, he said people know how the recognize their mother in a photograph but can't write down an algorithm for how. An algorithm is like a recipe for a computer, telling a computer step by step what to do.
"Today it's very easy to train a computer program to recognize your mother by showing it photographs, you say in this photograph, here's my mother, this photograph, my mother is not in this one," Mitchell said. "If you give it enough of those training examples, machine learning algorithms look at the details and they find what's common to the positive examples that distinguish it from the negative."
Mitchell said one of the earliest commerical uses of machine learning was credit card fraud detection, trained on hundreds of millions of examples legitimate and illegitimate transactions. This system is still used today.
Mitchell said machine learning is also used to diagnose skin cancer in a blemish.
"Computers are now at least as accurate as carefully trained doctors," Mitchell said. "It's simply because it can examine more training data than people will see in a career."
In the future, Mitchell predicts machine learning will apply to more and more parts of life. He said it will likely become more similar to how people learn, too, especially when it comes to systems such as Siri and Alexa.
"I think in the future, you'll be able to use [smart devices] by saying, that sound you just heard was my dog barking, and whenever my dog barks and you don't hear me respond, I want you to say in my voice, 'It's okay Fido, calm down,'" Mitchell said. "I think in the coming decade we'll be able to teach them the same way you would teach me to do something if I was your assistant."
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Siri, Tell Fido To Stop Barking: What's Machine Learning, And What's The Future Of It? - 90.5 WESA
Microsoft reveals how it caught mutating Monero mining malware with machine learning – The Next Web
Microsofts antivirus and malware division recently opened the bonnet on a malicious mutating cryptocurrency miner. The Washington-based big tech firm revealed how machine learning was crucial in putting a stop to it spreading further.
According to the Microsoft Defender Advanced Threat Protection team, a new malware dubbed Dexphot has been infecting computers since last year, but since June 2019 has been burning out thanks to machine learning.
Dexphot used a number of techniques such as encryption, obfuscation layers, and randomized files names, to disguise itself and hijack legitimate systems. If successful, the malware would run a cryptocurrency miner on the device. Whats more, a re-infection would be triggered if system admins detected it and attempt to uninstall it.
Microsoft says Dexphot always uses a cryptocurrency miner, but doesnt always use the same one. XMRig and JCE Miner were shown to be used over the course of Microsofts research.
At its peak in June this year, 80,000 machines are believed to have displayed malicious behavior after being infected by Dexphot.
Detecting and protecting against malware like Dexphot is challenging as it is polymorphic. This means that the malware can change its identifiable characteristics to sneak past definition-based antivirus software.
While Microsoft claims it was able to prevent infections in most cases, it also says its behavior-based machine learning models acted as a safety net when infections slipped through a systems primary defenses.
In simple terms, the machine learning model works by analyzing the behavior of a potentially infected system rather than scanning it for known infected files a safeguard against polymorphic malware. This means systems can be partly protected against unknown threats that use mechanics similar to other known attacks.
On a very basic level, system behaviors like high CPU usage could be a key indicator that a device has been infected. When this is spotted, antivirus software can take appropriate action to curtail the threat.
In the case of Dexphot, Microsoft says its machine learning-based detections blocked malicious system DLL (dynamic link library) files to prevent the attack in its early stages.
Microsoft has not released any information on how much cryptocurrency was earned as a result of the Dexphot campaign. But thanks to Microsofts machine learning strategy it seems to be putting a lid on it, as infections have dropped by over 80 percent.
It seems as long as there is cryptocurrency, bad actors will attempt to get their hands on it.
Just yesterday, Hard Fork reported that the Stantinko botnet, thats infected 500,000 devices worldwide, has added a cryptocurrency miner to its batch of malicious files.
Published November 27, 2019 09:27 UTC
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Microsoft reveals how it caught mutating Monero mining malware with machine learning - The Next Web
The Real Reason Your School Avoids Machine Learning – The Tech Edvocate
Education has embraced machine learning. Some educators, however, still find themselves reluctant to join their colleagues in its adoption. They understand that artificial intelligence can improve job performance, but they shun machine learning altogether. They would rather dig in and do everything themselves even though machine learning could increase their job satisfaction.
Machine learning provides text analysis, automated grading, and tutoring systems. These benefit students and teachers. As the scope and quality of artificial intelligence continue to improve, well continue to see it included more routinely in every aspect of instruction.
Many teachers rely on machine learning for testing, customizing instruction, and even predicting academic achievement outcomes. Their jobs have become more streamlined, and technology helps them reach more students.
Not all teachers, however, have been quick to accept the inevitable inclusion of machine learning in their classrooms. They analyze data on their own, rely on traditional classroom management strategies, and customize learning themselves. This adherence to past practices can be cumbersome and time-consuming. Teachers unwillingness to embrace change has left them and possibly their students behind.
Even intelligent software applications like Google Assistant and Apples Siri take a back seat in the classroom when the teacher wont experiment with new technology.
Some educators arent interested in what machine learning can do for them.
How machine learning helps the classroom
Teachers find themselves responsible for meeting more demanding expectations with each passing year. They must accommodate for a variety of learning styles. They have to create scaffolded instruction for every student. All of this is in addition to their other responsibilities for the safety, health, and well-being of their students.
Teachers do not have enough time in the day to do it all.
As a result, todays classrooms require that educators demonstrate immense adaptabilty and flexibility. Academic expectations are changing continuously. To meet these new standards, education leaders revise learning expectations and increase rigor. These revisions require new instructional methods and resources to support them.
Machine learning provides the extra boost teachers need to educate the whole child and meet new standards. Intelligence automation assists educators in the pursuit of whats best for every child in their classrooms. It makes teaching easier.
Why would teachers run from something that helps them do their jobs?
Job replacement concern
The real reason your school avoids machine learning is fear.
Some teachers worry that that artificial intelligence will make their skills obsolete. Machine learning will take over traditional teacher tasks. Some educators think AI will replace teachers. No educator wants to lose their job, especially not to artificial intelligence.
Its up to us to help our teachers understand that machine learning will not replace them. We need our teachers more than ever, especially as artificial intelligence becomes more prevalent. Teachers bring immense value to the classroom. They bring human empathy. Machine learning performs the most routine instructional tasks so that teachers can fulfill their roles as inspirational role models for their students.
No amount of artificial intelligence will ever replace that.
Follow this link:
The Real Reason Your School Avoids Machine Learning - The Tech Edvocate