What Is Machine Learning???

Machine Learning for Dummies

AnalyticsAnywhere

Amazon uses it. Target uses it. Google uses it. “It” is machine learning, and it’s revolutionizing the way companies do business worldwide.

Machine learning is the ability for computer programs to analyze big data, extract information automatically, and learn from it. With 250 million active customers and tens of millions of products, Amazon’s machine learning makes accurate product recommendations based on the customer’s browsing and purchasing behavior almost instantly. No humans could do that.

Target uses machine learning to predict the offline buying behaviors of shoppers. A memorable case study highlights how Target knew a high school girl was pregnant before her parents did.

Google’s driverless cars are using machine learning to make our roads safer, and IBM’s Watson is making waves in healthcare with its machine learning and cognitive computing power.

Is your business next? Can you think of any deep data analysis or predictions that your company can produce? What impact would it have on your business’s bottom line, or how could it give you a competitive edge?

Why Is Machine Learning Important?

Data is being generated faster than at any other time in history. We are now at a point where data analysis cannot be done manually due to the amount of the data. This has driven the rise of MI — the ability for computer programs to analyze big data and extract information automatically.

The purpose of machine learning is to produce more positive outcomes with increasingly precise predictions. These outcomes are defined by what matters most to you and your company, such as higher sales and increased efficiency.

Every time you search on Google for a local service, you are feeding in valuable data to Google’s machine learning algorithm. This allows for Google to produce increasingly more relevant rankings for local businesses that provide that service.

Big Big Data

It’s important to remember that the data itself will not produce anything. It’s critical to draw accurate insights from that data. The success of machine learning depends upon producing the right learning algorithm and accurate data sets. This will allow a machine to obtain the most efficient insights possible from the information provided. Like human data analysts, one may catch an error another could potentially miss.

Digital Transformation

Machine learning and digital technologies are disrupting every industry. According to Gartner, “Smart machines will enter mainstream adoption by 2021.” Adopting early may provide your organization with a major competitive edge. Personally, I’m extremely excited by the trend and recently spent time at Harvard attending its Competing on Business Analytics and Big Data program along with 60 senior global executives from various industries.

Interested In Bringing The Power Of Machine Learning To Your Company?

Here are my recommendations to get started with the help of the right tools and experts:

  1. Secure all of the past data you have collected (offline and online sales data, accounting, customer information, product inventory, etc.). In case you might think your company doesn’t generate enough data to require machine learning, I can assure you that there is more data out there than you think, starting with general industry data. Next, think about how you can gather even more data points from all silos of your organization and elsewhere, like chatter about your brand on social media.
  2. Identify the business insights that you would benefit from most. For example, some companies are using learning algorithms for sales lead scoring.
  3. Create a strategy with clear executables to produce the desired outcomes such as fraud protection, higher sales, increased profit margin and the ability to predict customer behavior. Evaluate and revisit this strategy regularly.

Source: Forbes

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Machine Learning and Prediction in Medicine — Beyond the Peak of Inflated Expectations

Big data, we have all heard, promise to transform health care with the widespread capture of electronic health records and high-volume data streams from sources ranging from insurance claims and registries to personal genomics and biosensors. Artificial-intelligence and machine-learning predictive algorithms, which can already automatically drive cars, recognize spoken language, and detect credit card fraud, are the keys to unlocking the data that can precisely inform real-time decisions. But in the “hype cycle” of emerging technologies, machine learning now rides atop the “peak of inflated expectations.”

Prediction is not new to medicine. From risk scores to guide anticoagulation (CHADS) and the use of cholesterol medications (ASCVD) to risk stratification of patients in the intensive care unit (APACHE), data-driven clinical predictions are routine in medical practice. In combination with modern machine learning, clinical data sources enable us to rapidly generate prediction models for thousands of similar clinical questions. From early-warning systems for sepsis to superhuman imaging diagnostics, the potential applicability of these approaches is substantial.

Yet there are problems with real-world data sources. Whereas conventional approaches are largely based on data from cohorts that are carefully constructed to mitigate bias, emerging data sources are typically less structured, since they were designed to serve a different purpose (e.g., clinical care and billing). Issues ranging from patient self-selection to confounding by indication to inconsistent availability of outcome data can result in inadvertent bias, and even racial profiling, in machine predictions. Awareness of such challenges may keep the hype from outpacing the hope for how data analytics can improve medical decision making.

Machine-learning methods are particularly suited to predictions based on existing data, but precise predictions about the distant future are often fundamentally impossible. Prognosis models for HER-negative breast cancer had to be inverted in the face of targeted therapies, and the predicted efficacy of influenza vaccination varies with disease prevalence and community immunization rates. Given that the practice of medicine is constantly evolving in response to new technology, epidemiology, and social phenomena, we will always be chasing a moving target.

The rise and fall of Google Flu remind us that forecasting an annual event on the basis of 1 year of data is effectively using only a single data point and thus runs into fundamental time-series problems. Yet if the future will not necessarily resemble the past, simply accumulating mass data over time has diminishing returns. Research into decision-support algorithms that automatically learn inpatient medical practice patterns from electronic health records reveals that accumulating multiple years of historical data is worse than simply using the most recent year of data. When our goal is learning how medicine should be practiced in the future, the relevance of clinical data decays with an effective “half-life” of about 4 months. To assess the usefulness of prediction models, we must evaluate them not on their ability to recapitulate historical trends, but instead on their accuracy in predicting future events.

Although machine-learning algorithms can improve the accuracy of prediction over the use of conventional regression models by capturing complex, nonlinear relationships in the data, no amount of algorithmic finesse or computing power can squeeze out information that is not present. That’s why clinical data alone have relatively limited predictive power for hospital readmissions that may have more to do with social determinants of health.

The apparent solution is to pile on greater varieties of data, including anything from sociodemographics to personal genomics to mobile-sensor readouts to a patient’s credit history and Web-browsing logs. Incorporating the correct data stream can substantially improve predictions, but even with a deterministic (nonrandom) process, chaos theory explains why even simple nonlinear systems cannot be precisely predicted into the distant future. The so-called butterfly effect refers to the future’s extreme sensitivity to initial conditions. Tiny variations, which seem dismissible as trivial rounding errors in measurements, can accumulate into massively different future events. Identical twins with the same observable demographic characteristics, lifestyle, medical care, and genetics necessarily generate the same predictions — but can still end up with completely different real outcomes.

Though no method can precisely predict the date you will die, for example, that level of precision is generally not necessary for predictions to be useful. By reframing complex phenomena in terms of limited multiple-choice questions (e.g., Will you have a heart attack within 10 years? Are you more or less likely than average to end up back in the hospital within 30 days?), predictive algorithms can operate as diagnostic screening tests to stratify patient populations by risk and inform discrete decision making.

Research continues to improve the accuracy of clinical predictions, but even a perfectly calibrated prediction model may not translate into better clinical care. An accurate prediction of a patient outcome does not tell us what to do if we want to change that outcome — in fact, we cannot even assume that it’s possible to change the predicted outcomes.

Machine-learning approaches are powered by identification of strong, but theory-free, associations in the data. Confounding makes it a substantial leap in causal inference to identify modifiable factors that will actually alter outcomes. It is true, for instance, that palliative care consults and norepinephrine infusions are highly predictive of patient death, but it would be irrational to conclude that stopping either will reduce mortality. Models accurately predict that a patient with heart failure, coronary artery disease, and renal failure is at high risk for postsurgical complications, but they offer no opportunity for reducing that risk (other than forgoing the surgery). Moreover, many such predictions are “highly accurate” mainly for cases whose likely outcome is already obvious to practicing clinicians. The last mile of clinical implementation thus ends up being the far more critical task of predicting events early enough for a relevant intervention to influence care decisions and outcomes.

With machine learning situated at the peak of inflated expectations, we can soften a subsequent crash into a “trough of disillusionment” by fostering a stronger appreciation of the technology’s capabilities and limitations. Before we hold computerized systems (or humans) up against an idealized and unrealizable standard of perfection, let our benchmark be the real-world standards of care whereby doctors grossly misestimate the positive predictive value of screening tests for rare diagnoses, routinely overestimate patient life expectancy by a factor of 3, and deliver care of widely varied intensity in the last 6 months of life.

Although predictive algorithms cannot eliminate medical uncertainty, they already improve allocation of scarce health care resources, helping to avert hospitalization for patients with low-risk pulmonary embolisms (PESI) and fairly prioritizing patients for liver transplantation by means of MELD scores. Early-warning systems that once would have taken years to create can now be rapidly developed and optimized from real-world data, just as deep-learning neural networks routinely yield state-of-the-art image-recognition capabilities previously thought to be impossible.

Whether such artificial-intelligence systems are “smarter” than human practitioners makes for a stimulating debate — but is largely irrelevant. Combining machine-learning software with the best human clinician “hardware” will permit delivery of care that outperforms what either can do alone. Let’s move past the hype cycle and on to the “slope of enlightenment,” where we use every information and data resource to consistently improve our collective health.

Source: The New England Journal of Medicine

Past, Present and Future of AI / Machine Learning (Google I/O ’17)

 

We are in the middle of a major shift in computing that’s transitioning us from a mobile-first world into one that’s AI-first. AI will touch every industry and transform the products and services we use daily. Breakthroughs in machine learning have enabled dramatic improvements in the quality of Google Translate, made your photos easier to organize with Google Photos, and enabled improvements in Search, Maps, YouTube, and more.

 

Our machines now have knowledge well never understand

The new availability of huge amounts of data, along with the statistical tools to crunch these numbers, offers a whole new way of understanding the world. Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all.

OnTheGo

So wrote Wired’s Chris Anderson in 2008. It kicked up a little storm at the time, as Anderson, the magazine’s editor, undoubtedly intended. For example, an article in a journal of molecular biology asked, “…if we stop looking for models and hypotheses, are we still really doing science?” The answer clearly was supposed to be: “No.”

But today — not even a decade since Anderson’s article — the controversy sounds quaint. Advances in computer software, enabled by our newly capacious, networked hardware, are enabling computers not only to start without models — rule sets that express how the elements of a system affect one another — but to generate their own, albeit ones that may not look much like what humans would create. It’s even becoming a standard method, as any self-respecting tech company has now adopted a “machine-learning first” ethic.

We are increasingly relying on machines that derive conclusions from models that they themselves have created, models that are often beyond human comprehension, models that “think” about the world differently than we do.

But this comes with a price. This infusion of alien intelligence is bringing into question the assumptions embedded in our long Western tradition. We thought knowledge was about finding the order hidden in the chaos. We thought it was about simplifying the world. It looks like we were wrong. Knowing the world may require giving up on understanding it.

Models Beyond Understanding

In a series on machine learning, Adam Geitgey explains the basics, from which this new way of “thinking” is emerging:

[T]here are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data.”

For example, you give a machine learning system thousands of scans of sloppy, handwritten 8s and it will learn to identify 8s in a new scan. It does so, not by deriving a recognizable rule, such as “An 8 is two circles stacked vertically,” but by looking for complex patterns of darker and lighter pixels, expressed as matrices of numbers — a task that would stymie humans. In a recent agricultural example, the same technique of numerical patterns taught a computer how to sort cucumbers.

Then you can take machine learning further by creating an artificial neural network that models in software how the human brain processes signals.[1] Nodes in an irregular mesh turn on or off depending on the data coming to them from the nodes connected to them; those connections have different weights, so some are more likely to flip their neighbors than others. Although artificial neural networks date back to the 1950s, they are truly coming into their own only now because of advances in computing power, storage, and mathematics. The results from this increasingly sophisticated branch of computer science can be deep learning that produces outcomes based on so many different variables under so many different conditions being transformed by so many layers of neural networks that humans simply cannot comprehend the model the computer has built for itself.

Yet it works. It’s how Google’s AlphaGo program came to defeat the third-highest ranked Go player in the world. Programming a machine to play Go is more than a little daunting than sorting cukes, given that the game has 10^350 possible moves; there are 10^123 possible moves in chess, and 10^80 atoms in the universe. Google’s hardware wasn’t even as ridiculously overpowered as it might have been: It had only 48 processors, plus eight graphics processors that happen to be well-suited for the required calculations.

AlphaGo was trained on thirty million board positions that occurred in 160,000 real-life games, noting the moves taken by actual players, along with an understanding of what constitutes a legal move and some other basics of play. Using deep learning techniques that refine the patterns recognized by the layer of the neural network above it, the system trained itself on which moves were most likely to succeed.

Although AlphaGo has proven itself to be a world class player, it can’t spit out practical maxims from which a human player can learn. The program works not by developing generalized rules of play — e.g., “Never have more than four sets of unconnected stones on the board” — but by analyzing which play has the best chance of succeeding given a precise board configuration. In contrast, Deep Blue, the dedicated IBM chess-playing computer, has been programmed with some general principles of good play. As Christof Koch writes in Scientific American, AlphaGo’s intelligence is in the weights of all those billions of connections among its simulated neurons. It creates a model that enables it to make decisions, but that model is ineffably complex and conditional. Nothing emerges from this mass of contingencies, except victory against humans.

As a consequence, if you, with your puny human brain, want to understand why AlphaGo chose a particular move, the “explanation” may well consist of the networks of weighted connections that then pass their outcomes to the next layer of the neural network. Your brain can’t remember all those weights, and even if it could, it couldn’t then perform the calculation that resulted in the next state of the neural network. And even if it could, you would have learned nothing about how to play Go, or, in truth, how AlphaGo plays Go—just as internalizing a schematic of the neural states of a human player would not constitute understanding how she came to make any particular move.

Go is just a game, so it may not seem to matter that we can’t follow AlphaGo’s decision path. But what do we say about the neural networks that are enabling us to analyze the interactions of genes in two-locus genetic diseases? How about the use of neural networks to discriminate the decay pattern of single and multiple particles at the Large Hadron Collider? How the use of machine learning to help identify which of the 20 climate change models tracked by the Intergovernmental Panel on Climate Change is most accurate at any point? Such machines give us good results — for example: “Congratulations! You just found a Higgs boson!” — but we cannot follow their “reasoning.”

Clearly our computers have surpassed us in their power to discriminate, find patterns, and draw conclusions. That’s one reason we use them. Rather than reducing phenomena to fit a relatively simple model, we can now let our computers make models as big as they need to. But this also seems to mean that what we know depends upon the output of machines the functioning of which we cannot follow, explain, or understand.

Since we first started carving notches in sticks, we have used things in the world to help us to know that world. But never before have we relied on things that did not mirror human patterns of reasoning — we knew what each notch represented — and that we could not later check to see how our non-sentient partners in knowing came up with those answers. If knowing has always entailed being able to explain and justify our true beliefs — Plato’s notion, which has persisted for over two thousand years — what are we to make of a new type of knowledge, in which that task of justification is not just difficult or daunting but impossible?

Source: backchannel.com

Alexa learns to talk like a human with whispers, pauses & emotion

OnTheGo

Amazon’s Alexa is going to sound more human. The company announced this week the addition of a new set of speaking skills for the virtual assistant, which will allow her to do things like whisper, take a breath to pause for emphasis, adjust the rate, pitch and volume of her speech, and more. She’ll even be able to “bleep” out words – which may not be all that human, actually, but is certainly clever.

These new tools were provided to Alexa app developers in the form of a standardized markup language called Speech Synthesis Markup Language, or SSML, which will let them code Alexa’s speech patterns into their applications. This will allow for the creation of voice apps – “Skills” on the Alexa platform – where developers can control the pronunciation, intonation, timing and emotion of their Skill’s text responses.

Alexa today already has a lot of personality – something that can help endear people to their voice assistants. Having taken a note from how Apple’s Siri surprises people with her humorous responses, Alexa responds to questions about herself, tells jokes, answers to “I love you,” and will even sing you a song if you ask. But her voice can still sound robotic at times – especially if she’s reading out longer phrases and sentences where there should be natural breaks and changes in tone.

As Amazon explains, developers could have used these new tools to make Alexa talk like E.T., but that’s not really the point. To ensure developers make use of the tools as intended – to humanize Alexa’s speaking patterns – Amazon has set limits on the amount of change developers are able to apply to the rate, pitch, and volume. (There will be no high-pitched, squeaks and screams, I guess.)

In total, there are five new SSML tags that can be put into practice, including whispers, expletive beeps, emphasis, sub (which lets Alexa say something other than what’s written), and prosody. That last one is about controlling the volume, pitch and rate of speech.

To show how these changes could work in a real Alexa app, Amazon created a quiz game template that uses the new tags, but can also be modified by developers to test out Alexa’s new voice tricks.

In addition to the tags, Amazon also introduced “speechcons” to developers in the U.K. and Germany. These are special words and phrases that Alexa knows to express in a more colorful way to make her interactions engaging and personal. Some speechcons were already available in the U.S., for a number of words, like “abracadabra!,” “ahem,” “aloha,” “eureka!,” “gotcha,” “kapow,” “yay,” and many more.

But with their arrival in the new markets, Alexa Skill developers can use regionally specific terms such as “Blimey” and “Bob’s your uncle,” in the U.K. and “Da lachen ja die Hühner” and “Donnerwetter” in Germany.

There are now over 12,000 Alexa Skills on the marketplace but it’s unknown how many developers will actually put the new voice tags to work.

After all, this humanization of Alexa relies on having an active developer community. And that’s something that requires Amazon to do more than build out clever tricks to be put to use – it has to be able to support an app economy, where developers don’t just build things for fun, but because there are real businesses that can be run atop Amazon’s voice computing infrastructure.

Source: techcrunch.com

Machine learning algorithms surpass doctors at predicting heart attacks

Between 15 and 20 million people die every year from heart attacks and related illnesses worldwide, but now, artificial intelligence could help reduce that number with better predictive abilities.

OnTheGo

Doctors are not clairvoyant, but it looks like technology is getting awfully close. Thanks to a team of researchers at the University of Nottingham in the United Kingdom, we could be closer than ever before to predicting the future when it comes to patients’ health risks. The scientists have managed to develop an algorithm that outperforms medical doctors when it comes to predicting heart attacks. And this, experts say, could save thousands or even millions of lives every year.

As it stands, around 20 million people fall victim to cardiovascular disease, which includes heart attacks, strokes, and blocked arteries. Today, doctors depend on guidelines similar to those of the American College of Cardiology/American Heart Association (ACC/AHA) in order to predict individuals’ risks. These guidelines include factors like age, cholesterol level, and blood pressure.

Unfortunately, that’s often insufficient. “There’s a lot of interaction in biological systems,” Stephen Weng, an epidemiologist at the University of Nottingham, told Science Magazine. And some of them make less sense than others. “That’s the reality of the human body,” Weng continued. “What computer science allows us to do is to explore those associations.”

In employing computer science, Weng took the ACC/AHA guidelines and compared them to four machine-learning algorithms: random forest, logistic regression, gradient boosting, and neural networks. The artificially intelligent algorithms began to train themselves using existing data to look for patterns and create their own “rules.” Then, they began testing these guidelines against other records. And as it turns out, all four of these methods “performed significantly better than the ACC/AHA guidelines,” Science reports.

The most successful algorithm, the neural network, actually was correct 7.6 percent more often than the ACC/AHA method, and resulted in 1.6 percent fewer false positives. That means that in a sample size of around 83,000 patient records, 355 additional lives could have been saved.

“I can’t stress enough how important it is,” Elsie Ross, a vascular surgeon at Stanford University in Palo Alto, California, who was not involved with the work, told Science, “and how much I really hope that doctors start to embrace the use of artificial intelligence to assist us in care of patients.”

Source: digitaltrends.com

Machine learning creates living atlas of the planet

Machine learning, combined with satellite imagery and Cloud computing, is enabling understanding of the world and making the food supply chain more efficient.

OnTheGo

There are more than 7 billion people on Earth now, and roughly one in eight people do not have enough to eat. According to the World Bank, the human population will hit an astounding 9 billion by 2050. With rapidly increasing population, the growing need for food is becoming a grave concern.

The burden is now on technology to make up for the looming food crises in the coming decades. But fortunately there is no shortage of ideas and innovative minds are seeking solutions to combat this problem.

Machine learning to the rescue
Descartes Labs, a Los Alamos, New Mexico-based start-up is using machine learning to analyze satellite imagery to predict food supplies months in advance of current methods employed by the US government, a technique that could help predict food crises before they happen.

Descartes Labs pulls images from public databases like NASA’s Landsat and MODIS, ESA’s Sentinel missions and other private satellite imagery providers, including Planet. It also keeps a check on Google Earth and Amazon Web Services public datasets. This continuous up-to-date imagery is referred to as the ‘Living Atlas of the Plant’.

The commercial atlas, designed to provide real-time forecasts of commodity agriculture, uses decades of remotely sensed images stored on the Cloud to offer land use and land change analysis.

Descartes Labs cross-references the satellite information with other relevant data such as weather forecasts and prices of agricultural products. This data is then entered into the machine learning software, tracking and calculating future food supplies with amazing accuracy. By processing these images and data via their advanced machine learning algorithm, Descartes Labs collect remarkably in-depth information such as being able to distinguish individual crop fields and determining the specific field’s crop by analyzing how the sun’s light is reflecting off its surface. After the type of crop has been established, the machine learning program then monitors the field’s production levels.

“With machine learning techniques, we look at tons of pixels from satellites, and that tells us what’s growing,” says Mark Johnson, CEO and Co-founder, Descartes Labs.

How to tackle a data deluge
The total database includes approximately a petabyte — or 1015 bytes — of data. Descartes has actually reprocessed the whole 40-year archive starting with the first Landsat satellite imagery to offer completely Cloud-free view of land use and land change to create this ‘Living Atlas of the Planet’.

The data platform is said to have analyzed over 2.8 quadrillion multispectral pixels for this. It enables processing at petabytes per day rates using multi-source data to produce calibrated, georeferenced imagery stacks at desired points in time and space that can be used for pixel level or global scale analysis or for visualizing or measure changes such as floods, or changes in the condition of crops. “The platform is built for analysis. It is not built to store the data. This is a vastly different philosophy than traditional data platforms,” says Daniela Moody, Remote Sensing and Machine Learning Specialist, Descartes Labs.

The platform churns out imageries at specific locations for specific time at different wavelengths, thus offering unique insights into land cover changes over broad swaths of land. For instance, the NDVI (normalized difference vegetation index) reveals live green vegetation using a combination of red and near-infrared spectral bands (Figure 2). Combining NDVI with visible spectral bands allows a user to examine the landscape through many lenses. The platform offers both Web and API interfaces. While the Web interface offers options for visualizing data, whereas the API allows the user to interact directly with the data for specific analysis. The platform’s scalable Cloud infrastructure quickly ingests, analyzes, and creates predictions from the imagery.

Change is the only constant
The ability to have such fine-grained data on agricultural production will help in making the food supply chain more efficient. As Descartes Labs adds more geospatial data to its already robust database of earth imagery, these models will get even more accurate. Cloud computing and storage, combined with recent advances in machine learning and open software, are enabling understanding of the world at an unprecedented scale and detail.

Earth is not a static place, and researchers who study it need tools that keep up with the constant change. “We designed this platform to answer the problems of commodity agriculture,” Moody adds, “and in doing so we created a platform that is incredible and allows us to have a living atlas of the world.”

Source: geospatialworld.net