Machine learning is the newest thing at BYU, thanks to the work of engineer Dah-Jye Lee, who has created an algorithm that allows computers to learn without human help. According to Lee, his algorithm differs from others in that it doesn’t specify for the computer what it should or shouldn’t look for. Instead, his program simply feeds images to the computer, letting it decide on its own what is what.
Similar to how children learn differences between objects in the world around them in an intuitive way, Lee uses object recognition to show the computer various images but doesn’t differentiate between them. Instead, the computer is tasked with doing this on its own. According to Lee:
“It’s very comparable to other object recognition algorithms for accuracy, but, we don’t need humans to be involved. You don’t have to reinvent the wheel each time. You just run it.”
Of course, computers can’t think, reason, or rationalize in quite the same way as humans, but researchers at Carnegie Mellon University are using Computer Vision and Machine Learning as ways of optimizing the capabilities of computers.
NEIL’s task isn’t so much to deal with hard data, like numbers, which is what computers have been doing since they first were created. Instead, NEIL goes a step further, translating the visual world into useful information by way of identifying colors and lighting, classifying materials, recognizing distinct objects, and more. This information then is used to make general observations, associations, and connections, much like the human mind does at an early age.
While computers aren’t capable of processing this information with an emotional response–a critical component that separates them from humans–there are countless tasks that NEIL can accomplish today or in the near future that will help transform the way we live. Think about it: how might Computer Vision and Machine Learning change the way you live, work, and interact with your environment?
Most intriguing is the idea that NFLers have not embraced these technologies as readily as their pro sports peers because the complexity of the game defies decoupling teamwork into discrete actions readily attributed to individual players.
Think of offensive linemen working together to protect quarterbacks and running backs. Or defensive linebackers who don’t get many tackles, but make it difficult for the offense to execute.
For this reason, many NFL coaches prefer to assess players based on how they look on film.
Over time, however, this resistance to analytics is likely to fade as machine learning based applications that use computer vision prove their value and become easier to use.
In fact, it could simply be a matter of identifying more subtle metrics to extract and analyze that previously evaded human detection.
For example, during the 2012 playoffs, the Wall Street Journal’s John Letzig reported on how MLB used motion-analysis software from Sportvision Inc. to quantify an outfielder’s ability to get a jump on fielding fly balls.
Given the rich data complexity of football, it’s hard to imagine coaches not eating up algorithm-powered, in-situ forecasts that take player stats, weather and game scenarios into account and identify those variables most likely to influence what happens next.
Or team management not angling for competitive advantage at the lowest possible cost by pinpointing those overlooked, game-deciding metrics that don’t correlate with salary levels like fourth down conversions (just like on-base percentage was the key focus in Moneyball).
In other instances, humans can request recalibration of the algorithms so video tracking models ignore what they consider to be noise and add additional factors they view as pivotal.
Part of football’s very appeal is its complexity and the many inter-dependencies that make it tick. And so it’s a natural for the video scrutiny and data mining that computer vision and machine learning make possible.
Are you involved in an activity where many individuals come together to form a whole greater than the sum of its parts?
How could analysis of its finer points of interaction unlock hidden value in your business?
Nancy Houser, writing for Digital Journal, surveys the landscape of cognitive computing and provides a lucid view of what to expect over the next 5 years.
A key theme is that of computers moving from glorified calculators to thinking machines that augment all 5 of your senses.
For example, imagine computers responding to your inquiry with richer, more valuable outputs like the texture of fabric or the aroma of fresh cut grass.
What’s making it possible to enhance and extend our senses with intelligence is the convergence of computer vision, machine learning, big data, speech recognition, natural language processing, smartphones and biometric sensors.
One hurdle to humanizing computers has been making the leap from performing predefined tasks based on programmed logic to handling new tasks autonomously without dedicated software programs already in place. (e.g., unsupervised machine learning.)
Towards bridging the gap, Houser discusses the promising work of Prof. Chris Eliasmith on computing efforts to reverse engineer the biology of the mammalian brain.
In the past, human brain structure and operation has been replicated in fine detail to accomplish tasks like handwriting recognition, adding via counting, and even interpreting patterns associated with higher-level intelligence.
Now, by adding interfaces that sense touch, pressure and heat, we’re beginning to see more human-like computers that can park cars and perform biometric security functions. IBM is already at work on cognitive computing applications for retailers.
In some respects, cognitive computing could be the near term forerunner of cyborg singularity speculated in the Google hire of Ray Kurzweil.
So, how do you see, hear, smell, touch, taste cognitive computing in the future of your business?
TechCrunch’s Leena Rao reports on ZestFinance, a startup poised to transform the payday loan industry with a new underwriting model that yields more accurate assessments of a borrower’s creditworthiness.
The result? Greater access to credit for more people at more affordable rates and fewer defaults.
Founded by former Google CIO, Douglas Merrill, ZestFinance weaves together traditional credit scoring with big data analysis, machine learning and human expertise.
From the lender’s point of view, the value proposition is the ability to better:
Quantify a borrower’s likelihood to repay
Manage the risks of their loan portfolios
So far, the model outperforms current industry best practice with a 54% lower default rate amid twice the approval rate for loans.
The human element comes in the form of ZestFinance’s team of predictive modelers who are experts in mathematics, computer science and physics.
As machine learning algorithms uncover thousands of variables, the team looks at them in the context of patterns and trends they are seeing before releasing them into multiple big data models that run in parallel.
In the process, load decisions are returned in minutes and ZestFinance continually improves its algorithms.
The challenge of extracting meaningful information from an ever-growing Internet awash in languages, dialects, and knowledge domains is clearly, too much for our brains to handle.
And traditional approaches are simply not up to the task.
However, a combination of statistical methods, data mining and machine learning could help change all that.
Mari-Sanna Paukkeri, a doctoral candidate at the Aalto University Department of Information and Computer Science in Finland has developed computational methods of text processing that are independent of any language or knowledge domain.
Languages share certain building blocks: symbols form words and words aggregate into sentences. Algorithms developed by Paukkeri analyze massive bodies of text and discover patterns to the presence of words and the structure of sentences. As a result, the meaning of specific words and sentences can be inferred.
To date, computational approaches to natural language processing have typically relied on rules defined in advance. Instead, Paukkeri’s algorithms use unsupervised machine learning to uncover meaning from statistical dependencies and structures that exist in the dataset with no help from data pre-processing or human intervention of any kind.
A familiar use of unsupervised machine learning and natural language processing would be the ability of Google News to bundle related new stories on any topic the user requests.
As such, Paukkeri’s methods have the potential to serve global corporations particularly well because they can glean meaningful insights from vast storehouses of data across multiple languages and knowledge domains.
Paukkeri has even studied how a search engine could ascertain if the user is an expert or a layperson and return suitable results by automatically assessing the difficulty of comprehension in the text it finds.