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There is no question that machine learning is at the top of the hype curve. And, of course, the backlash is already in full force: I’ve heard that old joke “Machine learning is like teenage sex; everyone is talking about it, no one is actually doing it” about 20 times in the past week alone.
But from where I sit, running a company that enables a huge number of real-world machine-learning projects, it’s clear that machine learning is already forcing massive changes in the way companies operate.
It’s not just futuristic-looking products like Siri and Amazon Echo. And it’s not just being done by companies that we normally think of as having huge R&D budgets like Google and Microsoft. In reality, I would bet that nearly every Fortune 500 company is already running more efficiently — and making more money — because of machine learning.
So where is it happening? Here are a few behind-the-scenes applications that make life better every day.
Making user-generated content valuable
The average piece of user-generated content (UGC) is awful. It’s actually way worse than you think. It can be rife with misspellings, vulgarity or flat-out wrong information. But by identifying the best and worst UGC, machine-learning models can filter out the bad and bubble up the good without needing a real person to tag each piece of content.
It’s not just Google that needs smart search results.
A similar thing happened a while back with spam emails. Remember how bad spam used to be? Machine learning helped identify spam and, basically, eradicate it. These days, it’s far more uncommon to see spam in your inbox each morning. Expect that to happen with UGC in the near future.
Pinterest uses machine learning to show you more interesting content. Yelp uses machine learning to sort through user-uploaded photos. NextDoor uses machine learning to sort through content on their message boards. Disqus uses machine learning to weed out spammy comments.
Finding products faster
It’s no surprise that as a search company, Google was always at the forefront of hiring machine-learning researchers. In fact, Google recently put an artificial intelligence expert in charge of search. But the ability to index a huge database and pull up results that match a keyword has existed since the 1970s. What makes Google special is that it knows which matching result is the most relevant; the way that it knows is through machine learning.
But it’s not just Google that needs smart search results. Home Depot needs to show which bathtubs in its huge inventory will fit in someone’s weird-shaped bathroom. Apple needs to show relevant apps in its app store. Intuit needs to surface a good help page when a user types in a certain tax form.
Successful e-commerce startups from Lyst to Trunk Archive employ machine learning to show high-quality content to their users. Other startups, like Rich Relevance and Edgecase, employ machine-learning strategies to give their commerce customers the benefits of machine learning when their users are browsing for products.
Engaging with customers
You may have noticed “contact us” forms getting leaner in recent years. That’s another place where machine learning has helped streamline business processes. Instead of having users self-select an issue and fill out endless form fields, machine learning can look at the substance of a request and route it to the right place.
Big companies are investing in machine learning … because they’ve seen positive ROI.
That seems like a small thing, but ticket tagging and routing can be a massive expense for big businesses. Having a sales inquiry end up with the sales team or a complaint end up instantly in the customer service department’s queue saves companies significant time and money, all while making sure issues get prioritized and solved as fast as possible.
Understanding customer behavior
Machine learning also excels at sentiment analysis. And while public opinion can sometimes seem squishy to non-marketing folks, it actually drives a lot of big decisions.
For example, say a movie studio puts out a trailer for a summer blockbuster. They can monitor social chatter to see what’s resonating with their target audience, then tweak their ads immediately to surface what people are actually responding to. That puts people in theaters.
Another example: A game studio recently put out a new title in a popular video game line without a game mode that fans were expecting. When gamers took to social media to complain, the studio was able to monitor and understand the conversation. The company ended up changing their release schedule in order to add the feature, turning detractors into promoters.
How did they pull faint signals out of millions of tweets? They used machine learning. And in the past few years, this kind of social media listening through machine learning has become standard operating procedure.
What’s next?
Dealing with machine-learning algorithms is tricky. Normal algorithms are predictable, and we can look under the hood and see how they work. In some ways, machine-learning algorithms are more like people. As users, we want answers to questions like “why did The New York Times show me that weird ad” or “why did Amazon recommend that funny book?”
In fact, The New York Times and Amazon don’t really understand the specific results themselves any more than our brains know why we chose Thai food for dinner or got lost down a particular Wikipedia rabbit hole.
If you were getting into the machine-learning field a decade ago, it was hard to find work outside of places like Google and Yahoo. Now, machine learning is everywhere. Data is more prevalent than ever, and it’s easier to access. New products like Microsoft Azure ML and IBM Watson drive down both the setup cost and ongoing cost of state-of-the-art machine-learning algorithms.
At the same time, VCs have started funds — from WorkDay’s Machine Learning fund to Bloomberg Beta to the Data Collective — that are completely focused on funding companies across nearly every industry that use machine learning to build a sizeable advantage.
Most of the conversation about machine learning in popular culture revolves around AI personal assistants and self-driving cars (both applications are very cool!), but nearly every website you interact with is using machine learning behind the scenes. Big companies are investing in machine learning not because it’s a fad or because it makes them seem cutting edge. They invest because they’ve seen positive ROI. And that’s why innovation will continue.
Anyone, at any level, who aims to make an impact, buck the status quo, and define his or her industry can take a page from Fast Company cofounder Bill Taylor’s book. He addressed top-level IT professionals in his recent Microsoft Virtual North America CIO Summit keynote speech, but his advice is relevant to professionals across sectors. Like a true sage, Taylor asked attendees to consider a variety of questions and seek the answers within themselves to reach greatness. These three questions offer a roadmap for those looking to raise their game to the next level.
Instead of focusing on being the best player, how can you redefine the game you’re playing?
Changing the nature of the competition is a great way to get ahead. By “embracing one-of-a-kind ideas in a world filled with me-too copycat thinking,” innovators come out on top. “The job today is not to be the best at what lots of other people already do. It’s to be the only one who does what you do,” asserted Taylor. One example is Umpqua Bank, a regional institution that was smaller than small—just six branches—until Ray Davis took the helm in 1994. Now boasting nearly 400 locations, Umpqua is Oregon’s largest bank, and this change was driven in large part by a focus on culture: the tellers also act as baristas, each transaction concludes with a piece of chocolate, and the branches become community centers after business hours. Umpqua is winning because it dares to redefine a bank as a regional resource.
Changing the nature of the competition is a great way to get ahead. By "embracing one-of-a-kind ideas in a world filled with me-too copycat thinking."
What single sentence will describe you?
Taylor stressed the importance of simplification for both individuals and corporate entities. Inspired by the story of Clare Boothe Luce—who told President Kennedy that “a great man is one sentence” before she asked him what sentence would describe his presidency—Taylor stressed the importance of this kind of simplification for both individuals and corporate entities. This reductionist method forces leaders, companies, or job seekers to zero in on their goals, minus buzzwords and jargon. Once you know how you want to be described, you can make changes to bring that fantasy into reality.
It doesn’t matter what keeps you up at night. What gets you up in the morning?
Taylor was emphatic that success is rooted in caring more than anyone else, whether it’s about your customers, your colleagues, or the way your organization conducts itself in the marketplace. It’s up to companies to provide their employees and their leaders with this motivation.
One exemplar is USAA, the financial services company for active and retired military. Taylor said new employees are immersed in the world of their customers on day one, engaging in a deployment simulation (to develop empathy while learning about the financial issues service people face), eating MREs at training instead of a catered lunch, even volunteering to go through the brutal first day of basic training. USAA takes work that might seem passionless (such as selling renter’s insurance) and gives staff a reason to believe this work is valuable and necessary and important. This directly translates to better customer service and, of course, more revenue.
Taylor summarized his thesis with contributions from two unusual thought leaders: cognitive-psychology pioneer Jerome Bruner and acerbic comedian George Carlin. Bruner once wrote that learning (or as Taylor extrapolated, innovation) “is, most often, figuring out how to use what you already know in order to go beyond what you currently think.” Carlin famously cracked wise about “vuja de”—“the strange feeling that somehow none of this has ever happened before.” Taylor believes fostering this feeling is a way to inspire Bruner-style breakthroughs.
The questions Taylor poses are a few ways to intentionally make the familiar feel unfamiliar, to look at your profession or your career or your industry as you’ve never seen it before. The answers may be surprising.
Metrics That Matter: How Does Technology Affect Government Outcomes?
CIOs need to develop better ways to measure the impact of technology. By Kevin C. Desouza as written on Govtech.com.
There's little doubt that government's already substantial investment in information technology is going to continue to grow as public agencies look for ways to streamline processes, engage with citizens and achieve social outcomes. Chief information officers are going to be required to show not only that IT funds are being expended effectively but also that these resources are driving outcomes that government and the public care about. Consider this question: If you were to invest $1 in a parks program and $1 in the IT department, which would provide a greater return?
Questions like that are going to take on even more importance with the emergence of new technologies (think about drones and self-driving cars), new platforms (consider bitcoin and the future of digital currency), and new tools (such as predictive analytics and the emerging field of algorithmic regulation).
Over the last six months, I have interviewed more than two dozen CIOs and other IT executives across all levels of government on the state of IT metrics for performance management in the public sector. The findings from these interviews are summarized in a new report published by the IBM Center for the Business of Government.
In the report, I offer three overarching recommendations to help CIOs begin to develop meaningful metrics for their organizations or to improve the ones they already use:
CIOs should engage stakeholders -- both internal and external ones -- in creating logic models that outline and trace the impact of IT assets to intermediate outputs and ultimately to outcomes that matter. In doing so, CIOs should engage with clients to develop client-specific metrics. They also should strive to gather input to gain a better understanding of how IT is perceived by stakeholders.
CIOs should develop networks to share data on IT performance, both internally and with other communities with similar populations, industries, demographics or needs. These networks offer CIOs an opportunity to gain insights about experiences, challenges and good ideas; benchmark or compare metrics; and gain buy-in for specific initiatives or projects. Currently, CIO networks at the local level are underdeveloped. Where we have seen these networks utilized, CIOs have used them as learning opportunities and are doing much better in utilizing metrics to improve performance.
CIOs need to work creatively to arrive at innovation metrics. In my research, I found a general lack of metrics to capture innovations associated with IT. CIOs are innovating and have anecdotal evidence to share, but they lack quality methods to measure those innovations. While metrics around innovation are inherently difficult to define and establish, they are needed more than ever. Creativity and constant refinement are essential.
As technology continues to develop, the level of IT innovation will become even more of a key differentiator among governments, particularly across local jurisdictions striving to streamline operations and create sustainable neighborhoods and resilient communities. A key question that will be asked of CIOs is how their investments in technology measure up. Clearly they are going to need to be able to answer that question.
This article was originally published by Governing.
Managed Solution partners with IT staff to offer consulting and government solutionsto improve IT infrastructure to reflect the latest technology.
Solar energy in the United States has seen immense momentum throughout the years. When the Solar Energy Industries Association released its annual report in 2008, it concluded that U.S. solar photovoltaic (PV) capacity reached a total of 1.183 gigawatts — a stellar achievement at the time.
Contrast that figure with today, and the number is dwarfed by the United States’ installed capacity of 21.3 gigawatts, enough energy to power 4.3 million homes.
As to what is powering this widespread adoption, one only needs to look at the residential market. According to recently released research by GTM, 72 percent of the market growth in 2014 is a result of solar tech companies offering diverse financing solutions and easy-to-navigate web platforms. Going solar for homeowners has become as easy as online shopping.
The commercial sector isn’t as fortunate, outside of a few large-scale projects driven by Fortune 500 and utility companies. This impasse is mostly because small and mid-scale companies face a number of complications when it comes to investing in solar — the largest being a lack of easy and cost-efficient methods to evaluate and mitigate the risk of any given project.
Tech is changing that. First it made waves in the residential sector, introducing seamless tech platforms with creative financing options to make solar a reality for homeowners. Now it’s paving a path for similar, widespread national success in the commercial space.
Outpacing Commercial Solar
We often hear of large companies, like Wal-Mart, Amazon and Target, deciding to go solar in a move that makes both economic sense and decreases their carbon footprint. If you were to search for large commercial solar sites, such as major corporate headquarters, metropolitan arenas or vast solar arrays, you wouldn’t be hard pressed to find them. They’re abundant, and they’re generally well publicized as a part of any company’s corporate social-responsibility program.
The future of solar has never looked brighter.
However, just because we know of an Apple or Google going solar doesn’t necessarily mean that the greater commercial solar industry is on a consistent upward swing. It is merely a segment of the market that is able to access renewable energy at scale because of their vast resources and investor relations. A significant portion of small and medium-sized enterprises are not adopting solar at record rates.
In fact, in 2014 the commercial solar market was no longer the leading market segment when it came to installed capacity — that’s when residential took over. This flip of leading markets is due to two facts: one being that residents are able to install PV systems at cost-effective rates thanks to technology advancements in established solar companies, the other being that a significant portion of the commercial market is bottlenecked and untapped.
An untapped market is untapped potential, and the tech industry is beginning to take note.
Bringing Small And Medium-Sized Businesses To The Solar Grid
When it comes to making the actual investment, large corporations have economies of scale that drive down costs and increase efficiencies. Additionally, financing of solar projects for these large corporations is generally easier to come by, typically because they have reliable and accessible public credit ratings that satisfy Wall Street’s risk evaluation and mitigation criteria.
Aside from large commercial facilities, we are left with the small to medium-sized firms that haven’t been able to go solar in similar numbers as the nation’s homeowners and their large corporate brethren. Think restaurants, wineries, galleries, printing shops, local gift stores and local religious community centers.
With complex tariff modeling, demand charges, time-of-use rates and limited roof space, as well as layers upon layers of decision-making, an average sales cycle can eclipse 12-18 months. That is if the deal ever closes.
To top it off, most of these smaller commercial facilities lack those readily available and reliable public credit ratings enjoyed by corporations, so even if they are able to navigate the design process, persuading external investors of the viability of the project can be an uphill battle.
This is all changing, and the credit can be given to technology and innovation. With design and modeling tools from companies like HelioScope, Energy Toolbase, Wiser Capital and the National Renewable Energy Laboratory’s Building Component Library (built in conjunction with Concept 3D), the solar industry is beginning to access a large, often ignored market.
These companies have been able to develop software to streamline a complex design and modeling process, enabling solar systems to seamlessly work for the specific needs of small and medium-sized businesses.
The bulk of whether or not your favorite mom-and-pop sandwich shop down the street is going to go solar, however, largely rests on ensuring the cost of capital falls within acceptable risk tolerances for the deal to deliver required returns.
Again, because these types of facilities typically lack reliable and readily accessible public credit ratings that satisfy investor risk evaluation criteria, the cost of capital has generally been too high for many of these projects to take off.
An untapped market is untapped potential, and the tech industry is beginning to take note.
SolarCity, however, just announced its own foray into this space, utilizing California’s Property Assessed Clean Energy (PACE) financing programs to attach a solar lease agreement to a building’s property, essentially negating the need for a risk-rating score. Unfortunately, this option is currently limited to California.
The greater solution lies in technology. If every facility and project could earn a consistent, transparent and highly automated score, then the entire U.S. market would open up.
My own firm utilizes a proprietary cloud-based platform to optimize savings and determine any given project’s bankability. By automating the process and creating a marketplace for hosts, installers and investors, we have successfully brought new capital into a once stagnant market.
This underserved market is driving a new boom in solar. According to the 2015 Solar Investment Index, for example, a staggering 83 percent of investors will make an investment in solar a priority in the next five years, with one in five already having made commercial solar investment a priority in the same timeframe.
A Bright Future
It’s becoming increasingly clear that the new boom in solar lies in the innovation and disruptive nature of tech bringing down the cost of capital required to make more solar projects possible, as well as simplifying the entire design and sales cycle.
This is true of both markets. Homeowners have been able to reap the joys of lower electricity costs while ensuring a positive impact on the environment, and now the small and medium-sized business market is starting to enjoy similar benefits.
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Microsoft researcher Jamie Shotton honored as one of MIT Tech Review Innovators under 35
Jamie Shotton at Microsoft Research Cambridge is among the latest MIT Technology Review’s Innovators under 35, a distinction that goes to exceptionally talented young innovators whose work the editors believe has the greatest potential to transform the world. Previous winners include Google co-founders Larry Page and Sergey Brin and Facebook co-founder Mark Zuckerberg.
Shotton was among the researchers who played a key role in bringing Kinect to market. The system has been a differentiating feature for Xbox, and it’s also been used in countless other areas, from improving healthcare to making meetings more productive. Shotton has continued to build on that work with a more recent project called Handpose, which aims to track hand motions to millimeter precision.
Find out more about Shotton and his work over on Inside Microsoft Research.
The US Food and Drug Administration (FDA) protects and promotes public health. The agency, which receives 100,000 handwritten reports of adverse drug affects each year, needed a way to make the data entry process more efficient and reduce costs. By using AWS, the FDA and AWS Partner Captricity quickly turn manual reports into machine-readable information with 99.7% accuracy, reducing costs from $29 per page to $0.25 per page.
US FDA Brings Scale, and Cost Effective Innovation to New Programs Utilizing AWS Cloud
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