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Anik Bose (BGV General Partner) and Niranjan Venkatesan (BGV intern and Kellogg MBA student) share their perspective on innovation and value creation in computer vision. Computer vision is defined as tasks that include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions. In layman’s terms, computer vision is an arm of artificial intelligence (A.I.) that focuses on enabling machines to “understand” images by processing and analyzing them on a pixel-by-pixel basis, rather than relying on human-controlled categorization data, such as keywords and descriptions. Getting computers to recognize objects just like human beings do remains elusive; 100% accuracy and reliability is almost impossible. As a reference point, the human eye delivers 91% recognition. New technologies such as deep learning are evolving that promise to increase accuracy and reliability dramatically, but these technologies need more research before they can become mainstream. These technologies are modeled on a neural network and will allow the conversion of an image to text or speech. Deep learning will also enable the conversion of text or speech to image or video and the two together can enable tagging, searching, and indexing of a video or image just like text. Some of these new software technologies can deliver a recognition accuracy of 95%. Intellivision, a BGV portfolio company, is able to deliver 98% accuracy for specific vertical applications. Early M&A activity in the computer vision space by larger companies is a lead indicator of the large market potential. The most prominent acquisition being Intel’s recent $15bn acquisition of Mobile eye. Adoption Challenges While the computer vision market forecasts are significant, we believe there are several adoption challenges that must be addressed for the market opportunity to be realized. Key adoption factors include image data privacy concerns, lack of data accuracy, quality and ubiquity, automotive regulations, evolving industry standards and dependence on analytic platforms and applications. These factors, along with operational challenges such as costs, increases in video/image data driven by higher security needs, inability to maintain and fit legacy systems to the new requirements and the difficulties associated with scaling pilots in addition to high bandwidth requirements for hardware, are all rate limiting factors influencing the timing and size of the actual market opportunity. The Venture Investment Opportunity We believe that the computer vision technology stack can be broken down into three layers: a) hardware – This includes cameras, sensors, chipsets and video cards; b) solution frameworks – This includes image-processing algorithms, analytics/deep learning algorithms; c) application – This includes facial recognition, AR/VR and 3D imaging applications. This stack must be applied to proprietary data needed to train the algorithms required to solve specific customer problems. Access to such data will be the long-term differentiator for the winning companies, sometimes more important than the technology stack itself. We believe that there are six specific areas where innovative startups can play a strong role in value creation. These are:
  • Robotics Vision – Driven by demands for the following: safety, quality, reliability, and ease of use, cost efficiencies, benefits of 3D over 2D technology, rapid deployment, and penetration of smart camera in retrofit robotic systems. Key early adopter industries will be automotive and food processing. 6dbytes is an innovative robotics software orchestration company that is targeting the food-processing industry.
  • Visual inspection/Machine Vision – Manufacturers prefer machine vision for visual inspections for their high speed and repeatability of measurements. This accuracy, along with the ability to safely and reliably identify flows and defects in products without disrupting or delaying processes, will create strong demand for computer vision. Key early adopter segments are likely to be the electronics and automotive industries. Drishti is another innovative startup that aims to revolutionize the manufacturing assembly line by digitizing human actions based on technology that can enable non-intrusive and real time observation.
  • Augmented Reality – Applications such as improved logistics in a warehouse, remote monitoring, trouble-shooting and hands free access to contextual information are expected to drive the adoption of AR. Retail and transportation are expected to be early adopter segments. Augment is a startup seeking to transform the retail shopping experience with AR. They are one of the first companies targeting the enterprise B2B space with an end- to-end AR platform.
  • Video Analytics – Intelligent video surveillance to respond to rising threats along with increased demand for business intelligence and adoption of cloud and analytics are also creating the need for computer vision technology. Early adopter markets are likely to be public safety and defense. Intellivision is an innovative BGV portfolio company that provides intelligent video analytics and smart camera solutions for retail, smart home and IOT markets.
  • 3D Imaging – The automotive and construction sectors are progressively adopting 3D imaging solutions for designing, previewing and rectifying the final version of the end products. Fast-paced adoption is expected in healthcare as well for surgical applications and diagnosis.
  • Image/Facial Recognition – Applications will increasingly value code recognition, digital image processing, object recognition, pattern recognition and optical character recognition.   Media and entertainment is expected to be a lead early adopter segment. Finally, facial recognition will be driven by applications such as law enforcement, surveillance, mobile device authentication as well as targeted advertising in retail.
In conclusion, we believe that the analysis of content via computer vision and artificial intelligence is no longer a concept being discussed only in academia. It is an area where we expect to see significant startup innovations that will empower and disrupt existing industries over the next ten years.
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Garrett Gafke, President and CEO of IdentityMind Global, an on-demand online risk management and compliance automation platform. The regulatory focus of the new administration can best be divided into two categories: regulations that address the economy and regulations that address security. In the first bucket are regulations and policies that affect what businesses pay from a tax perspective, and the operational regulations that affect their business practices (e.g. safety, quality, environmental). In the second group are regulations that affect things like cybercrime, human trafficking and financial crimes (e.g. the funding of terrorism, money laundering). In this post, we’ll focus on the security side, specifically on the technology that is being developed to address this problem directly: RegTech. RegTech is the vehicle to simplify and automate regulatory compliance around anti-money laundering and sanctions screening, among others. For instance, the new startups in online lending, online banking, virtual currency and the rest of the companies creating the broader fintech universe have been, up to this point, largely unregulated. RegTech provides the ability for these companies to now have the capability to comply with applicable regulations and to manage risk without having to over-hire, dramatically lower their margins, or worry about what happens as they reach internet scale transaction volumes. The traditional financial services industry can also benefit. While it already has regulatory processes in place, it also carries a ton of cost. Its processes are powered by people connected to legacy systems, which makes them more expensive, less scalable and uneven in quality. RegTech can help them make a difference in terms of effectiveness, thus improving their bottom line with a goal to create real effectiveness and efficiency. However, RegTech companies have generally not had a seat at the table when it comes to figuring out what makes sense in terms of compliance policy, and what is possible in terms of compliance. A seat at the table is important because:
  • Government technology tends to be antiquated. It is in the administration’s best interest to ensure that its platforms are brought up to date and can handle the complexity and scale required by today’s regulatory environment and online businesses. And it’s important that the U.S. government understands not only the state of current technology but also what is coming next so that it can update regulations to take RegTech into account for maximum effect. From a regulatory perspective, RegTech has the potential of shifting the mindset from reactive supervision to real-time preventive and proactive monitoring.
  • Fintech companies have escaped most regulation for the past six years or so. While regulation is coming, imposing regulation for which compliance is too difficult could damage this sector of the economy and hinder innovation. Understanding what is possible through technology is essential because company officers could potentially face prison time if they are not able to meet their compliance responsibilities.
A more appropriate regulatory framework is needed to bolster financial innovation. The framework must ensure the health of the new fintech industry and take advantage of technology innovations to reduce the financial system compliance cost. An inclusive approach with a broader set of voices and stakeholders makes the most sense. And with a large number of banks in the U.S., many of them headquartered overseas, the global ramifications of a U.S.-driven policy cannot be ignored.
Forbes Finance Council is an invitation-only organization for executives in successful accounting, financial planning and wealth management firms.
Source: https://www.forbes.com/sites/forbesfinancecouncil/2017/03/27/making-the-most-of-the-new-regulatory-environment/#3fe76c193a9c
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Guy Yehiav, CEO of Profitect, leading provider of prescriptive analytics for retailers shares his perspective on Prescriptive analytics software that can help retailers attain some of the unrealized promises that emerged with passive RFID technology. Oct 19, 2016 – For many businesses, data is a precious commodity. Its ability to reveal insights about an organization is unprecedented, which is why it has become a major driver for the adoption of the Internet of Things. The IoT’s network of interconnected devices can produce a remarkable amount of raw data, which is proving to be very attractive for business leaders across industries. Earlier this year, in fact, Gartner released a report revealing that 43 percent of businesses will have launched their own IoT strategy by the end of this year. But are CIOs and their counterparts thinking long-term about their IoT solutions? There are plenty of reasons to invest in the IoT, but without pairing the data it generates with the right tools, you’d effectively have the world’s most valuable book without any way to read it. Data collection alone is not enough to draw value; the real value is found in translating that information into insights and actions, which can be taken immediately to improve operations. That’s what makes prescriptive analytics a natural fit for any IoT solution. Once integrated with IoT devices, prescriptive analytics tools collect information and intelligently identify trends. The idea is to ingest the data, look for patterns of behavior through machine learning and spit out a descriptive insight, combined with a prescriptive action. These are then delivered in real time to the most relevant person. In simple terms, this eliminates the need for a data scientist to review and submit reports. It creates a constant loop of data collection, translation, insight delivery and action. It is, interestingly, similar to what the retail industry is doing with RFID for cold chainmonitoring.
According to IDC, the industry with the highest investment in the IoT in 2015 was discrete manufacturing. How could manufacturers benefit from pairing their solution with a prescriptive analytics tool? Let’s look at car manufacturers, specifically. Through IoT-connected devices, data is collected at every step of production. During a set period, the prescriptive engine identifies that one station is taking X number of seconds longer to complete its task, compared to Y fewer seconds at other stations, resulting in the loss of $Z in total profit. The prescriptive analytics tool then immediately flags this to the appropriate engineer, and provides recommendations on how to fix the problem. While an analytics tool might seem like an obvious step in designing the optimal IoT strategy, remember that this isn’t our first time down this road. What many do not realize is that the Internet of Things as we know it is, in many ways, the second iteration of retail data-collection technology. In the early 2000s, those in the retail industry saw RFID in the same light as the IoT: a means to collect mountains of data on customers and their purchasing habits. Despite investment from several industry giants, it never fully reached its potential. Why? A lack of reporting. In those days, RFID was never matched with any sort of long-term solution to draw meaningful insights. Tools such as prescriptive analytics help IoT technologies, including RFID, succeed where they had failed in the past. The IoT isn’t filtering out and streamlining data—in fact, it is doing the opposite. The sheer volume it is creating is staggering. To maximize the value of the IoT, the data it generates must come paired with the right solution that sifts through the junk and hand-delivers the value. While there are other options, what makes prescriptive analytics the ideal candidate for that task is its instantaneous value-added functionality. Real-time delivery of actions can result in quick fixes which, over time, can save a fortune in profit. Prescriptive analytics is the next logical step in reaping the true value of IoT data. Guy Yehiav is the CEO and chairman of the Board of Profitect, a leading provider of prescriptive analytics. Prior to working at Profitect, Guy Yehiav served as the VP of sales and strategy for Oracle‘s Value Chain Planning Solutions division, where he was responsible for sales, strategy and customer success. Yehiav was also the founder of Demantra US, a global provider of demand-driven planning solutions that was acquired by Oracle in 2006. Previously, he directed the Global Professional Service Group, where he was in charge of creating methodologies and infrastructure through value chain transformations that enabled demand-driven and seamless operations for Fortune 1000 companies. Original Article: http://www.iotjournal.com/articles/view?15108
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In this special guest feature, Guy Yehiav, CEO at Profitect, discusses how prescriptive anaytics holds the keys to efficiency with the least amount of risk and the fastest time to value. Prior to Profitect, Guy Yehiav served as Vice President Sales & Strategy for Oracle’s Value Chain Planning Solutions where he was responsible for sales, strategy and customer success. Guy was also founder of Demantra US, a leading global provider of demand-driven planning solutions, which was acquired by Oracle in 2006. Previously, he directed the Global Professional Service Group where he was in charge of creating methodologies and infrastructure through value chain transformations that enabled demand driven and seamless operations for fortune 1000 companies. (www.profitect.com) Stagnation is a familiar yet unfortunate trap for businesses. The early business model proves successful, profits soar, and a “don’t fix what isn’t broken” mentality sets in. But trends come and go, the market evolves. In order to survive, and even thrive, companies need to consistently seek out new and innovative ways to improve themselves. In that never-ending search, many have turned to the opportunities found in leveraging big data. Across a number of industries, the C-suite often asks itself, “how can we best use the wealth of information we’re already gathering?” Here enters prescriptive analytics. Once implemented, collected data is run through an engine to identify hidden gems of value and is then translated into insight and actions outlining how to maximize efficiency of current business practices. This crowd sourcing method helps professionals understand what exactly is working in the field. Simply put, it helps reduce waste and raise the top & bottom line. What is making prescriptive so attractive is that it does not discriminate between internal and external behaviors. For example, a retailer might leverage prescriptive to determine which sections of a store are receiving the most attention from customers and how to capitalize upon that (i.e. external behaviors). Versus a supply chain manager who uses prescriptive to identify average shipping times which can increase the efficiency of deliveries (i.e. internal behaviors). Furthermore, it democratizes analytics by delivering the information in plain English, right to the person who should see it, rather than requiring a trained professional for interpretation.
 But prescriptive also has the potential to go beyond simple practice improvement. As solution providers create more intelligent engines, they are able to actively identify problem areas that are costing the organization in revenue. To use the retailer as an example once again: prescriptive is able to flag excessive lead times when hot items aren’t being replaced on the shelves fast enough to meet consumer demand. Over time, if retailers don’t act upon these insights, it can cost a small fortune.
So if prescriptive analytics has such potential to improve operations, why isn’t it the superstar of big data? There are, unfortunately, a few misconceptions which are hindering adoption:
  • Time to implementation. This is an issue that applies to a number of analytics solutions, not just prescriptive. Despite a desire to leverage analytics for big data, many decision makers are hesitant to pull the trigger due to a belief that time-to-launch may require up to six months. In fact, there are providers who can have a solution fully integrated and working in two weeks.
  • Return on investment. As with any new technology, business leaders worry that it would not be worth the investment in the long run. However, prescriptive has proven its value time and time again. In one instance, prescriptive was able to save a national grocer $1.8 million annually by recognizing a packaging issue pattern.
Ultimately, as businesses decide how to best leverage their big data, how exactly to implement an analytics strategy will become a necessary conversation among decision makers. With demand growing for a reliable solution, prescriptive holds the keys to efficiency with the least amount of risk and the fastest time to value. For businesses, it is simply a matter of time before market trends force change. Prescriptive analytics can help them stay ahead of the curve. Source: http://insidebigdata.com/2016/09/03/prescriptive-analytics-ultimate-self-help-tool/ Sign up for the free insideBIGDATA newsletter.
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