Vue.ai, a U.S./India startup that develops an AI platform to help online retailers work more efficiently and sell more, has announced a $17 million Series B round.

The investment is led by Falcon Edge Capital  with participation from Japan’s Global Brain and existing backer Sequoia Capital India. Parent company Mad Street Den was founded in 2014 and it raised $1.5 million a year later; Sequoia then bought into the business via an undisclosed deal in 2016. Vue.ai is described as an “AI brand” from Mad Street Den and, all combined, the two entities have now raised $27 million from investors.

In an interview with TechCrunch, Vue.ai CEO and co-founder Ashwini Asokan — who started Mad Street Den  with her husband Anand Chandrasekaran — explained that Vue.ai is a “retail vertical” of Mad Street Den that launched in 2016, and that the company may add “another vertical in a year or two.”

Vue.ai is solely focused on working with online retailers, predominantly in the fashion space, and it does so in a number of ways. That includes expected areas such as automating product tagging and personalized recommendations (based on that tag library), as well as visual search using photos as input and tailored product discovery.

Areas in which Vue.ai also plays that surprised me, at least, include generating human models who wear clothing items — thus saving considerable time, money and effort on photo shoots — and an AI stylist that doesn’t take human form but does learn a user’s style and helps them outfit themselves accordingly.

Tagging and visuals may appear boring, but these are hugely important areas for retailers who have huge amounts of SKUs, items for sale, on their site. Making sure the right person finds the right item is critical to making a sale, and Vue.ai’s goal is to automate as much of that heavy-lifting as possible. Even tagging is essential because it needs to be done consistently if it is to work properly.

Ashwini Asokan, CEO and co-founder of Vue.ai

More than just working correctly, Vue.ai aims to help online retailers, who often run a tight ship in terms of profitability, save money and get new product online and in front of consumer eyeballs quickly.

“These are solutions that optimize the bottom line for retail companies,” said Asokan, who spent over a decade working in the U.S. before returning home to India in 2015. “We are digitizing products 10X faster than you did before… you cannot afford to lose productivity and efficiency, online retail is not somewhere you can lose money.”

“We want to be that data brain mapping digital products,” she added.

Vue.ai is now pushing into new areas, which include advertising and development of videos and marketing content.

“The future of retail is entertainment and the experience economy is the small start of that era,” Asokan said, reflecting on the trend of social media buying through platforms like Instagram  and the rise of live-streaming e-commerce in China.

“The electricity that powers all of these complicated retail interactions is content; we need to understand content and every customer style profile and merchandise,” she added.

Some of Vue.ai’s public customers include Macy’s and Diesel in the U.S., Latin American e-commerce firm Mercadolibre and Indian conglomerate Tata .

Vue.ai is headquartered in Redwood City with an office in Chennai, India. Asokan said it is planning to expand that presence with new locations in Seattle, for tech hires, and Japan and Spain to help provide closer support for customers. The company doesn’t disclose raw numbers, but it said that annual revenue grew by four hundred percent in 2018, which was its third year since incorporation.

Source:TechCrunch

Automation, from robotic process automation to artificial intelligence, is transforming every function of every business in every industry. In fact, according to research from PWC, AI’s impact on business will be greater than the internet. The potential applications are limitless, from individualized customer marketing, to employee screening and selection, to smarter products that collect data, to automated customer support. AI has begun to change organizational processes on a scale that the re-engineering movement of thirty years ago could only imagine. Leaders of businesses that don’t move quickly to capitalize on the power of AI will be left behind.

Despite the many indicators of a transforming marketplace, almost all legacy leaders and board members still hesitate to apply artificial intelligence to corporate strategy. Perhaps wondering whether machines are beginning to complete with high priced-strategy consultants. The answer is yes. In fact, no consulting team, no matter how big, how skilled or how expensive, gather data, analyze it, and create recommendations with the speed and scale of machines. Board members and leaders who don’t believe this can simply look to see the evolution of AI powered marketing, sales and customer support. Adopting an AI powered strategy is the natural next step. No matter the application, the process is similar. The four steps of AI powered strategy:

1. Data

Creating an AI powered strategy is all about using machines and data science to chart a better and more valuable course, as opposed to using people and spreadsheets. The key ingredient is obviously data, and in this case we’re talking about data relevant to corporate strategy. That includes traditional data like financial reports and stock performance, and also alternative data, which can take many forms. Key topics for alternative data include customer sentiment, employee satisfaction, leadership capabilities, digital readiness, and many more.

It’s important to recognize that to get the most out of an AI powered strategy initiative, you need to look beyond your industry peer group to consider at all top performers. Some of the most innovative strategies are best found among today’s unicorn startups that are applying modern business model principles such as AI powered platforms and multi-sided revenue models. Given that companies are crossing industry boundaries more frequently, an industry approach is far too narrow.

Some of this data is publicly available, some is created and owned by the firms themselves, and some can be purchased—data brokers are popping up all over the place. The key questions leaders should ask are:

  • What metrics are more important for our success?
  • What investments do we believe make a difference in our trajectory?
  • What are the unmeasured, intangible items we want to understand?

 2. Analysis

Once you have the data relevant to your strategic aims and your hypotheses about what really matters, you can start your machine learning journey. Unfortunately, machines aren’t self-starters yet. This means you need some smart humans, to teach the smart machines how to think about strategy problems. The competition for top machine learning talent is stiff, but remember that you don’t really need a PhD-level scientist for most machine learning applications. There are a plethora of off-the-shelf tools that a good developer with some relevant experience can apply to your data and problems.

The goal is to begin uncovering the relationship between the data you have, and the outcomes you wish to track. Remember that it’s essential to bring a point of view to your artificial intelligence projects. You don’t want the team to be looking under every rock in hopes of finding insight, but instead to be validating and supporting what you believe to be true. The key questions you should ask are:

  • How will I position a machine learning team organizationally?
  • What are the key beliefs we would like to validate?

3. Prediction

 Once your team has begun creating algorithms that reflect your strategy beliefs (or if proven wrong, algorithms that reflect your updated understanding), you will have a new understanding of what is really driving success. Perhaps you might uncover a relationship between employee and customer satisfaction, or between research and development and revenue growth. Whatever it is, before you can act on this insight, you will want to make sure it is not just descriptive, but also predictive. That is, you want to make sure you are doing more than just describing how things stand now—you want to be sure that your insight can actually help your organization chart its future.

A good way to do this is to examine historical data and see if it does a good job of explaining “what happened next.” For example, at AIM atters we examine how organization’s investment in business models affects their stock performance over future years. This proof point can help you push your machine learning past “interesting,” and into “useful.” The key questions to ask are:

  • What does our algorithm tell us is important for strategy?
  • Do our new insights help us predict the future?
  • Does this insight apply to other companies than our own?

4. Recommendation

Once you are convinced of the predictive power of your machine learning, you can begin to derive recommendations. Transforming products, services or processes is never going to be an easy, overnight task but it does help to have some direction. The best machine learning applications for strategy will indicate clear recommendations based on their algorithms. What changes lead to what results and in what timeframes. Often there are some quick wins—short-term priorities—that will help demonstrate value and gain buy-in for bigger AI projects. Further, unlike consultants, AI powered strategy should be able to predict the quantifiable impact of recommendations based on thousands of data points. To ensure that your autonomous AI strategy agent is doing her job, ask the following questions:

  • Are we identifying processes that can be optimized, relatively inexpensively?
  • Which projects that offer great returns but require more investment?
  • Does our AI quantify the impact of changes we could make?

Once you evaluate all the alternative moves/recommendations that are available to you, and you have weighed the cost/benefit of each, it is time to move onto execution – that’s right, getting done what the machines recommend. Think of it like a GPS – the machines can only recommend routes, but for the time being, you have to do the driving!

Adopting AI is all about people

For all those companies that aren’t Apple, Amazon, Uber or Airbnb, already AI and data powerhouses, adopting AI to power strategy is likely to be real challenge. Therefore, leaders and board members need to consider their own roles in its success. Will the leadership team commit to understanding the technology? To supporting a transformative team in the face of resistance? To funding a machine powered strategy?

Research shows that most leaders are still wary of AI, while simultaneously being afraid of its impact. Now is the time to get started and adapt to these realities. Waiting much longer might leave your company looking a lot like the yellow cab companies—too far behind to ever catch up.

Source: Forbes

There are concepts and technologies that come better as a pair, just like pen and paper, knowledge and power or nuts and bolts. Ok, better, the internet and routing, the blockchain and hashing, or digital twins, say a real time digital replica of a physical device.

The Internet of Things (IoT) and Artificial Intelligence (AI) are also one of those dance partners with perfect connection that are meaningful together. And it does make sense.

IoT is about connecting machines and making use of the data generated from those machines, which is huge. In fact, IDC research group estimates that the amount of data created annually will reach 44 zettabytes in 2020 and up to 180 zettabytes (180 + 21 zeros) by 2025. And there is no end in sight to this flood of data as there are new cIonnected devices every minute.

This data needs to be processed before travelling through the networks to produce useful actions such as traffic control, climate prediction or crime detection. This is where AI needs to play an important role, be it by the means of Machine Learning, Cognitive Computing reasoning, natural language processing, speech recognition and vision (object recognition), human–computer interaction or dialog and narrative generation.

The point is stimulating intelligent behavior in machines. Could this be done some other way? Well, experts say that traditional methods of analyzing structured data are not designed to efficiently process the vast amounts of real-time information that stream from IoT devices. So, yes, quantity is an important factor in this equation.

The next level
That is the reason why the use of AI and Machine Learning is making a splash in Industrial IoT (IIoT) markets. Deloitte underlines that the combination of AI and IoT technology is helping companies “avoid unplanned downtime, increase operating efficiency, enable new products and services, and enhance risk management.” Major vendors of IoT platforms such as Amazon, GE, IBM, Microsoft, Oracle, PTC, and Salesforce have already learned some ropes and integrated AI capabilities.

Figures prove them right. IDC has estimated that in 2019, 40 percent of digital transformation initiatives will use AI services. By 2021, this figure will go up to 75 percent. On the other hand, Gartner says that more than 80 percent of IoT projects will include an AI component by 2022, in comparison to the current 10 percent.

Ricardo Santos, CEO of Heptasense and speaker at Internet Solutions World Congress (IoTSWC), acknowledges that adding AI to IoT brings the solutions to the next level. "IoT without intelligence is just big amounts of unstructured and meaningless data. AI has brought the tools for companies to understand how to leverage the value of all that information,” he says.

“Where Heptasense is concerned, AI enables the analysis of a remarkable amount of video in real-time and helps security teams detect threats without looking randomly at the cameras,” he adds.

There is no doubt that machine learning engines are a huge leap for IoT-based businesses where the ability to analyze, predict and automatically adjust to a particular need is highly prized. We’re not just talking about predictive maintenance, which is probably the brightest showcase of AI used in IIoT.

Artificial Intelligence is now being embedded in everything from logistics to healthcare, transportation, and agriculture and is expected to go much further in a variety of sectors. Platforms are there, but some experts are already arguing that putting AI at the edge, say within the devices themselves or on local servers rather than in the cloud, is the next goldmine. People constantly interacting with their digitally-assisted realities in real time will certainly require dynamic and competitive solutions.

Ambient Computing
Lasse Rouhiainen, author of Artificial Intelligence: 101 Things You Must Know Today About Our Future,holds that AI-powered devices are becoming smaller and able to perform more functions, with greater efficiency, behind the scenes. This is what he calls “Ambient Computing.”

“It’s highly likely that by 2025-2027, so many things in our daily lives will function in an ambient environment that it will be a bit the way electricity is today: something that is always working in the background, which we never think about until it stops working,” he says.

In this scenario, companies should begin to game out the potential impact of pervasive intelligence on their business, even if there are technical constraints, cultural obstacles, organizational barriers to adoption and other philosophical questions to be overcome.

These questions will be discussed at IoTSWC to help companies and organizations create their own roadmap in exploring AI’s new paths. For a reason: The advancement of AI is unstoppable. Considering this technology as a savior is woefully naïve. Yet predicting dystopian outcomes can cause potential solutions to be missed. “The new electricity,” as Andrew Ng described Artificial Intelligence and deep learning, deserves more.

Food for thought.

Source: 

New AI capabilities that can recognize context, concepts, and meaning are opening up surprising new pathways for collaboration between knowledge workers and machines. Experts can now provide more of their own input for training, quality control, and fine-tuning of AI outcomes. Machines can augment the expertise of their human collaborators and sometimes help create new experts. These systems, in more closely mimicking human intelligence, are proving to be more robust than the big data-driven systems that came before them. And they could profoundly affect the 48% of the US workforce that are knowledge workers—and the more than 230 million knowledge-worker roles globally. But to take full advantage of the possibilities of this smarter AI, companies will need to redesign knowledge-work processes and jobs.

Knowledge workers—people who reason, create, decide, and apply insight in non-routine cognitive processes—largely agree. Of more than 150 such experts drawn from a larger global survey on AI in the enterprise, almost 60% say their old job descriptions are rapidly becoming obsolete in light of their new collaborations with AI. Some 70% say they will need training and reskilling (and on-the-job-learning) due to the new requirements for working with AI. And 85% agree that C-suite executives must get involved in the overall effort of redesigning knowledge work roles and processes. As those executives embark on the job of reimagining how to better leverage knowledge work through AI, here are some principles they can apply:

Let human experts tell AI what they care about. Consider medical diagnosis, where AI is likely to become pervasive. Often, when AI offers a diagnosis the algorithm’s reasoning isn’t obvious to the doctor, who ultimately must offer an explanation to a patient—the black box problem. But now, Google Brain has developed a system that opens up the black box and provides a translator for humans. For instance, a doctor considering an AI diagnosis of cancer might want to know to what extent the model considered various factors she deems important—the patient’s age, whether the patient has previously had chemotherapy, and more.

The Google tool also allows medical experts to enter concepts in the system they deem important and to test their own hypotheses. So, for example, the expert might want to see if consideration of a factor that the system had not previously considered—like the condition of certain cells—changed the diagnosis. Says Been Kim, who is helping develop the system, “A lot of times in high-stakes applications, domain experts already have a list of concepts that they care about. We see this repeat over and over again in our medical applications at Google Brain. They don’t want to be given a set of concepts — they want to tell the model the concepts that they are interested in.”

Make models amenable to common sense. As cyber security concerns have mounted, organizations have increased the use of instruments to collect data at various points in their network to analyze threats. However, many of these data-driven techniques do not integrate data from multiple sources. Nor do they incorporate the common-sense knowledge of cyber security experts, who know the range and diverse motives of attackers, understand typical internal and external threats, and the degree of risk to the enterprise.

Researchers at the Alan Turing Institute, Britain’s national institute for data science and artificial intelligence, are trying to change that. Their approach uses a Bayesian model—a method of probabilistic analysis that captures the complex interdependence among risk factors and combines data with judgment. In cybersecurity for enterprise networks, those complex factors include the large number and types of devices on the network and the knowledge of the organization’s security experts about attackers, risk, and much else. While many AI-based cybersecurity systems incorporate human decision-making at the last minute, the Institute’s researchers are seeking ways to represent and incorporate expert knowledge throughout the system. For instance, security analysts’ expert understanding on the motivations and behaviors behind an IP theft attack—and how those may differ from, say, a denial-of-service attack—are explicitly programmed into the system from the start.  In the future, that human knowledge in combination with data sources from machines and networks will be used to train more effective cyber security defenses.

Use AI to help turn novices into recognized experts. AI can rapidly turn beginners into pros. Hewlett Packard demonstrated that when they used their AI lab’s cognitive computing platform to analyze two years’ worth of call data for a client’s call center. The call center was using a queue-based system for routing customer calls, resulting in long wait times and poor quality customer support. The cognitive computing platform was able to determine each agent’s unique “micro-skills”—the agent’s knowledge of a specific kind of customer request, captured from previous calls. These micro-skills are now used to match incoming calls to agents who have successfully processed similar requests. The customer support center has seen a 40 percent improvement in first contact resolution and a 50 percent reduction in the rate of transferred calls.

As customer service agents learn new skills, the AI software automatically updates their expertise, eliminating the need to manually update their skills profile in their HR records. Moreover, as an agent becomes more knowledgeable the software learns to route more complex problems to her. Meanwhile, the software continually reinforces her expertise and the AI’s deduction of “micro-skills” increases the efficiency with which the expert “trains” the software.  It worth pointing out that there are a number of other companies working on this retraining challenge; for example, ASAPP, a well-funded startup, is providing real-time suggestions for customer service reps.

Use data-efficient AI techniques to map the work processes of human experts. Because many types of experts are relatively scarce, they don’t generate large amounts of data. But deep learning and machine learning, on which many AI advances have been based, need mountains of data to train and build systems from the bottom up. In the future we will see more top-down systems that require far less data for their construction and training, enabling them to capture and embody workers’ specialized knowledge.

Consider a recent competition organized by the Laboratory of Medical Image Processing at the University Hospital of Brest and the Faculty of Medicine and Telecom Bretagne in Brittany, France. Competitors vied to see whose medical imaging system could most accurately recognize which tools a surgeon was using at each instant in minimally invasive cataract surgery. The winner was an AI machine vision system trained in six weeks on only 50 videos of cataract surgery—48 operations by a renowned surgeon, one by a surgeon with one year of experience, and one by an intern. Accurate tool recognition systems enable medical personnel to rigorously analyze surgical procedures and look for ways to improve them. Such systems have potential applications in report generation, surgical training, and even real-time decision support for surgeons in the operating room of the future.

As these examples suggest, engineers and pioneers across disciplines are designing AI so that it is more easily trained and evaluated by experts and can incorporate their extremely valuable and often scarce knowledge. To begin to take advantage of these new possibilities, organizations will have to allocate their AI spend accordingly. And to get the greatest value out of both their systems and their knowledge workers they will need to reimagine the way specialists and machines interact. Just as today’s machine learning systems augment the capabilities of ordinary workers, tomorrow’s systems will elevate the performance of knowledge workers to previously unattainable levels of uniform excellence.

Source: Harvard Business Review

The growth of artificial intelligence, robotics and other next-generation automation technologies are prompting some corporate leaders to ask age-old business questions: How much should we pay for this? And who is in charge?

These and other issues are among the obstacles to fully deploying such tools cited by nearly 600 chief information officers, tech and business directors, and other C-suite executives surveyed by KPMG LLP.

Together they represent firms in a range of industries world-wide, each with $1 billion or more in revenue—including nearly two dozen with revenue above $10 billion, according to KPMG.

Roughly 30% said their companies have allocated $50 million or more to smart automation projects, and more than half have already spent at least $10 million. The initiatives include various combinations of robotic process automation, artificial intelligence, machine learning, cognitive computing and analytics.

“Once a foundational investment is made in tools, staffing, process redesign and core infrastructure including cloud, they can be applied across a wide-ranging scope of applications and functions to achieve scale,” Cliff Justice, KPMG’s head of intelligent automation, told CIO Journal.

Scaling Automation TechnologyWhen will your adoption of intelligent automation be scaled-up and industrialized?Source: HFS Research in conjunction with KPMG International, State of intelligent automation, 2019
%Function levelEnterprise levelAlreadythereWithin nextyearWithin next2 yearsWithin next5 yearsMore than 5yearsNever/unsure051015202530354045

So far, funding is being channeled into corporate finance and accounting functions, followed by group benefits strategies and compliance, and industry-specific core operations, the survey found. Other areas included supply chain and procurement and human resources.

More than half of the officials surveyed said their firm’s key strategic goal for implementing these tools is to improve or streamline customer services and front-office effectiveness. Roughly a quarter said their goal is to drive revenue growth.

Yet most of these efforts are still in the pilot-project phase. Only 17% of surveyed officials said their firms have smart automation technologies operating at full scale. As many as 30% haven’t begun investing in smart technologies or are unsure of their plans.

Among the top three obstacles identified as holding back full deployments was a lack of resources—from storage to staffing—necessary to build up smart technologies, the survey found.

 

Efforts also suffer through “inadequate change management and governance, lack of senior management sponsorship or lack of alignment of AI goals with overall corporate objectives,” Mr. Justice said.

Similarly, the next biggest hurdles were uncertainty about the amount of spending needed to make these deployments worthwhile, followed by a lack of “organizational clarity and accountability” to drive implementation projects.

 

That is prompting many companies to take a more piecemeal approach to smart automation, the survey found.

“The more ‘moonshot’ approaches to artificial intelligence or smart technologies have been cooling off over the last two years,” said Craig Le Clair, vice president and principal analyst at Forrester Inc. for enterprise architecture and business process professionals.

He said large deployments often require data science and machine learning expertise—adding to recruiting costs—while tending to have less clear timelines or business objectives.

Instead, many firms are finding a better return on investments in limited deployments of smart-tech building blocks, such as bots that mimic and replace low-value and repetitive tasks, Mr. Le Clair said.

Because smart-tech projects typically span different corporate divisions, they can include multiple corporate leaders.

The survey found that 43% of smart technologies deployments are led by IT units, and less than one-fifth involved IT and business units working together. “This scenario makes for a less than ideal outcome if a limited number of departments actually get involved,” KPMG said.

Michael Clementi, vice president of human resources for North America at Unilever PLC, said the key to successfully deploying smart technologies is getting people from across the business to work together.

Unilever recently used an AI-enabled application to identify promising job applicants, replacing a monthslong college-recruiting process.

Rather than lead smart-tech projects, chief executives and other top company officials should identify business problems that need to be solved. Tech and business unit leaders can then get together to assess the ability of smart tools to fix those problems, he said.

 

“There’s a big conversation constantly about how we can fast-track this technology,” Mr. Clementi said this week at the WSJ Pro Artificial Intelligence Executive Forum.

Write to Angus Loten at This email address is being protected from spambots. You need JavaScript enabled to view it.

Source: Wall Street journal

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