A group of prominent venture capital investors has named Hypersonix among 29 top tech startups of the year in the “booming” big data industry, in a report published by Business Insider.

Hypersonix is a cloud platform that helps businesses in e-commerce, grocery, restaurant, hospitality and other consumer-focused industries make better, faster and more confident daily decisions. It uses AI and voice computing to turn their data into actionable business information in real time.

The recognition comes just a few weeks after Hypersonix secured $11.5M in Series A funding, led by Intel Capital.

“Data operations and data engineering have taken off,” said the report, in which more than a dozen investors weighed in with their picks for the most promising solutions in the sector.

Observed one of the participating VCs, Derek Zanutto, general partner at CapitalG (formerly Google Capital), “Companies are increasingly collecting vast swathes of data, though it’s often fragmented across the silos of their business. This makes tools for processing that data more necessary than ever.”

A spokesperson from Intel Capital told Business Insider, “Hypersonix is addressing that need with an autonomous AI platform that uses a virtual assistant to collect and process data stored in different locations and quickly turn them into predictive insights.”

“We are honored to learn that Hypersonix has been recognized among this impressive group of innovators,” said Prem Kiran, Founder and CEO. “Our solution was created from the ground up to enable data-supported decisioning. It is already delivering daily advantage to leaders at visionary companies like KFC, Smart & Final and Bashas’ Family of Stores.”

The post VCs Peg Hypersonix As A Top New Tech Firm For Big Data appeared first on Artificial Intelligence.

Thinking about the future of robots and autonomy is exciting; driverless cars, lights-out factories, urban air mobility, robotic surgeons available anywhere in the world. We’ve seen the building blocks come together in warehouses, retail stores, farms, and on the roads. It is now time to build robots for humans, not to replace them.

We still have a long way to go. Why? Because building robots that intend to work fully autonomously in a physical world is hard.

Humans are incredibly good at adapting to dynamic situations to achieve a goal. Robotic and autonomous systems are incredibly powerful at highly precise, responsive, multivariate operations. A new generation of companies is turning their attention to bringing the two together, building robots to work for humans, not replace them, and reinventing several industries in the process.

Innovation through limitation

New methods of ML, such as reinforcement learning and adversarial networks, have transformed both the speed and capability of robot systems.

These methods work extremely well when:

1. Designed for well-known tasks.
2. Within constrained environments and limited variable change.
3. Where most end states are known.

Where the probability of unforeseen situations and ‘rules’ are low, robots can work miraculously better than any human can.

An Amazon robot-powered warehouse is an excellent illustration of well-characterized tasks (goods movement), in constrained environments (warehouse), with limited diversity (structured paths), and all end states are known (limited task variability).

Robots in a complex world

What about in a less structured environment, where there are greater complexity and variability? The probability of errors and unforeseen situations is proportional to the complexity of the process.

In the physical world, what is a robot to do when it encounters a situation it has never seen before? That question conflicts with the robots’ understanding of the expected environment and has unknown end states.

The conflicted robot is precisely the challenge companies are facing when introducing robots into the physical world.

Audi claimed they would hit level 3 autonomy by 2019 (update: they recently gave up). Waymo has driven 20 million miles yet operationally and geographically constrained.

Tesla reverted from a fully robotic factory approach back to a human-machine mix, the company stating, “Automation simply can’t deal with the complexity, inconsistencies, variation and ‘things gone wrong’ that humans can.”

Yes — this complex issue will be figured out — but the situation is not solved yet.

To solve these problems in the physical world, we’ve implemented humans as technology guardrails.

Applications such as driverless cars, last-mile delivery robots, warehouse robots, robots making pizza, cleaning floors, and more, can operate in the real world thanks to ‘humans in the loop’ monitoring their operations.

Humans are acting as either remote operators, AI data trainers, and exception managers.

Human-in-the-Loop robotics

The ‘human in the loop’ has accelerated the pace of technology and opened up capabilities we didn’t think we would see in our lifetime, as the examples mentioned earlier.

At the same time, it has bounded the use cases to which we build. When we design robotic systems around commodity skill sets, the range of tasks is limited to those just those skills.

Training and operating a driverless car, delivery robot, or warehouse robot all require the same generally held skill sets.

As a result, what robots are capable of today primarily cluster around the ability to navigate and identify people/objects.

As these companies bring their solutions to market, they quickly realize two realities:

(1) Commodity tasks make it easier for others to also attempt a similar solution (as seen with the number of AV and warehouse robot companies emerging over the past few years).

(2) High labor liquidity depresses wages, thus requiring these solutions to fully replace the human, not augment, in high volumes to generate any meaningful economics. E.g., Waymo/Uber/Zoox needs to remove the driver and operate at high volumes to turn a profit eventually.

The result of the commodity approach to robotics has forced these technology developers to completely replace the human from the loop to become viable businesses.

Changing the intersection of robotics and humans

The open question is: is this the right intersection between machine and human? Is this the best we can do to leverage the precision of a robot with the creativity of a human?

Expert-in-the-Loop robotics

To accelerate what robots are capable of doing, we need to shift focus from trying to replace humans, to building solutions that put the robot and human hand-in-hand. For robots to find their way into critical workflows of our industries, we needed them to augment experts and trained technicians.

Industries such as general aviation, construction, manufacturing, retail, farming, and healthcare could be made safer, more efficient, and more profitable. Changing the human’s role of operator and technician to manager and strategist.

Helicopter pilots could free themselves from the fatiguing balance of flight and control management. Construction machine operators could focus on strategies and exceptions rather than repetitive motions.

Manufacturing facilities could free up workers to focus on throughput, workflow, and quality, rather than tiring manual labor. Retail operators could focus on customer experiences rather than trying to keep up with stocking inventory.

These industries all suffer from limited labor pools, highly variable environments, with little technology, and high cost of errors. Pairing robotic or autonomous systems that work hand in hand with the experts could invert from the set of dynamics compared to commodity use cases.

Companies could build solutions that need only to augment the operator, not replace him or her, to meaningfully change the economics of the operation.

Building for an expert-robot generation

The current generation of technology innovation is starting, with a new generation of companies using robotics and autonomy to change the operating experience across industries.

• Innovative companies such as Skyryse* with complex aircraft flight controls.
• Built Robotics in the construction.
• Path Robotics in manufacturing.
• Caterpillar in mining.
• Blue River in agriculture.
• Saildrone in ocean exploration.
• Simbe Robotics* in retail.
• Intuitive Surgical in healthcare.

Robot solutions that share many key dimensions:

• Introduce advanced levels of automation or autonomy that can pair with its human operator.
• Deliver at least two of the three value dimensions: safer operation, improved cost of operation, high total utilization of assets.
• Shift the operators’ time to higher-value tasks; eventually to manage across multiple functions in parallel.
• Primarily software-defined across both control and perception systems.
• Easily retrofit into customers’ assets base at price points less than 20% of the cost of the underlying asset.
• Can go to market ‘as a service’ with recurring revenue and healthy margins.

Technology has empowered humankind to be capable of the impossible.

The impossible means we can make more complex decisions at orders of magnitude more precision and speed. Yet so many industries still rely on human labor and operations over human ingenuity and authority.

As the world adapts to social distancing and remote work, it’s more important than ever to leverage technology as our proverbial exoskeletons to maximize what humans are great at, and let technology do the rest.

The post Time to Build Robots for Humans, Not to Replace appeared first on Artificial Intelligence.

The worlds of artificial intelligence and cybersecurity have become deeply entwined in recent years, as organizations work to keep up with — and ideally block — increasingly sophisticated malicious hackers. Today, a startup that’s built a deep learning solution that it claims can both identify and stop even viruses that have yet to be identified has raised a large round of funding from some big strategic partners.

Deep Instinct, which uses deep learning both to learn how to identify and stop known viruses and other hacking techniques, as well as to be able to identify completely new approaches that have not been identified before, has raised $43 million in a Series C.

The funding is being led by Millennium New Horizons, with Unbound (a London-based investment firm founded by Shravin Mittal), LG and Nvidia all participating. The investment brings the total raised by Deep Instinct  to $100 million, with HP and Samsung among its previous backers. The tech companies are all strategics, in that (as in the case of HP) they bundle and resell Deep Instinct’s solutions, or use them directly in their own services.

The Israeli-based company is not disclosing valuation, but notably, it is already profitable.

Targeting as-yet unknown viruses is becoming a more important priority as cybercrime grows. CEO and founder Guy Caspi notes that currently there are more than 350,000 new machine-generated malware created every day “with increasingly sophisticated evasion techniques, such as zero-days and APTs (Advanced Persistent Threats).” Nearly two-thirds of enterprises have been compromised in the past year by new and unknown malware attacks originating at endpoints, representing a 20% increase from the previous year, he added. And zero-day attacks are now four times more likely to compromise organizations. “Most cyber solutions on the market can’t protect against these new types of attacks and have therefore shifted to a detect-response approach,” he said, “which by design means that they ‘assume a breach’ will happen.”

While there is already a large profusion of AI-based cybersecurity tools on the market today, Caspi notes that Deep Instinct takes a critically different approach because of its use of deep neural network algorithms, which essentially are set up to mimic how a human brain thinks.

“Deep Instinct is the first and currently the only company to apply end-to-end deep learning to cybersecurity,” he said in an interview. In his view, this provides a more advanced form of threat protection than the common traditional machine learning solutions available in the market, which rely on feature extractions determined by humans, which means they are limited by the knowledge and experience of the security expert, and can only analyze a very small part of the available data (less than 2%, he says). “Therefore, traditional machine learning-based solutions and other forms of AI have low detection rates of new, unseen malware and generate high false-positive rates.” There’s been a growing body of research that supports this idea, although we’ve not seen many deep learning cybersecurity solutions emerge as a result (not yet, anyway).

He adds that deep learning is the only AI-based autonomous system that can “learn from any raw data, as it’s not limited by an expert’s technological knowledge.” In other words, it’s not based just on what a human inputs into the algorithm, but is based on huge swathes of big data, sourced from servers, mobile devices and other endpoints, that are input in and automatically read by the system.

This also means that the system can be used in turn across a number of different end points. Many machine learning-based cybersecurity solutions, he notes, are geared at Windows environments. That is somewhat logical, given that Windows and Android account for the vast majority of attacks these days, but cross-OS attacks are now on the rise.

While Deep Instinct specializes in preventing first-seen, unknown cyberattacks like APTs and zero-day attacks, Caspi notes that in the past year there has been a rise in both the amount and the impact of cyberattacks covering other areas. In 2019, Deep Instinct saw an increase in spyware and ransomware on top of an increase in the level of sophistication of the attacks that are being used, specifically with more file-less attacks using scripts and powershell, “living off the land” attacks and the use of weaponized documents like Microsoft Office files and PDFs. These sit alongside big malware attacks like Emotet, Trickbot, New ServeHelper and Legion Loader.

Today the company sells services both directly and via partners (like HP), and it’s mainly focused on enterprise users. But since there is very little in the way of technical implementation (“Our solution is mostly autonomous and all processes are automated [and] deep learning brain is handling most of the security,” Caspi said), the longer-term plan is to build a version of the product that consumers could adopt, too.

With a large part of antivirus software often proving futile in protecting users against attacks these days, that could come as a welcome addition to the market, despite how crowded it already is.

“There is no shortage of cybersecurity software providers, yet no company aside from Deep Instinct has figured out how to apply deep learning to automate malware analysis,” said Ray Cheng, partner at Millennium New Horizons, in a statement. “What excites us most about Deep Instinct is its proven ability to use its proprietary neural network to effectively detect viruses and malware no other software can catch. That genuine protection in an age of escalating threats, without the need of exorbitantly expensive or complicated systems is a paradigm change.”

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Pecan.ai, a startup that wants to help business analysts build machine learning models in an automated fashion, emerged from stealth today and announced an $11 million Series A.

The round was led by Dell Technologies Capital and S Capital. Along with a previously unannounced $4 million seed round, the company has raised a total of $15 million.

CEO Zohar Bronfman says he and co-founder Noam Brezis, whom he has known for more than a decade, started the company with the goal of building an automated machine learning platform. They observed that much of the work involved in building machine learning models is about getting data in a form that the algorithm can consume, something they’ve automated in Pecan.

“The innovative thing about Pecan is that we do all of the data preparation and data, engineering and data processing, and [complete the] various technical steps [for you],” Bronfman explained.

The target user is a business analyst using business intelligence and analytics tools, who wants to bring the power of machine learning to their data analysis, but lacks the skills to do it. “The business analyst knows the data very well, knows the business problem very well and speaks directly to the business owner of the problem — and they are currently conducting basic analytics,” he said.

Pecan includes a series of templates designed to answer common business questions. They divide these into two main categories. The first is customer questions like how much churn do we have, and the second is business operations questions related to things like risk or fraud. If the question doesn’t fall into one of these categories, it is possible to build your own template, but Bronfman says that is really for more advanced users.

After you select the template and point to a data source such as a database, data lake or CRM repository, Pecan does the work of connecting to the source and pulling data into a dashboard. You can also export the algorithm for use in an external service or application, or Pecan can automatically update a data repository with data the algorithm is measuring, such as churn rate.

The founders have been building this platform since 2016, when they founded the company, and have been working with beta customers for the last 18 months or so. Today, they emerge from stealth and bring Pecan to market in earnest.

Bronfman plans to move to New York City and open a sales and marketing office in the U.S., while Brezis will remain in Tel Aviv and oversee engineering. It’s early days for this startup, but with $11 million in capital, it has a chance to take the product to market and see what happens.

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Gecko Robotics has landed $40 million in financing as it looks to build an additional 40 robots over the next year to meet what the company sees as growing demand for its safety and infrastructure monitoring services.

“We are growing fast solving critical infrastructure problems that affect our lives, and can even save lives,” says Jake Loosararian, Gecko Robotics’ 28-year-old co-founder and chief executive officer, in a statement. “At our core, we are a robot-enabled software company that helps stop life-threatening catastrophes. We’ve developed a revolutionary way to use robots as an enabler to capture data for predictability of infrastructure; reducing failure, explosions, emissions and billions of dollars of loss each year.”

In the three years since its launch in 2016, Gecko Robotics  has managed to grow from a small team of Pittsburgh robotics experts hailing from Carnegie Mellon. Indeed, the company has added more than 100 new employees. The hiring push has been largely around creating a team of qualified experts in particular market segments who can operate the robots that Gecko deploys to industrial work sites.

There’s been something of a robotics revolution in the safety and compliance market over the past few years. From automated assembly lines to warehouses and now to chemical plants and refineries, robots are making their presence felt.

And Gecko isn’t the only company that’s trying to tackle the market. Other companies like Invert Robotics, a Christchurch, New Zealand-based company, has built its own competitive robotic safety inspector.

The initial pitch from Gecko managed to attract angel investors like Mark Cuban, Deep Nishar (a managing partner at SoftBank),  Josh Reeves and Jake Seid, the managing director at Stone Bridge Ventures.

Now the company adds the Midwestern venture capital juggernaut Drive Capital  to its stable of investors.

“We are very excited for the future of robotics in industrial inspection. The Gecko Robotics team are revolutionizing an industry that is in need of a real upgrade and will save lives,” said Mark Kvamme, lead investor and partner at Drive Capital. “I see amazing potential for Gecko’s business model, they are on the path to become a market leader in their industry.”

Gecko Robotics has already opened a 20,000-square-foot office in Houston, and has offices in Houston, Austin and Pittsburgh.

“The robots are amazing, but they’re not going to be able to complete the job done by these experts who have experience of 30 to 40 years,” says Loosararian. “We have thought leaders who go out in the field… they take the robots out and they use their own manual ability and knowledge to provide the expertise to the clients.”

Gecko currently has 60 robots in its stable of robots and will add at least another 40 over the course of the year. “The product at the end is the software license that they pay for annually,” Loosararian says.

The post Building robotic safety inspectors nabs Gecko Robotics $40 million appeared first on Artificial Intelligence.

Machine learning is a complex process. You build a model, test it in laboratory conditions, then put it out in the world. After that, how do you monitor how well it’s tracking what you designed it to do? Arthur wants to help, and today it emerged from stealth with a new platform to help you monitor machine learning models in production.

The company also announced it had closed a $3.3 million seed round, which closed in August.

Arthur CEO and co-founder Adam Wenchel says that Arthur is analogous to a performance-monitoring platform like New Relic or DataDog, but instead of monitoring your systems, it’s tracking the performance of your machine learning models.

“We are an AI monitoring and explainability company, which means when you put your models in production, we let you monitor them to know that they’re not going off the rails, that you can explain what they’re doing, that they’re not performing badly and are not being totally biassed — all of the ways models can go wrong,” Wenchel explained.

Data scientists build machine learning models and test them in the lab, but as Wenchel says, when that model leaves the controlled environment of the lab, lots can go wrong, and it’s hard to keep track of that. “Models always perform well in the lab, but then you put them out in the real world and there is often a drop-off in performance — in fact, almost always. So being able to measure and monitor that is a capability people really need,” he said.

Interestingly enough, AWS announced a new model-monitoring tool last week as part of SageMaker Studio. IBM also announced a similar tool for models built on the Watson platform earlier this year, but Wenchel says the involvement of the big guys could work to his company’s advantage as his product is platform-agnostic. “Having a neutral third party for your monitoring that works equally well across stacks is going to be pretty valuable,” he said.

As for the funding, it was co-led by Work-Bench  and Index Ventures, with participation from Hunter Walk at Homebrew, Jerry Yang at AME Ventures and others.

Jonathan Lehr, a general partner at Work-Bench, sees a company with a lot of potential. “We regularly speak with ML executives from Fortune 1000 companies and one of their biggest concerns as they become more data-driven is model behavior in production. The Arthur platform is by far the best solution we’ve seen for AI monitoring and transparency…” he said.

The company, which is based in New York City, currently has 10 people. It launched in 2018, and has been heads-down working on the product since. Today marks the release of the product publicly.

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In the early 1980s, presentations about Infosys began with the founders’ pointing out India and Bengaluru on a world map. Today, globally listed companies such as Dr Reddy’s, Tata Motors, and Reliance Industries have made that redundant.

The country is also the third-largest startup nation. A number of its business-to-consumer (B2C) ventures, from e-commerce major Flipkart to ride-sharing platform Ola, are known across the world.

Now, a new wave of business-to-business (B2B) startups in niche segments is silently creating a significant impact globally. Rising from the streets of nondescript suburbs, they are breaking the glass ceiling, global oil & gas majors and large manufacturers being their customers. These firms are solely responsible for India’s breakthrough in the industrial artificial intelligence (AI) space.

A simple case: A large oil & gas conglomerate looking to predict the safety of its drilling machines spread over an area of thousands of kilometres can hire an Indian AI startup for the task.

There are many such collaborations sprouting.

That leads us to a question: Is there a playbook emerging for Indian startups to go global? Let us find out.

Indian success ingredient

Like those in Stuttgart, Germany, industrial clusters in several Indian cities like Bengaluru, Chennai, Pune, and Coimbatore have, over the years, spawned many micro industries. This is one of the reasons startups have ventured into industrial AI in India.

“The kind of innovation India’s industrial AI startups have is unparalleled across the globe. Surprisingly, such innovation is not stemming out of the Bay area,” said Derick Jose, co-founder of Flutura, a global leader in applying industrial AI for energy and engineering applications. “Our strength is the cluster-oriented talent we have been able to hone. For example, Bengaluru has defence as a vertical, Pune has auto and manufacturing, and Chennai has auto and original equipment manufacturers (OEMs). They are also thriving ecosystems where engineering prowess and industry intersect.”

Bengaluru, even before the advent of IT, was a defence and aeronautics cluster. There was a very strong engineering base in the southern Indian city. Then slowly, we saw the evolution of an intersection of analytics and engineering. A great example of this is Sunlux Technologies, which manufactures sensors for the Indian Navy.

“In every hi-tech hub of developed innovation economies, the rise of the modern-day tech entrepreneurship ecosystem (startups and venture capital) happened under the larger canopy of an advanced industrial innovation ecosystem,” said Vishwanathan Sahasranamam, co-founder and CEO of Forge Accelerator. “This ecosystem powered and pioneered the development and commercialisation of advanced technologies in creating globally competitive solutions for the most challenging industrial sectors. The greatest contribution of this being the creation of highly competent technologists, who, with the vision to exploit the power of these technologies and the entrepreneurial ambitions, created the next generation of multibillion-dollar ventures.”

Additionally, Indian startups have low operating expenses and capital expenditure, and the solution is generally affordably priced, which is also a major advantage.

Breaking through the global opportunity

A 2016 report by McKinsey states that, while “feeling” prepared, only 30% of the technology suppliers and 16% of the manufacturers have an overall strategy for Industry 4.0 (the trend towards automation and data exchange in manufacturing technologies) in place, and only 24% have assigned clear responsibilities for that.

Large global companies invest several millions of dollars on R&D and many prefer hiring internal teams to work on sensitive challenges. “If someone in your family is ill, will you buy a heart valve from a startup or a big company? That got us thinking. They are not buying AI, they are buying peace of mind,” said Derick Jose of Flutura. “We are touching some of their core processes, as a lot of sensitive data is concerned. Therefore, they are hesitant to move us to the core. Our strategy was to go to customers who have experienced internal failure.”

Global companies, especially those operating within specific industries, are tightly knit communities. One way to win their trust is to begin speaking their language and winning smaller projects. Once you have their trust, they refer you to several others in their circles.

“India is known for its software prowess and we have the talent and capabilities in machine learning, computer vision, and deep learning,” said Tarun Mishra, co-founder of DeTech Technologies. “The next level is building the AI layer on top and India is front-ending this revolution. In many ways, we are the OEMs of this data and it is only organic for us to expand to the field of industrial AI.” DeTech Technologies is a Chennai-based startup incubated at the Indian Institute of Technology Madras in 2016. It works with leading global oil and gas companies.

The playbook for early sales

Technology and innovation have been and will remain central to how production evolves. Despite the recent slowdown in global economic growth, production continues to be a critical driver of the economy in the developed and developing countries, according to a World Economic Forum whitepaper.

What would help Indian startups in this space is larger government grants, akin to what the US, China, and Israel provide. Indian talent is also slowly warming up to this space, thus, engineering colleges must modify their curriculum to keep pace.

At a micro level, according to some of the founders of these startups, what has worked for them is:

• Aniruddha Bannerjee, founder, SwitchOn: “…show tangible return on investment. From day one, we work with customers who’re looking at numbers. Working with a demanding customer makes it easier to work with others.” SwitchOn builds AI solutions for auto and FMCG firms.
• Saad Nasser, founder of Ati Motors: “Automation is all about increasing efficiency and productivity; and will change the way we work. With automation you get reconfigurability and we see an openness towards that. Hence, Sherpa, our industrial electric vehicle, is seeing more acceptance.” Bengaluru-based Ati Motors makes an all-electric autonomous cargo vehicle, Sherpa.
• Harsimrat Bhasin, co-founder of Neewee AI: “Bodhee’s value proposition of delivering analytics outcomes on the shop-floor and its holistic approach of integrating silos in the manufacturing value chain has helped us gain faster traction.” Bodhee is the company’s industrial analytics product with an AI solution that predicts industrial outcomes.

These ventures are not only working on cutting edge technologies that will be crucial for several industries in future, they’re also doing a lot for India. “The startup story of India has largely been domestic till now. Thanks to the kind of global companies being built, industrial AI is emerging as a space where India can establish a leadership position in the global AI ecosystem,” said Ganapathy Venugopal, co-founder and CEO of Axilor Ventures, an early-stage seed fund and startup accelerator.

The post Industrial AI is helping the Indian startup industry build a name beyond e-commerce By Nisha Ramchandani appeared first on Artificial Intelligence.

To organise employees and manage the work across borders is a crucial task done by multinational companies. As we see, technology is updating every day which leading the companies to adopt such technologies and organise the work culture as much as possible.

Artificial Intelligence is already forcing leadership teams of the companies around the world to reconsider some of their core structure. Advances in technology are causing firms to restructure their organizational makeup, transform their HR departments, develop new training models, and re-evaluate their hiring practices.

So here are some startups that helping brands to organise their work structure:

Routematic

One of the most innovative technology enabled transportation brand in India, Routematic is aimed at providing a simplified, robust and cost-effective transportation system for corporate employees. The company offers a combination of 2 services – Routematic Fleet Service and Transport Automation Software. As a SaaS product, it offers automation software complimentary to its fleet, which helps clients plan, optimize and monitor their employee transport operations.

Aurelius

We have been instrumental in developing consultative in-sourcing solutions which can enable organizations to streamline their operational procedures and business models. The company develops and customizes training programs focusing on the application side of technology. These programs are delivered Real-Time and Virtual with cloud-hosted labs based on the diverse requirements of companies.

Roadcast

A GPS-based real-time asset tracking, management and monitoring platform that allows businesses, which deal in logistics, transport and home delivery services, to track shipments/vehicles in real-time and tabulates data such as distance, time and routes. The platform helps in Live Location Tracking, Task Management, Attendance Management, Extensive Reporting, Customizable platform, and is suitable for any type of business.

Intuition

This Bengaluru-based startup which develops point-of-sale (POS) and billing systems using AI and ML, has collaborated with Lantern Pharma, a biopharmaceutical company which uses precision oncology to treat cancer and its related diseases. With cancer being harder to detect and treat at initial stages, Lantern aims to alleviate this problem using its advanced genomics and AI for improved drug development. Intuition Systems will work with Lantern’s team to help with AI, big data, cloud services and infrastructure to support drug development and biomarker identification.

Customer Success Box

A B2B SaaS customer success platform backed with $1 million in venture funding, Customer Success Box is aimed at delivering proactive customer success. As the startup believes, customer churn is the biggest blocker of growth and it’s like trying to fill up a leaky bucket. Such businesses cannot continue to operate with the old reactive support model, this is where CSB comes into the picture.

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Why are Indian startups looking for talent abroad? Well, the answer to this is the dearth of homegrown professionals on the ground with knowledge of UI/UX (user interface/user experience), artificial intelligence, machine learning, reports The Economic Times.

Industry experts are of the opinion that roles in data science and data engineering are not typically part of the curriculum of Indian colleges.

The ratio of the number of people to jobs in deep learning is 0.53, while for machine learning it’s 0.63 and for NLP it’s 0.71. Only 4 per cent of AI (Artificial Intelligence) professionals in India have worked on cutting-edge technologies such as deep learning and neural networks, the key ingredients in building advanced AI-related solutions, said Rishabh Kaul, co-founder of recruitment startup Belong.

Another reason for Indian startups to hunt for talent from overseas is to increase their global presence. Hence, these companies are either hiring foreign professionals or those Indians who wish to come back to India.

For the past two years, there have been a lot of global uncertainties, especially in the US. A lot of people are thinking of working from India and not the US. “The funds are normally coming from global companies and the startups are here, so it is best of both worlds as there’s a reverse brain drain,” said Rajeev Banduni, chief executive of advisory services firm GrowthEnabler, who has a lot of requests from European students, particularly the UK and Spain, for internships. He said 80-85 per cent talent is coming back to India.

UpGrad, started by UTV co-founder Ronnie Screwvala, is among those looking to expand presence in global markets. It has a team of three Chinese Singaporeans focused on analysing the Southeast Asian market and deciding upon its market-entry strategy. The firm is also talking to some Chinese education professionals to join it to help bring learning from Chinese markets.

As many as 22 members in Nykaa’s 80-people business and marketing team are those who have come back to India from the US and Europe.

But why are Indian startups more interested in hiring Indian professionals? According to these firms, the availability of such global talent is tougher and their salaries are higher. A similar talent in the US/western world coming to India would be 30-50 per cent costlier than what’s available in India.

These companies also believe that a change in the talent pool can be brought through government initiatives just like China where they have a three-year plan to dominate in AI.

A breather for the startup ecosystem hunting for top talents is that that in his budget speech in February, finance minister Arun Jaitley had announced that the government will launch a national programme on AI and doubled the budget allocation for Digital India — the government’s main initiative for promoting AI, machine learning and other innovations — to Rs 3,073 crore for this year.

The post Startups hiring foreign professionals due to dearth of Artificial Intelligence talent in India appeared first on Artificial Intelligence.

There’s a misconception that to leverage machine learning you need to be a mathematical genius. In reality, most machine-learning applications use well-understood, well-tested, off-the-shelf algorithms.

For many developers, especially those at startups, the real challenge lies in training the data. Overcoming this challenge takes clever product development with an eye on user experience.

Do you really need machine learning?

Machine learning can make a good product even better: more engaging, more responsive, and more effective. But, before tackling machine learning, ask yourself whether algorithms are right for your product.

Start testing the learning aspect with humans before jumping into machine learning. This will give you a better sense for whether the result is actually useful. Testing the machine learning will also give you an idea of when a human should be involved and when the machine learning should take over.

Often, a product’s sweet spot lands somewhere in between human-run and machine learning-automated. Either the human helps the computer when the algorithm is out of its depth or the computer helps the human to scale. For example, Clara Labs has differentiated its scheduling assistant by knowing which tasks are good for algorithms and when a real person needs to step in. This hybrid approach has helped Clara Labs separate itself from AI-only virtual assistants, which lose trust when the AI falters.

Once you have identified that your product would benefit from machine learning and know how much machine learning is right, then comes the challenge of labeling that data.

Labeling the data

Without high-quality, labeled training data, machine-learning accuracy gets constrained. Labels ensure that models can predict, classify or analyze data with accuracy.

Manually labeling data is a thankless, relatively low-level job. The best machine-learning products find ways to integrate labeling into the application’s overall experience.

Trading value for labels

For the sheer number of labels needed to train algorithms, manual labeling is often too time-consuming. Instead, well-designed, thoughtful applications often leverage users to do much of the labeling. The goal is to take the task that humans are good at, transfer the knowledge to the applications and have the applications take over.

For instance, reCAPTCHA is a free service from Google that helps protect websites from spam and abuse. The user has to identify images to prove that they aren’t a bot. At the same time, reCAPTCHA is training algorithms to recognize real-world objects. The images themselves are the training data and as users identify objects, the data gets the labels it needs.

There’s a cautionary tale here. Labeling cannot be a means to a distant far-off end. If the task you’re using to train the data doesn’t have value or the user won’t see the value for a long time, users won’t take part. Even reCAPTCHA, with its clear benefit to security and quality, wears on the nerves of internet goers — an issue Google has been grappling with.

If users are going to label your data, the labeling must be clearly and immediately valuable. Generally speaking, there are two types of value. The first is making the action valuable in its own right. For example, we’re willing to tag Facebook photos because it lets our friends and family know that they’re in the picture. With the labels, Facebook begins to recognize faces, making it easier to find people in pictures in the future. Although it may take some time before Facebook’s algorithm recognizes your best friend’s face, the act of labeling has value in and of itself.

The second value comes when the labeling has an immediate impact. Netflix asks users to rank movies with the promise that it will help improve movie recommendations. To make the value clear, Netflix immediately responds with new recommendations based on the rating you just gave.

Another tactic is to make labeling a game. Foursquare was successful at getting users to provide location data by incentivizing location check-ins. Dedicated users provided valuable labels about locations while competing for “badges” and “mayorship.”

While Foursquare no longer needs to use check-ins thanks to passive location tracking, the competitive check-in aspect lives on in Foursquare Swarm, and early on all those check-ins provided FourSquare with information that added greater context to location.

While tying the labeling process to clear value is an effective way to enlist users to train data, there are also tactics that don’t require active user involvement.

Derive from behavior

One way around enlisting users to actively label data is to observe their behavior. The benefit of deriving labels from behavior is that the user doesn’t need to actively participate in the labeling process. This eliminates a lot of the pitfalls that can harm user experience.

For example, Amazon observes your buying behaviors to recommend products and deals. At my company, we took a similar route. We monitor data usage, like which reports get used most and which SQL queries are being written, to help analysts find the right data set for the task at hand.

Learning without teachers

In the near future, users may not be as important to training data. Simulations provide a contained environment and a perfect way to label data. Chess, Go, and Pong are all games that can be easily simulated, allowing thousands or even hundreds of thousands of scenarios to run. Google’s Alpha Zero was able to teach itself chess and beat the leading chess programs, going on to master two other games while only playing itself.

While board games are closed environments, simulation is also helping to train devices intended to function in the real world. Autonomous vehicle developer Waymo is using simulations to train self-driving cars. The company is using virtual environments based on real-world locations to train vehicles for real-world driving. While very new, simulation offers the potential to create labels without human intervention.

User experience is paramount

Machine learning can help make more compelling, responsive products. Users aren’t going to provide their data or patiently train your algorithms if the value isn’t there and the experience isn’t compelling. Whether the user is directly labeling your data, indirectly labeling your data or not involved at all — user experience is paramount.

For startups, this demands another layer of design thinking. Not only does the product itself need to be great, but if users are contributing to the machine learning, the data collection and training process must be just as compelling. But, it is exactly these kinds of hurdles that drive creativity.

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