Robots Take Over the World




Source: ABI/Massachusetts Technology Collaborative



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The New Servant Class: 50 Startups Developing Home Robots


CB Insights:

Consumer robotics startups have bagged 25% of the global robotics deal share in the last 5 years. Around 40% of these consumer deals went to social robots like Anki, UBTECH, and Rokid. Educational robots that teach children how to code raised 7 deals each in 2014 and 2015, with deals projected to surpass that number this year at the current run rate. Over 10 deals went to service robots last year, from fewer than 5 in 2014.

  • Social: Social robots, including companion and entertainment robots for homes, have received the greatest share of consumer robotics deals. Humanoid robotics startup UBTECH joined the unicorn club this year after raising a $100M Series B round from CDH Investments, Goldstone Investment, and CITIC Securities International. China-based Turing Robot, which develops robotics operating systems, announced the launch of a companion robot for kids at the price of a smartphone. London-based Olly raised $10M last quarter from Alliance Capital Ventures and Lightning Capital. Another startup, Anki, raised $52.5M in Q2’16 from Index Ventures, Two Sigma Ventures, JPMorgan Chase & Co., and Andreessen Horowitz.
  • Personal drones: Intel Capital invested $60M in China-based Yuneec. You can read more about Intel, Google, Foxconn, and other corporations investing in private robotics startups here. Other personal drone startups, most of them in their early stages of funding, include xCraftUVifyOpenROV, and EHANG. Another company, 3D Robotics, which recently announced that it has exited hardware, has been excluded from the map. DJI Innovations, which also develops industrial robots, is included in this market map as it also manufactures consumer drones Mavec and Phanthom.
  • Educational: Robots that teach children to code have piqued the interest of investors in recent years. The most well-funded startup in this category is Wonder Workshop, which has raised $36M in equity funding from investors including CRV, Madrona Venture Group, and Google Ventures. Another startup, Modular Robotics, is backed by Foundry Group. Sequoia Capital China backed China-based Makeblock in a $6M Series A round in 2014.
  • Service Robots: This includes robotic arms, personal transportation robots, as well as those that perform household chores like cooking, vacuuming, bartending, and even cleaning your fishtank. So far this year, 5 companies have raised equity funds, including personal transportation robot Ninebot and desktop robotic arm Dobot.
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Internet of Things: To Fog or to Cloud?


BI Intelligence:

Cloud computing, usually just called “the cloud,” involves delivering data, applications, photos, videos, and more over the Internet to data centers. The Internet of Things, meanwhile, is the term for the connection of devices (other than the standard ones such as computers and smartphones) to the Internet. Automobiles, kitchen appliances, and even heart monitors could all be connected through the IoT. And as the Internet of Things explodes in the next few years, more types of devices will join that list.

Cloud computing and the IoT both serve to increase efficiency in our everyday tasks, and the two have a complimentary relationship. The IoT generates massive amounts of data, and cloud computing provides a pathway for that data to travel to its destination.

Some of the more popular IoT cloud platforms on the market include Amazon Web Services, GE Predix, Google Cloud IoT, Microsoft Azure IoT Suite, IBM Watson, and Salesforce IoT Cloud.

Fog computing is more than just a clever name. Also known as edge computing, it provides a way to gather and process data at local computing devices instead of in the cloud or at a remote data center. Under this model, sensors and other connected devices send data to a nearby edge computing device. This could be a gateway device, such as a switch or router, that processors and analyzes this data.

IoT Big Data & Analytics

Big data is exactly what it sounds like: it’s a lot of data. The Internet of Things is allowing us to generate more data than ever before, and the eye-popping numbers are still climbing. The “Internet of Everything,” which consists of all people and things connected to the Internet, will generate 507.5 zettabytes of data by 2019, according to Cisco. For context, one zettabyte = one trillion gigabytes.

BI Intelligence believes that fog computing will be instrumental in analyzing all of this data, as it offers several advantages that a cloud computing model simply does not have. These include quicker data analysis, reduced costs tied to data transmission, storage, and management, as well as enhanced network and application reliability.


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Asimo The Humanoid Robot in Action


In 2000…

in 2016…

And on Saturday Night Live…

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Visually Linking AI, Machine Learning, Deep Learning, Big Data and Data Science



Source: Battle of the Data Science Venn Diagrams

HT: KDnuggets


What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning?

Over the past few years AI has exploded, and especially since 2015. Much of that has to do with the wide availability of GPUs that make parallel processing ever faster, cheaper, and more powerful. It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe (that whole Big Data movement) – images, text, transactions, mapping data, you name it.


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Maana Deploys AI to Optimize Enterprise Knowledge at Maersk


Maana Knowledge Platform for Oil and Gas

Does your company suffer from corporate amnesia? Palo Alto, California-based startup Maana has developed a cure for what ails organizations everywhere: Knowledge of how to perform a certain task or make a specific decision walks out the door with employees migrating to another job or retiring. Even when this tacit knowledge is captured, codified and stored in a database, it may not be accessible to the people who need it, when they need it. “We patented a unique and novel way of indexing and organizing the knowledge that is locked in data silos across the organization,” says founder and CEO Babur Ozden. Today, Maana released a new version of its AI-driven platform.

Failing organizational memory is particularly harmful when there is a “decision deadline,” explains Ozden: “These are decisions that need to take place along the workflow of an operation and need to be taken in a few hours or a few minutes.” Maana’s knowledge graph, which captures complex relations between actions, processes, and assets, coupled with advanced AI algorithms, semantic search, and deep learning, helps employees make faster and more relevant data-driven decisions by providing them with the relevant pieces of organizational memory at the moment they need it most.

Maana’s technology “captures the knowledge people acquire on the job and enables other employees, who do not have a similar experience, to have a head start in making a decision instead of starting from zero,” says Ibrahim Gokcen, Head of Data Science & Analytics at Maersk. The Maersk Group is a worldwide conglomerate that operates in 130 countries with a workforce of over 89,000 employees. Headquartered in Copenhagen, Denmark, with 2015 revenues of $40.3 billion, it owns Maersk Line, the world’s largest container shipping company, and is involved in a wide range of activities in the shipping, logistics, and the oil and gas industries.

“We want to make AI part of our digital journey,” says Gocken. “Strong technology platforms with AI capabilities help the data science and analytics people focus on the business logic, on the algorithms, and on churning models very quickly. These platforms give a head start not just to employees making decisions but also to our data scientists.”

Adds Donald Thompson, Maana’s founder and president: “We capture in a pragmatic way the knowledge of subject matter experts and business users and make it explicit so more people can take advantage of it.”

In a statement, Thompson said that the new version of Maana’s platform is “introducing our first collection of Knowledge Assistants and Knowledge Applications that really bring out the value of our user-guided and machine-assisted approach. People at all levels are empowered to rapidly gain the understanding they need in order to make the best decisions, while generating new knowledge assets (models) that others can use or build upon.”
Originally published on
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40 Startups in the IoT in Retail Market Landscape


CB Insights:

Beacon- and sensor-based analytics – These companies provide hardware and software to help stores track visitors. They focus on data collection for internal analytics, such as merchandise tracking, adjusting staffing levels, monitoring promotions, etc. Euclid Analytics, for example, promises visitor tracking to monitor the impact of promotions on driving store visits and better understand stores’ busy times and aisles.

Beacon-based marketing – These companies also track visitors, but focus more on proximity marketing use cases (which may also include some analytic insights). Estimote provides small, colorful beacons that send push notifications to users’ phones about products or promotions when it senses someone near. Kimetric’s sensors aim to visually identify shoppers’ age, gender, eye focus, and clothing style to present them with personalized marketing.

Beacon analytics and marketing – These startups track visitors and provide a mix of internal analytics and proximity marketing services.

Inventory tracking – QueueHop provides connected anti-theft tags for items that automatically unclip after payment. Cosy is building inventory-tracking robots that use indoor mapping software.

Indoor mapping – These startups take advantage of connected devices to create detailed indoor maps of stores and malls. Stores can help users find the right items and direct them to promotions.

Service robots – Simbe Robotics and Fellow Robots are designing robots for use in-store, to help customers find items and ensure the shelves stay stocked. Fellow Robots worked with Lowe’s to launch the LoweBot in eleven stores this fall.

Loss prevention – Gatekeeper uses RFID tags that work with wheel-locking features to automatically stop shopping carts that leave the store area, helping prevent theft. Carttronicstracks baskets and carts with RFID tags, provides cameras that receive signals from the tags and can film anyone leaving the store with unauthorized items.

At-home shopping buttons – Kwik and Hiku offer connected devices that can automatically place online orders for goods from users’ homes, comparable to the Amazon Dash buttons. Kwik will provide branded buttons that let customers re-order items with one touch, while the Hiku device can also scan barcodes, to identify items for re-ordering, and can recognize users’ voices.

Smart dressing rooms – Oak Labs created an interactive, touchscreen mirror that lets shoppers request new items, adjust fitting room lighting, and see outfit recommendations. The mirror can sense which products the shopper brought into the room using RFID technology, and then present related products, save the items to shoppers’ online accounts, or display related items. Oak has worked with Polo Ralph Lauren.


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