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Fireside Chats

Are VCs Worthless? Why Startups Need To Go After ‘Hard Things’

Palihapitiya dove deep on weaknesses in the current VC model and took them to task for not having skin in the game and being too focused on money and successful exits as opposed to handling actual world-changing issues. Issues like healthcare and prevention-based initiatives can take years or decades to see positive returns for investors, but will have saved many lives in the process. Does this make these companies more or less worthy of investment?

Speaker: Chamath Palihapitiya, Social Capital

Going Public Is Strictly For The Big Dogs

With the craze for multi-billion-dollar IPOs, it can be hard to hear that that IPOs are not for everyone. Kupor sounded off about how difficult it can be for smaller companies ($1-$2B market cap) to go public and weather the uncertainty of the public markets. How has the resultant shift away from going public and towards more robust private markets impacted VCs, startups, and private investors?

Speaker: Scott Kupor, Andreessen Horowitz

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Keynotes

Stop Catering To Your Power Users

Benchmark’s Scott Belsky used his keynote speech to give Innovation Summit attendees a world-class crash course in good design and customer engagement, based on the starting premise of “Every user is selfish and lazy in the first 15 seconds; they need to see a product’s value right up front.” Required watching if you want to build a successful app or business.

Speaker: Scott Belsky, Benchmark

‘Strategic Money Is An Oxymoron’ And 13 Other Quotes On Corporate VC, AI Hype, And UX Fails

Mark Suster of Upfront riffs on the dos and don’ts of working as a corporate VC, along with more of the best quotes from day two of CB Insights’ Innovation Summit. Hear what Scott Belsky had to say about great design, new frontiers in insurance, AI in healthcare, and what’s next for cybersecurity.

Speaker: Mark Suster, Upfront

13 Future Technology Trends That Will Shape Business And Society

Anand Sanwal, co-founder and CEO of CB Insights, dug deep into the trends that will shape our future and the companies, investors, and corporations involved in them. Topics covered include customizable babies, robotics, artificial limbs, 3D-printed houses, and more.

Speaker: Anand Sanwal, CB Insights

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Panels

The Next Big Platform: Voice

Liew stated that voice will be the next big development platform. He and the other panelists discussed Alexa, Amazon, Google, Siri, and the Echo and how the abundance of data that Amazon is gathering on voice and how users interact with their voice interfaces will impact the future of the platform. Are bots, AR/VR, and voice control the next important UI platform?

Speakers:

  • Arthur Johnson, Twilio
  • Jeremy Liew, Lightspeed
  • Ari Levy, CNBC Moderator

Corporates Narrow Their Focus Within IoT

To date, the easiest applications in industrial IoT have been ones that save companies money, but the future is going to be about creating new business opportunities. One of the biggest challenges of the IIoT is that implementation comes with huge upfront costs. These cost challenges will only become greater as cybersecurity threats rise. How will companies deal with linking products and infrastructures that were likely never built to be connected?

Speakers:

  • Michelle Ash, Barrick Gold Corporation
  • Amit Chaturvedy, Cisco Investments
  • Mike Dolbec, GE Ventures
  • Dave Paresh, LA Times Moderator

Insurance Incumbents on the Smart Home, Big Data and Scale

Data analytics, machine learning, and a surge of new entrants are disrupting the traditional insurance model and creating easier, more affordable access to coverage. Legacy insurance companies can ignore trends like these at their perils. But how will they deal with them? Panelists discussed startups doing interesting things in customer experience, venturing into new lines, and how smart homes devices might reduce loss costs. Healthcare, property, and home insurance issues discussed.

Speakers:

  • Sarah Street, XL Catlin
  • Deborah Sundal, UnitedHealth Group
  • Matthew Wong, CB Insights Moderator

AI In Healthcare Will Save Lives

AI in healthcare would save lives and change the industry for the better, but how quickly will this new technology be integrated into a legacy industry like healthcar with all its bureaucracy and complexity? Panelists stressed the need for incremental introduction of the new technology to minimize negative outcomes during early adoption. Increased volumes of data will also allow AI systems to be more effective, but privacy issues abound. How will doctors, startups, and corporations handle these challenges?

Speakers:

  • Jack Young, Deutsche Telekom Capital Partners
  • Gabriel Otte, Freenome
  • Matthew Zeiler, Clarafai (AI 100)
  • Nick Romeo, The Washington Post Moderator

The Financial Services Industry Can’t Rush AI Adoption

AI’s integration into fintech is inevitable, but Gupta stated that slow progress is best in sensitive domains like fintech because it safeguards against potential unforeseen consequences of rapidly advancing technology. It’s likely that AI in fintech will first be implemented for technology like automated investing, as in Quantopian’s development of algorithmic investing. Developing user-friendly interfaces with AI was seen as being a bit further in the distance. “Areas where issues become hard, are when humans ask questions back, and we need intelligent responses,” said Kadaba.

Speakers:

  • Ramneek Gupta, Citi Ventures
  • Bharath Kadaba, Intuit
  • John Fawcett, Quantopian
  • Robin Wigglesworth, The Financial Times Moderator

Big Data Needs Big Intelligence

Blondeau summed up one of the biggest challenges facing Big Data companies and users: “Data tends to be overrated and intelligence tends to be underrated.” There’s a race not just to collect the most data, but to glean the most useful insights from it and that’s where startups and corporations can distinguish themselves with actual intelligence tied to their massive data sets. How will this impact how and how much data companies collect and what they do with it over time?

Speakers:

  • Antoine Blondeau, Sentient Technologies
  • Janet George, Western Digital
  • Peter Coles, Airbnb
  • Heather Somerville, Reuters

AI Critical To The Future Of Cybersecurity

AI is eating the world, including cybersecurity. Who better to keep a computer system safe than an AI? But it’s not a silver bullet. The future of cybersecurity is in prevention, and AI can be great for that, but they’ll need a deep well of data to make their threat detection and recognition more accurate. Will consumers and corporations be willing to take the AI leap?

Speakers:

  • Stuart McClure, Cylance
  • Nagraj Kashyap, Microsoft Ventures
  • Paresh Dave, Los Angeles Times

Startups Are The New Corporate R&D

It’s not a controversial statement to say that innovation can be harder for larger, legacy corporations. This is bad news for them given how good startups are at creating new, off-the-wall ideas and disrupting those companies. To stay competitive, corporate investors need to realize what innovative startups can do for them, invest wisely, and bring more than just money to the table. What makes for a good corporate-startup partnership? Find out!

Speakers:

  • Dave McClure, 500 Startups
  • Jeff Pashalides, Sequoia Capital
  • Tom Frangione, Greylock Partners
  • Matt Garrett, Salesforce
  • Paresh Dave, Los Angeles Times

The Rise of the Connected Car: How Automated Cars and More Will Change Mobility

A subscription for your car? The rise of automated cars, connected cars with sensors and software, and ride-sharing and -hailing companies will radically change the way people get around. Will car ownership disappear completely? How about a subscription for all your mobility needs or for your car upkeep like a fleet service package? The next few years will see the rise of automation in a wide variety of small increments, each paired with different safety protocols along the way. Watch the panel did into the biggest hurdles.

Speakers:

  • Stuart McClure, Cylance
  • Nagraj Kashyap, Microsoft Ventures
  • Paresh Dave, Los Angeles Times

Vignettes

The Changing Face of Financial Services

Unsurprisngly, there’s a ton of money flowing into FinTech startups. This sector has seen massive growth, the birth of unicorns and other large successful companies across verticals: loans, wealth management, and payments. But it hasn’t been smooth sailing all the time, with incumbents scrambling to win back ground and robo-advisors losing steam. The fintech landscape is changing and companies in the insurance tech, bitcoin/blockchain, and AI algorithm spaces are gaining steam.

Big Data, Insurance Tech, and Innovation

The insurance world is changing. Low interest rates have forced big insurance companies to look for efficiencies and changes in consumer behavior and the tech landscape have led them to offer new products that require new resources and ways of doing business. One resource that every insurance company and startup is looking for: data. Startups are also emerging that are geared towards digital distribution (Lemonade, etc.), more complex lines of insurance, as well as AI and machine learning. New insurance startups have embraced a digital native model and even use chatbots to help customers set up their coverage, trends that appeal to younger users.

Getting Around: Innovation in Transportation and Travel

Automated vehicles, decreased reliance on fossil fuels, and increased ride-hailing/car-sharing will massively change the way people get around cities. Startups working in these areas have secured $13.8B across 212 deals and leaders in the space like Uber and Tesla are also experimenting with driverless cars and getting them on the road in various test markets. Incumbents are sensing the sea change and seeking to partner with innovators to avoid being disrupted right out of existence, as younger city-dwellers eschew car ownership for ride- and vehicle-sharing. Even trucking companies are looking for automated options and last-mile fulfillers are deploying drones that will deliver packages to our doors via pedestrian spaces or air.

What’s Next In Healthcare?

Healthcare is a trillion-dollar nut that startups of all shapes and sizes have been cracking in a myriad ways for years. Today, big data and AI are poised to unleash massive changes in the healthcare space. The rise of personal sensors like the FitBit and Apple Watch, genetic testing, and more anonymized data from more interconnected medical records will allow doctors and AI analysts to get deeper insights into patient health, treatments, and preventive medicine. Areas like diagnostics, drug discovery, applies genomics, and automated care, aided by AI, are gaining traction as well.

Startups

Affectiva

Affectiva, an MIT Media Lab spin-off, is the pioneer in Emotion AI, the next frontier of artificial intelligence. Affectiva’s mission is to bring emotional intelligence to the digital world with emotion recognition technology that humanizes how people and systems interact. Affectiva’s patented technology uses computer vision, deep learning and the world’s largest emotion database of 4.8 million faces analyzed in 75 countries. Affectiva’s SDKs and APIs enable developers to add emotion-sensing and analytics to their own apps, games, devices and digital experiences. Affectiva is used by one third of the Fortune Global 100, including more than 1,400 brands, to gather insight and analytics in consumer emotional engagement. Affectiva’s emotion recognition technology is applied in many different verticals including online education, healthcare, gaming, robotics, media and advertising, market research, automotive retail, human resources, training and coaching, video communication, experiential design, and in wearables and devices.

Automat

Automat is the world’s first AI powered Conversational Marketing Platform. They enable companies to have personalized one-on-one messaging conversations with their customers to drive meaningful engagement that improves upon decades of static catalog-like website and mobile app content. Automat’s Bot Creator authoring environment lets marketing professionals, creatives, and developers collaborate to build Conversational Software powered by artificial intelligence that beats today’s one-size-fits-all sales funnels. Starting either from their Bot Library of pre-existing marketing objective templates such as Product Discovery, Influencer Marketing, Scheduling, Conversational Ads, etc, or beginning from scratch to build a wholly original conversational campaign, marketers can now reach mobile customers where they spend the bulk of their time, inside popular messaging apps like Messenger, Kik, WhatsApp, and WeChat. Riding on top of Automat’s Conversation as a Service (CaaS) platform, marketers can harness the power of Conversational Language Understanding (CLU) to recognize what consumers are saying about their brand and products providing a personalized one-on-one purchasing experience

Benevolent AI

Despite despite the huge growth of knowledge, scientific discovery has not changed for 50 years. It’s impossible for humans alone to process all of the information potentially available to advance scientific research – a new scientific paper is published every 30 seconds, there are 10,000 updates to PubMed every day. Consequently, only a small fraction of globally generated scientific information can form ‘useable’ knowledge. BenevolentAI applies AI and deep learning techniques to enable the analysis of vast quantities of complex scientific information. The Company is changing the way knowledge is created by producing an enormous structured, curated and qualified proprietary ‘data lake’ of dynamic usable knowledge that can be applied for real world use in conjunction with human experts. The Company’s first application of AI was in bioscience to accelerate drug discovery. BenevolentAI is now expanding into other large global scientific industries such a veterinary medicine, aggrotech, nutraceuticals and materials science.

Citrine

Citrine hosts the world’s largest materials repository platform. Citrine uses this platform to build AI software that enables more efficient discovery, optimization, manufacturing, and deployment of materials. Their software platform ingests structured and unstructured materials data from a wide variety of sources, both public and private; they then use AI engines to identify important signals in those data and directly enhance customers’ R&D and manufacturing efforts. They serve organizations that rely on cutting-edge materials for competitive advantage, in industries ranging from automotive, aerospace, consumer goods, batteries, and electronics.

Clarifai

Clarifai is an artificial intelligence company that excels in visual recognition, solving real-world problems for businesses and developers alike. Founded in 2013 by Matthew Zeiler, a foremost expert in machine learning, Clarifai has been a market leader since winning the top five places at the ImageNet 2013 competition, and predicts more than 1.4 billion concepts in images every month. Clarifai’s powerful visual recognition technology is built on the most advanced machine learning systems and made easily accessible by a clean API, empowering developers all over the world to build a new generation of intelligent applications. Clarifai builds products to make it easy, quick, and inexpensive for developers and businesses to innovate with AI, go to market faster, and build better user experiences. Clarifai also makes “teaching” AI just as accessible as they make using AI, which is why their technology is the most customizable and accurate solution in the market.

CognitiveScale

CognitiveScale builds machine intelligence software for healthcare, commerce, and financial services markets. The company’s products— Engage and Amplify help large enterprises increase customer engagement, improve decision-making, and deliver self-learning and self-assuring business processes. CognitiveScale has successfully deployed its software with multiple Global 500 companies and has formed strategic go to market and technology partnerships with IBM, Microsoft, and Deloitte. CognitiveScale was founded in 2013 by highly successful serial entrepreneurs and senior executives from IBM Watson, Oracle, and Salesforce with deep expertise in vertical enterprise software, cloud computing, and machine learning. The company has won numerous awards and featured in prominent research and publications. It is headquartered in Austin, Texas, with offices in the United Kingdom and India. Investors include Norwest Ventures, Intel Capital, IBM Watson, and Microsoft Ventures.

Cylance

Cylance is a cybersecurity products and services company focusing on stopping tomorrow’s attacks today. Founded in 2012 by ex-McAfee Global CTO Stuart McClure and ex-McAfee Chief Scientist Ryan Permeh, Cylance was created to solve the malware problem once and for all. Their flagship product, CylancePROTECT®, is the world’s first nextgeneration antivirus built on artificial intelligence and machine learning. Named the SC Magazine Award winner for “Best Emerging Technology” in 2015, CylancePROTECT offers exponentially improved prevention capabilities when compared to other endpoint security solutions. For a demo of CylancePROTECT, visit us at cylance.com

Freenome

Freenome is building software to understand the changes in plasma cell-free DNA (cfDNA) patterns over time. By studying normal cfDNA dynamics Freenome discovered signatures for early cancer detection that outcompete existing screening methods from a single blood draw in Prostate, Lung, Colorectal, and Breast cancers. In addition, the deep learning models have been leveraged to distinguish disease subtypes such as castration-sensitive from castration-resistant prostate cancer. Thus, the resolving power of Freenome’s software enables both disease diagnosis and personalized treatment recommendation from the same platform. Freenome raised $5.5M from Andreessen Horowitz, Founders Fund, and DCVC, and partnered with UCSF, Stanford, Duke, Emory, NYU, and several other centers to expand the validation of Freenome’s technology

Digital Genius

DigitalGenius brings practical applications of deep learning and artificial intelligence into customer service operations of leading companies. At its core are deep learning algorithms, which are trained on historical customer service data and integrated directly into the contact center’s existing software. Already deployed with industry innovators like KLM Royal Dutch Airlines, Unilever, and HSBC, the product delivers value through two main functions: Predictive Case Intelligence: using machine learning to predict and automatically pre-fill all case metadata around an incoming customer service message. Improving accuracy and significantly reducing average handling time. Human+AI Question Answering: a deep learning algorithm trained on historical customer service logs suggests and automates answers to customer questions over email, live chat, social media, mobile messaging and SMS. DigitalGenius has built a scalable deep-learning product for the customer service industry, leveraging and creating cutting edge research, to transform the customer service function inside businesses.

GrokStyle

Given a photo, consumers want answers to questions like “What is this product? Where can I buy it?”, “Show me where/how people are using this product”, “Who in the world is posting about this product?” GrokStyle provides these answers using innovations in deep learning technology, developed by co-founders Dr. Sean Bell (CEO) and Prof. Kavita Bala (Chief Scientist). GrokStyle is developing software for visual search to enable instant recognition of an object. The techniques they are developing can be applied broadly to domains like interior design, apparel search, real estate search, product lookup, etc. The company’s current focus is on interior product design. GrokStyle will provide new capabilities to consumers and retailers to assist in matching a consumer’s desired product with the correct or closest matching product based a visual similarity.

H20 Ai

H2O.ai is the maker behind H2O, the leading open source AI platform for data products. With H2O, a plethora of machine learning models (from linear models to tree-based ensemble methods to Deep Learning) can be trained from R, Python, Java, Scala, JSON, H2O’s Flow GUI, or the REST API, on laptops or servers running Windows, Mac or Linux, in the cloud or on premise, on clusters of up to hundreds of nodes, on top of Hadoop or with the Sparkling Water API for Apache Spark. Some of H2O’s mission critical applications include fraud, anti-money laundering, auditing, churn, credit scoring, user based insurance, ICU monitoring, predictive maintenance, operational intelligence and more in over 7,000 organizations. H2O is brewing a grassroots culture of data transformation in its customer communities. Customers include Capital One, Progressive, Zurich, Transamerica, Comcast, Nielsen Catalina Solutions, Neustar, Macy’s, Walgreens, Kaiser Permanente and Aetna.

Kasisto

KAI, a conversational AI platform, enables companies to engage and transact with their customers via intelligent conversations, anytime, anywhere. KAI Banking is fluent in banking with thousands of intents and millions of banking sentences. Financial institutions power smart bots on messaging platforms like Facebook Messenger and virtual assistants in their mobile apps to fulfill requests, solve problems, and predict needs for their customers. KAI-powered bots and assistants help with payments, transaction and account insights, and personal finance management. They also provide new ways for banks to support and market product and services. The experience is as natural as texting a friend because KAI understands human-like conversations including idiosyncratic phrases, rapid topic changes and interruptions. KAI keeps track of the flow of conversations versus just natural language understanding – staying focused on customers’ goals and interpreting the context of a conversation. It enables lifestyle banking with financial decisions woven into everyday life.

Lunit

Lunit is an AI-centric company and develops a visual perception technology that interprets medical images and visualizes abnormal regions based on its own data-driven criteria. The company gained international attention after achieving the highest rank among startup teams in the ImageNet Large-scale Visual Recognition Challenge (ILSVRC) 2015. Based on the expertise in deep learning, Lunit has been working on abnormality detection in chest x-ray, mammography as well as automatic grading of breast histopathology slides. The high level of Lunit’s technology has been well demonstrated, presented 4 scientific abstracts in Radiological Society of North America (RSNA) 2016 and won the first place in the Tumor Proliferation Assessment Challenge (TUPAC) 2016 ahead of IBM and Microsoft. Lunit envisions a constructive partnership between physicians and technology in becoming “better together” faced with challenges in accurate diagnoses of diseases.

Petuum Inc

Petuum is creating a development platform that serves the full spectrum of Artificial Intelligence and Machine Learning applications. They empower organizations to create AI/ML solutions that are correct, fast, scalable, and consume minimal computing resources. Their omnisource platform processes and integrates data in different formats such as numeric, textual, imagery, tabular, structured or unstructured, static or streaming from diverse sources like social media, consumer profiles, electronic health records, sensor logs from IoT devices, transaction logs from financial systems, and machine logs from manufacturing equipment. It is also omni-lingual, programmable with multiple popular languages such as Python, R, Java, C++, and Julia. It’s also omnimount, supporting different hardware platforms such as datacenters, workstations, laptops, mobile, and embedded devices.management, and beyond.

ROSS

ROSS Intelligence uses artificial intelligence to allow attorneys to do more than was ever humanly possible and focus on what matters most – their clients. Co-founded in 2014 by Andrew Arruda, Jimoh Ovbiagele and Pargles Dall’Oglio, ROSS developed a proprietary framework, LegalCognition and has combined with IBM Watson, an early partner of ROSS Intelligence. ROSS’ first legal A.I. went live in the area of legal research, allowing attorneys to perform tedious research tasks in seconds rather than hours. A graduate of Y Combinator’s 2015 class and NextLaw Labs, ROSS is currently partnered with a variety of in-house teams, bar associations, and law schools, and has a client base that includes Dentons, the world’s largest law firm by headcount, and Latham & Watkins, the world’s largest law firm by revenue.

Sight Machine

Sight Machine is the category leader for manufacturing analytics and used by Global 500 companies to make better, faster decisions about their operations. Sight Machine’s analytics platform, purpose-built for discrete and process manufacturing, uses artificial intelligence, machine learning, and advanced analytics to help address critical challenges in quality and productivity throughout the enterprise. The platform delivers “AI for the plant floor” and is powered by the industry’s only Plant Digital Twin (patent pending), which enables real-time visibility and actionable insights for every machine, line, and plant throughout an enterprise. Founded in Michigan in 2011 and expanded to the Bay Area in 2012, Sight Machine fuses the spirit of Silicon Valley technology innovation with rock-solid Detroit manufacturing. Its team includes the founders of Slashdot along with leadership from early Yahoo, Palantir, Tesla, Cisco, IBM, McKinsey, and Apple.

SigOpt

SigOpt is the optimization platform that amplifies your research. SigOpt takes any research pipeline and tunes it, right in place. Their cloud-based ensemble of optimization algorithms is proven and seamless to deploy, and is used by globally recognized leaders within the insurance, credit card, algorithmic trading and consumer packaged goods industries. SigOpt was born out of the desire to make experts more efficient. While co-founder Scott Clark was completing his PhD at Cornell he noticed that often the final stage of research was a domain expert tweaking what they had built via trial and error. After completing his PhD, Scott developed MOE to solve this problem, and used it to optimize machine learning models and A/B tests at Yelp. SigOpt was founded in 2014 to bring this technology to every expert in every field.

Sentient Technologies

Sentient’s mission is to transform how businesses tackle their most complex, mission-critical problems by empowering them to make the right decisions faster. Sentient’s technology has patented evolutionary and perceptual capabilities that will provide customers with highly sophisticated solutions, powered by the largest computer infrastructure dedicated to distributed artificial intelligence.

Zymergen

Zymergen is a technology company unlocking the power of biology. Zymergen has developed a proprietary platform, which uses robots and machine learning to engineer microbes faster, more predictably, and to a level of performance previously unattainable. These microbes, and the products they produce, have broad applications across industries such as chemicals and materials, agriculture, and healthcare. Zymergen works with customers in these industries to improve the economics of existing products, bring new products to market faster, and to develop entirely new products.

SkyMind

Skymind is the Red Hat of artificial intelligence. It provides support, training and services around an enterprise distribution of its opensource libraries called the Skymind Intelligence Layer (SKIL). These libraries include Deeplearning4j, the most widely used deep learning tool for Java; and the scientific computing library ND4J, or n-dimensional arrays for Java (Numpy for the JVM). Deep learning can equal and surpass expert human accuracy on many pattern recognition tasks, and Skymind is applying it to old, hard business problems such as fraud detection, network intrusion detection, hardware breakdown prediction, churn prediction, market forecasting and image recognition. Skymind’s customers are in such sectors as financial services, telecoms, manufacturing, healthcare and retail. Skymind’s open-source software integrates with the rest of the AI stack, working with tools such as Hadoop, Spark, Kafka, and ElasticSearch. Skymind works with large clients on-premise and in the hybrid cloud to ensure data privacy and security.