Artificial Intelligence and Machine Learning will be eating the world. Don’t take my word for it – in the roundup of venture capital predictions for 2017, I found it to be the top recurring theme. Some examples:
- AI will be the new mobile. Investors will ask management what their “AI strategy” is before investing and will be wary of companies that don’t have one (Fred Wilson, USV)
- Security will shift from defensive to predictive AI-powered security (Norwest Venture Partners)
- AI, M/L, And D/L will continue to be darlings of VC & M&A? (Chris Rust, Clear Ventures)
- Artificial Intelligence is an important, foundational technology that gets more important every year and will be used to solve more and more problems going forward. Many large companies will be built (Michael Wolfe, Point Nine Capital)
Since AI and ML startups cut across verticals (analytics, fintech, health, adtech, security, etc), it’s easier to group them under the “machine intelligence” umbrella, coined by Shivon Zillis, a partner at Bloomberg Beta. In 2016 alone, 300+ “machine intelligence” (AI + ML) startups in Europe raised over €1.4 Billion in VC funding.
But what’s signal and what’s noise? Is “Deep Learning” the new “Big Data”?
While the terms AI and ML get thrown around readily, the companies that truly apply Artificial Intelligence, Machine Learning, Computer Vision and Deep Learning have the potential to address problems that were unsolvable before.
DeepMind beating the world’s best at Go and AI beating pro-poker players consistently, represent much more than a computer getting good at playing video games:
The gaming world offers a perfect place to start machine intelligence work (e.g., constrained environments, explicit rewards, easy-to-compare results, looks impressive)—especially for reinforcement learning. And it is much easier to have a self-driving car agent go a trillion miles in a simulated environment than on actual roads. Now we’re seeing the techniques used to conquer the gaming world moving to the real world (Shivon Zillis).
These events mean that technology is advancing fast enough to make better decisions than humans in order to accomplish a given task. What tasks are next? that’s the essence of the list of startups below: the contenders who want to ultimately replace X with AI.
Noteworthy Machine Intelligence startups in Israel ??
There’s been a proliferation of Machine Intelligence companies in Israel. Below are 30 Artificial Intelligence (AI) and Machine Learning (ML) startups to watch in Israel (Part 1).
Voyager Labs (2012) – cognitive computing for understanding human behaviour. Voyager’s cognitive-computing, deep-insights platform assesses billions of publicly available, unstructured data points to provide insights for its clients in finance, retail and consulting. The company emerged from stealth in November 2016 and announced a $100 million investment round from Sir Ronald Cohen, Lloyd Dorfman, OCAPAC Holding Company, and Horizons Ventures.
Anodot (2014) – a real-time analytics and automated anomaly detection system that discovers outliers in vast amounts of data. Raised $8M in series B in September 2016. The company took an approach prevalent in cyber security and ported it to Business Intelligence.
Beyond Verbal (2012) – understands people’s moods, attitudes and emotional characteristics (also known as personality) from their raw vocal intonations in real-time, as they speak. One of the interesting applications for this technology is assessing intellect in job interviews.
Fraugster (2014) – Technically based in Berlin, the company uses AI to predict malicious attacks before they happen. This is done by enriching transaction data points such as name, email address, and billing and shipping address with around 2,000 extra data points, such as an IP latency check to measure the real distance from the user, IP connection type, distance between key strokes, and email name match. Chen Zamir is the company’s CTO and former Intelligence officer in the IDF as well as Paypal risk manager.
Innoviz Technologies (2016)- High Definition Solid State LiDAR (HD-SSL), enables smart and advanced 3D remote sensing for fully autonomous vehicles, while significantly reducing both cost and size. All key technologies for autonomous driving. Innoviz recently teamed up with Magna, a tier-1 supplier to the automotive industry.
Kang Health (2016) – HQ’d in New York and started by Allon Bloch, the former CEO of Wix.com, Kang Health wants to crowdsource health data to provide better information about health conditions, symptoms and treatments. the company raised a strong $3.3M in seed round in November.
Orcam (2010)– Orcam can potentially help the 280 million visually impaired people to automatically read any text they are looking at via a discreet device that attaches to the user’s eyeglasses. Orcam was created by the founders of Mobileye (NYSE: MBLY and one of Israel’s largest IPOs), Prof. Amnon Shashua and Ziv Aviram (great interview with them here). The company unveiled its latest model in January 2017 at CES, offering the blind the ability to read and recognize faces.
Twiggle (2014) – Twiggle develops next generation e-commerce search leveraging advanced techniques in data science, artificial intelligence, machine learning and natural language processing (NLP). As part of their recent investment round by Alibaba, Twiggle’s announced Udi Manbar, former head of search at Google and Chief Scientist at Yahoo joined the company’s board of directors.
Windward (2010) – maritime data and analytics company. Windward processes more than 100 million data points every day to deliver unprecedented insights into the vital cargo-shipping industry as well as security (smuggling, sovereignty etc). The platform does this through combining ships’ location data with data about the ships (their capacity to carry weight vs. their actual weight via satellite images) to map optimized shipping paths and behaviours while at sea. Co-founders Ami Daniel and Matan Peled both served as naval officers. This interview with Ami sheds more light on the company.
YouAppi (2011)- YouAppi creates adtech to streamline mobile user acquisition. The company’s tech predicts the right app and location to present an ad based on user and cohort behaviour. It uses machine learning and predictive algorithms to analyze over 250 terabytes of data daily.
Chorus.ai (2015) – Chorus uses AI to analyze sales calls by joining the conference calls, transcribing the data and extracting important action items. The company just raised its $16M series A led by Redpoint Ventures, just four months after announcing their seed funding.
Cimagine (2012) – launched the world’s largest implementation of AR in retail to date with Shop Direct. Rumoured to have been acquired by Snap in December 2016 for $30-40M and will likely serve as Snap’s R&D center in Israel.
Zebra Medical Vision – The company combines its vast imaging database with deep learning techniques to build algorithms that will automatically detect and diagnose medical conditions – helping radiologists to detect overlooked indications and give fast, accurate imaging diagnosis. Zebra Medical Vision was recently selected as one of the most Innovative AI companies for 2017 by Fastcompany.
Logz.io (2014) – Logz.io is an AI-powered log analysis platform that offers the open source ELK Stack as an enterprise-grade cloud service with machine learning technology. Provides real-time access to data insights based on the collaborative knowledge of system administrators, DevOps engineers, and developers throughout the world. The company completed its $16M Series B in November 2016.
Revuze (2011) – The company graduated from the Nielsen Innovate incubator in Caesarea, the company analyzes user sentiment on products, product-attributes and brands by analyzing reviews as well as sentiment extraction from survey responses, call-center text and social media. Source text is analyzed against category taxonomies generated via semi-supervised machine learning. They are part of a bigger trend to automatically evaluate user feedback.
Fifth Dimension (2014) – Fifth dimension provides big data analysis for the Intelligence and security space. It can process petabytes of data in real time to identify faces in crowds, voices in audio recordings and predict threats, (and opportunities) before they become a reality. The company’s chairman was the former General Chief of Staff of the IDF 2011-2015.
Deep Instinct (2014)- Deep Instinct safeguards the enterprise’s end-points or mobile devices against any threat on any infrastructure, whether or not it is connected to the network or internet. Deep Instinct was named “Cool Vendor in 2016” by the Gartner Research Group.
Nexar (2015)- A community-based AI dashboard cam app that helps drivers to protect themselves on the road and provides documentation, recorded video, and situational reconstruction in case of an accident. The Nexar solution employs machine vision and sensor fusion algorithms, leveraging the iPhone’s sensors to analyze and understand the car’s surroundings. Nexar recently hired deep learning heavyweight Professor Trevor Darrell from UC Berkeley as Nexar’s Chief Scientist.
SparkBeyond (2013) – an automated general purpose research engine designed to leverage and intelligently augment masses of data that exist on the web, and discover complex patterns within them. The SparkBeyond Discovery Platform is being used by Fortune 500 companies in the Finance, Manufacturing, Life-Sciences, Energy, e-Commerce, Internet and Healthcare industries.- Fdna (2010) – FDNA stands for Facial Dysmorphology Novel Analysis, a technology that transforms facial photos into deep and accurate phenotypic information in real time.
The company developed the Face2Gene suite of phenotyping apps that facilitate comprehensive and precise genetic evaluations through computer vision. 
Loom systems (2015)– Loom Systems Ops sends proactive notifications about meaningful issues in an IT environment, empowering its end-users with visibility into their IT blindspots. The company applies AI to sculpt big data into reports delivered in plain English.
AIdoc (2015)- Aldoc applies deep learning to the radiology space by guiding the radiologist to the most relevant places in the scan and consolidating multiple sources of data to one screen. The company hopes to take the number of diagnostic errors to zero. The company recently completed its $3.5M series A in November of 2016.
Atidot (2016) – an InsureTech startup that offers a SaaS platform to insurers. Atidot offers a predictive analytics platform for actuarial science and risk management.- Cognata (2017) – still in stealth, Cognata is developing Artificial Intelligence simulation engines for automated vehicles. The company has no public URL yet.
Neurala (2006) – Neurala’s Brains for Bots SDK helps bring artificial intelligence to drones, robots, cars, and consumer electronics by helping these devices inspect their environment, make decisions and navigate obstacles. The company already works with a broad range of clients including the US Air Force, Motorola and Parrot; to back its vision it recently raised $14M series A in January 2017.
Javelin Networks (2014) – the company’s main value proposition is to protect the Active Directory (used by 9 out of 10 companies) from cyber attacks by combining AI, obfuscation and advanced forensics methodologies right at the point of breach. Javelin has just announced its $5M series A in February 2017.
Dynamic Yield (2011)- Dynamic Yield’s advanced machine learning engine builds actionable customer segments in real time, enabling marketers to increase revenue via personalization, recommendations, automatic optimization & 1:1 messaging. Media and e-commerce sites are prime customers of this. The company has just announced its $22M series C in December 2016.
Dragonera (2016) – AI based software development service. The platform can supposedly automate up to 70% of early development of new products by leveraging micro-services, and pre-existing pieces of code. The timeframe for a fully functional product varies between 14 and 45 days. Dragonera raised $3M seed round in December led by Singulariteam.
MedyMatch Technology (2013)- automated medical diagnostic support system that utilizes machine learning and expert feedback to deliver diagnostic recommendations for medical imaging. Raised $2M in seed funding in March 2016.
Augury (2012) – predictive maintenance for industrial IOT. Augury automatically diagnoses machines based on the sounds they make. The product connects vibration and ultrasonic sensors to smartphones and pairs them with machine-learning algorithms to reduce environmental impact, energy usage, and operational costs. See Augury’s tech in action on The Verge.
Credit is due to Ha Duong who compiled a list of 600+ AI startups in Europe at Tech.eu: 600+ European AI tech startups to watch, which served as a starting base for this list. On the next post, you’ll see 20+ more Israeli startups in Machine Intelligence and the clusters that are being formed. Know a company that needs to be on this list? drop me a line at eze at vccafe dot com.
Co Founder and Managing Partner at Remagine Ventures
Eze is managing partner of Remagine Ventures, a seed fund investing in ambitious founders at the intersection of tech, entertainment, gaming and commerce with a spotlight on Israel.
I'm a former general partner at google ventures, head of Google for Entrepreneurs in Europe and founding head of Campus London, Google's first physical hub for startups.
I'm also the founder of Techbikers, a non-profit bringing together the startup ecosystem on cycling challenges in support of Room to Read. Since inception in 2012 we've built 11 schools and 50 libraries in the developing world.
I'm a former general partner at google ventures, head of Google for Entrepreneurs in Europe and founding head of Campus London, Google's first physical hub for startups.
I'm also the founder of Techbikers, a non-profit bringing together the startup ecosystem on cycling challenges in support of Room to Read. Since inception in 2012 we've built 11 schools and 50 libraries in the developing world.
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