I have already (partially) reviewed previous studies where data have been proved to help identify signals that are relevant to assess the success potential of a startup. Even though the list is quite comprehensive, every study usually tends to look at one single factor and a couple of different success scenarios (namely, acquisition and IPO). In our work, we tried to have a more holistic view and use over 120,000 companies to spot signals not only for acquisitions and IPOs but also to compute the probability of raising a subsequent round of funding or shutting the startup down. In the same fashion as backtesting, we created a time-aware approach and analyzed companies that were no older than four years old by 2015 and tried to predict their success in the following three years. We also used more than a hundred variables as possible explanatory indicators of success, as well as five different models: Support Vector Machines (SVM); Decision Trees (DT); Random Forests (RF); Extremely Randomized Trees (ERT); and Gradient Tree Boosting (GTB).

After the last financial crisis, the interest rates decreased exponentially and venture capital suddenly became an attractive option to achieve high returns. However, in only a decade the market moved so fast, got so mature and saturated, and so many empires have been created, that is now cumbersome to obtain sustainable returns investing in risky early-stage companies. In fact, capital is abundant nowadays and funds have been raised everywhere, while there is no scarcity either in companies of every shape and size. For these reasons, investing has become incredibly competitive and it has never been harder to spot the needle in the haystack that would make you rich. Unfortunately, the toolbox investors currently have available is not robust enough to reduce their risk and help them managing uncertainty in a better way. This is where machine learning can come to aid.

Twenty-seven startups were funded in May for a total of $2.5 billion. This month's $2.5 billion in fundings doubles the January thru April total of $2.5 billion. Four acquisitions occurred in May. The most notable was SPX Corp., the large inspection equipment components manufacturer, which acquired CUES, a Florida robotic pipeline video inspection and rehab company, for $189 million. Look at all those LiDARs!

Twenty startups were funded in April 2018. Fifteen disclosed transaction amounts totaling $808 million of which the $600 million to SenseTime, the Alibaba-funded Chinese deep learning and facial recognition software provider focused on smart self-driving vehicle systems, was by far the largest. Seven acquisitions also occurred in April. The most notable was the acquisition by Teradyne (which previously acquired Universal Robots and Energid) of MiR (Mobile Industrial Robots) for $148 million with an additional $124 million predicated on very achievable milestones between now and 2020. SenseTime, a Chinese deep learning and facial recognition software provider focused on smart self-driving vehicle systems, raised $600 million in a Series C funding round led by Alibaba Group with participation by Temasek Holdings and Suning Commerce Group.

Corrindus has raised $118 million and installed 33 systems to date. Playground Global led the round, with participation from Sony Innovation Fund and existing investor Robotics Hub. Agility's two-legged Cassie robot is already deployed in 6 research institutes. Agility is planning on using Cassie for everything from deliveries to facility inspections to hazardous search-and-rescue operations.

Ubtech, a Shenzhen-based humanoid robots maker startup, raised $400 million in a Series C round led by Tencent Holdings (which invested $40 million in the round). Ubtech (Union Brothers Technology) builds and sells toy robots. Their most recent is a $300 Star Wars Stormtrooper robot which will ship just before the movie debuts mid-December. TuSimple, a Chinese startup providing autonomous driving technology for the trucking industry, raised $55 million in a Series C round led by Fuhe Capital with Zhiping Capital and SINA Corp. Note that TuSimple raised $20 million in August in a Series B round.

The following graphs highlight the exit activity in the Artificial Intelligence sector. The graphics include data through July 2017. The Machine Learning Applications category leads the sector with 4 IPOs and 43 acquisitions. The Natural Language Processing category is the runner-up with 4 IPOs and 29 acquisitions. We are currently tracking 1896 Artificial Intelligence companies in 13 categories across 70 countries, with a total of $19B in funding.

CloudMinds, a Chinese startup developing cloud-intelligence-based services for robotics and other application areas, raised $100 million in a Series A funding round. Although no funder information was provided, seed financing was provided by SoftBank, Foxconn, Walden International and Keytone Ventures. Desktop Metal, a 3D metal printing startup based in Burlington, Mass., is gearing up to take its first product into production and raised $45 million from GV, BMW I Ventures and Lowe's Ventures. The company has now raised a total of $97 million. Other investors include NEA, Kleiner Perkins Caufield & Byers, Lux Capital, GE Ventures, Saudi Aramco, and Stratasys.

The Machine Learning - Applications category leads the sector in IPOs (4 companies), while the Computer Vision - Applications category leads the sector in acquisitions (22 companies). Both 2013 and 2015 lead the sector in IPOs (2 companies for each year), while 2014 leads the sector in acquisitions (17 companies). We are currently tracking 1185 Artificial Intelligence companies in 13 categories across 71 countries, with a total of 7.3 Billion in funding. Click here to see the full Artificial Intelligence landscape report and data.

April was a big month for investing in robotics – 19 companies were funded to the tune of 175 million vs. 15.8M in January, 18.6M in February, and 45.4M in March. Four companies were acquired with 3 of the 4 reporting selling prices totaling 422 million. Precision Hawk CEO Bob Young said, "Building and selling planes will arguably be the smallest part of our business. Our biggest opportunity and the faster growing part of our business is the platform we built for aerial data services. At DuPont Pioneer, we're driving a new era of agriculture productivity that enables farmers to increase profitability and sustainability through data-driven insights," said Neal Gutterson, DuPont Pioneer Vice President of R&D.