We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Register today!
Machine learning and artificial intelligence are timely subjects that spark the public imagination. In 2016, between $26 billion and $39 billion was invested in AI, according to recent estimates from the McKinsey Global Institute, a leading private-sector think tank. That number is three times the amount spent just three years prior, an increase driven by entrepreneurial activity and technological advancements.
Although thousands of venture firms are investing in sexy machine learning projects, there are very real benefits that machine learning and AI are realizing now, not in a future timeline of self-driving cars and full home automation. Smart investors and observers should consider following companies that are solving these five issues.
1. Eliminate dull tasks
We all have days where we have to do busy work: dull, boring tasks that may be necessary but are neither important nor valuable. Fortunately, machine learning and AI are beginning to address some of this through human-computer interaction technologies. Virtual assistants like Siri, Cortana, and Google Assistant perform basic tasks, conversing with the user in natural language.
More robust use of this technology can be found in companies like Dialogflow, a subsidiary of Google, and formerly known as Api.ai. Such companies build conversational interfaces that use machine learning to understand and meet customer needs. Want to send 5,000 calendar invites, or book a flight from San Francisco to Paris? Need a reliable method to answer basic customer questions online, instantaneously? Solutions like those offered by Dialogflow cut through the dull work that would otherwise require hours of human time.
2. Focus diffuse problems
Data informs every level of a modern company’s operation. Even small businesses have a lot of material to interpret, so a major enterprise consumes amounts of information at a scale equally awe-inspiring and terrifying.
The expanse of available data has gone beyond what human beings are capable of synthesizing, making it a perfect job for machine learning and AI. For example, Elucify helps sales teams automatically update their contacts. By simply clicking a button, information is pulled from a multitude of public and private data sources. Elucify takes all of that diffuse data, compares it, and makes changes where necessary.
3. Distribute data
Modern cybersecurity drives a need for comparing terabytes of inside data with a comparable amount of outside data. This has been a very difficult problem to solve, but machine learning and AI are perfect tools for the job.
I was an investor in Vectra Networks — a security company that uses AI to fight cyber-attacks — during my time at Intel Capital. By comparing outside network data to the log inside the enterprise, Vectra Networks can automate the process of detecting attacks. Human workers simply could not wrap their arms around such a broad distribution of information.
A similar company is RiskSense, which pioneered proactive cyber risk management and utilizes machine learning and AI to automate data processes. As the problems of cybersecurity change and increase, organizations like these will be vital in addressing the problems of distributed data.
4. Solve dynamic data
Every business book written in the past 50 years has sections devoted to developing efficiency; it is a prized workplace characteristic. A major stumbling block in that pursuit has always been addressing individual employee traits. Now, some forward-looking companies are adopting AI to solve the dynamic problems of human behavior.
A startup called GitPrime utilizes code data to determine the most productive work patterns for software engineers. These complex patterns help organizations move faster and be more responsive to changing needs. Finding that human influence in millions of lines of code would have been impossible in the past, but machine learning and AI can help us see them.
5. Prevent dangerous issues
Cutting-edge industrial systems combine AI-powered robots, 3D printing, and human oversight. Not only can companies realize billions in savings with these systems, but they will also save lives.
Industrial automation leader Rethink Robotics builds interactive robot systems driven by AI. This process not only reduces cost and improves efficiency for the company, but creates much safer environments for the human workers. The dangerous elements of manufacturing jobs are supplanted by machines, while the brains behind them remain safe and human.
Futuristic, headline-grabbing investments in machine learning and AI certainly have their place — I have seen a lot of venture capital money spent in that direction. But having spent 18 years in the venture industry, I look for companies that produce impactful and realistic solutions. Machine learning and AI provide an excellent approach to solving many of these dull, diffuse, distributed, dynamic, and dangerous problems. I encourage investment groups to consider carefully.
Ken Elefant is the managing director at Sorenson Capital, where he leads the firm’s early-stage technology practice.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Learn more about membership.