Even as 2017 winds down, all eyes are still fixed on the future of AI and machine learning.
With the recent revelation that the number of VC funding deals to AI startups stalled in Q3 2017, some critics note that investor interest in AI has perhaps reached its peak. Despite this claim, investors put forth well over $ 1.165 billion toward AI and machine learning companies, signaling that new blood in the space will have to fight tooth and nail to score future funding.
The takeaway heading into 2018? The barrier to entry for AI startups is becoming higher than ever – even as the current movers and shakers in the space are poised to change tech as we know it.
But who are those players, anyway? What should trends should we be looking out for? Below we’ve outlined the top five machine learning companies to watch next year.
1. Status Today
The concept of productivity goes hand in hand with machine learning and automation. Status Today is working to boost productivity in the workplace through AI to both keep track of employee performance and detect anomalies.
This robust, AI-driven engine clues leaders in on which roles, departments and teams are the most engaged and active, in real-time. Status Today automatically stores all employee activity logs, which companies can ultimately use to track workplace trends over time. Rather than digging for developments regarding employee performance, this AI solution paints a clear picture of any given company’s productivity status automatically.
As an added bonus, Status Today’s solution is striving to keep company data safer, using company information to detect any employees who could represent security threats by acting atypically. In a tech world plagued with breach concerns, this is obviously a major plus.
Organizations struggling to make sense of the many moving pieces of business intelligence are continuously looking at machine learning for a solution. Sisense not only simplifies the process of analyzing BI through visualized data, but also uses machine learning to help business understand how to meet goals regarding their most important metrics.
Sisense Pulse uses AI to track the desired KPIs and crucial metrics of an organization over time, which can include anything from including sales numbers and win rates to supply chain performance and production floor efficiency. The system detects abnormalities and helps businesses determine at a glance whether or not they’re on track to reach their respective goals.
Custom alerts keep businesses informed as to whether or not they need to take immediate action regarding a particular KPI. Sisense Pulse also integrates with the likes of Zapier and Slack, ensuring speedy action and a smooth synthesis with the tools that are already second nature to modern companies.
Sometimes it’s the problems you think only your organization has, which can best be solved by the likes of AI. Such is the case of Deepgram, the automated audio transcription service that uses deep learning to convert audio into text.
From interviews and meetings to conversations and beyond, Deepgram removes the headache of having to sort through lengthy audio files manually, or to pay for professional transcriptions that are often fraught with errors. Using heaps of data to make recordings searchable, items such as closed captions, subtitles and transcriptions are only a copy-and-paste away.
Claiming to leave Google Speech API in the dust, Deepgram prioritizes accuracy and efficiency on behalf of companies with demand for processing a high volume of audio. The self-learning solution is poised to improve with time to provide precise transcriptions in an instant.
The theme of AI removing otherwise tedious tasks from the plates of organizations is perhaps most prevalent in the work currently being done by Iris.AI.
This tool is capable of sifting through scientific language on behalf of R&D departments as well as individual users. Breaking down key concepts and topics from over 66 million Open Access papers, Iris.IA notes that their solution can cut traditional research time in half for its users.
In short, the functions of this so-called science assistant are transforming the way that we look at research at large.
Visual search technology offers some key advantages over text search. With keywords, if the way you formulate your query doesn’t match the taxonomy of the original content publisher, then your results won’t match what you’re hoping for. But with visuals, assuming that the search system is “smart” enough to discern the object’s attributes, you’re far more likely to find what you’re after.
ViSenze’s visual search engine seems to have the smarts required to make this happen. Through partnerships with Ratuken and other ecommerce platforms, their machine learning-powered technology is already helping shoppers find the looks that they see in the wild. The Singapore-based company is currently forging alliances with content publishers to help monetize their videos by making clips shoppable.
In the ever-changing worlds of AI and machine learning, these companies are among the top holding the reigns for their respective areas of expertise. As investors are becoming more selective in regard to the companies they invest and more advanced bots emerge, the buzz over the AI space will only continue to grow.