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The 2018 Global CVC Report
CB Insights’ third annual cohort of AI 100 startups is a list of 100 of the most promising private companies providing hardware and data infrastructure for AI applications, optimizing machine learning workflows, and applying AI across a variety of major industries.
Our research team selected the 100 startups from a pool of 3K+ companies based on several factors, including patent activity, investor profile, news sentiment analysis, proprietary Mosaic scores, market potential, partnerships, competitive landscape, team strength, and tech novelty.
The AI 100 companies are disrupting 12 core sectors, including healthcare, telecommunications, semiconductors, government, retail, and finance, as well as the broader enterprise tech stack.
(Note: this excludes patent applications in international markets by US and non-US startups.) Hover, for example, allows users to take pictures of their home using a smartphone camera, and uses image processing techniques to stitch together a 3D model of the home.
Cerebras, which has revealed few details about its AI processor, filed 3 patents in 2018 that highlight its R&D on semiconductor fabrication and accelerating deep learning.
Artificial Intelligence in testing: Top five use cases in financial services in 2019
“Our intelligence is what makes us human, and AI is an extension of that quality.” – Yann LeCun The global financial services industry is at an inflection point.
Our recent World Quality Report, a joint publication of Capgemini, Microfocus and Sogeti, indicates that artificial intelligence (AI) in testing is an upcoming trend to improve speed to test particularly for digital initiatives in financial services.
Finally create a small team comprising of professionals possessing data analytics, statistics and machine learning algorithms that can create proof of concepts and drive this transformation.
Addressing social determinants of health? Consider artificial intelligence and machine learning
'Social determinants of health' is one of the hot buzz-phrases in healthcare these days, and for good reason.
SDOH refers to outside factors that may impact a patient's health, such as employment status and access to education, and providers can improve efficiency and curb costs by addressing these factors.
'What's new is the emerging of two fields: computer science groups, where they focus on machine learning, and statistics groups from universities,' said Berg.
Left to their own devices (so to speak), computers are essentially robotic research assistants which help researchers make decisions based on data.
Statistics groups and economists make predictions, but also focus on estimation -- for example, looking at information to prognosticate what clinical affect a particular treatment might have.
Over the years, though, many factors have been added to that mission which facilitate education but aren't directly related -- such as offering free or reduced-price meals to students of low socioeconomic standing.
'As risk has shifted from a payer to a health plan to different payers, it seems to me that value-based care is really the catalyst for using social determinants of health,' Berg said.
Putting data into the EHR in a systematic way is helpful, but knowing what to do next with the data can trigger an actual clinical intervention -- whether that takes into account the socioeconomic status of the patient, whether they live alone (which poses a greater health risk) or whatever other unknown factors may be affecting a person's health.
And access to a vehicle is important, as those without it are more likely to miss physician appointments, resulting in challenges to access their medical care.