Artificial intelligence is one of the fastest moving and least predictable industries.
But it never hurts to try our chances at predicting the future of AI.
Heres what you oughta know.
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These are areas where AI could really start to make a difference, Tas told TNW.
Philips is a key player in the development of necessary AI-enabled apps seamlessly being integrated into existing healthcare workflows.
There has certainly been a growing focus on AI ethics in 2019.
Early in the year, the European Commission published a set ofseven guidelines for developing ethical AI.
In 2020, enterprises will pay closer attention to AI trust whether theyre ready to or not.
Take the automotive industry, for example.
However, its difficult, expensive, and time-consuming to collect real-world driver data.
However [data synthesis] can augment those data sets.
The creation and detection of deepfakes has already become acat-and-mouse chase.
As AI enters more and more fields, new issues and concerns will arise.
In 2018, she helped develop RISE, a method forscrutinizing the decisions made by computer vision algorithms.
Automating AI has become a growing area of research and development in the past few years.
An example is theNeurosymbolic Concept Learner, a hybrid AI model developed by researchers at IBM and MIT.
For the manufacturing industry, one of the biggest challenges is quality control.
Product managers are struggling to inspect each individual product and component while also meeting deadlines for massive orders.
Manufacturers will move towards the edge, Versace adds.
New routes to training AI that can be deployed and refined at the edge will become more prevalent.
Already, governments are investing heavily in AI as a possible next competitive front.
Manaktala added, But experts urge more investment, warning that the U.S. is still behind.
Instead, highly accurate analysis driven by AI can lead to radically faster drug discoveries.
The costs of developing new drugs can easily exceed $1 billion.
But theres hope thatAI algorithms can speed up the processof experimentation and data gathering in drug discovery.
Additionally, cell counting is a massive problem in biological researchnot just in drug discovery.
People are hunched over microscopes or sitting in front of screens with clickers in their hands counting cells.
There are expensive machines that attempt to count, inaccurately.
