What's Trending in AI and Big Data

Chabots and Virtual Assistants
The history of language processing: The 1950 test to determine if a particular computer is intelligent by
asking an ordinary user to determine if a conversational partner is human or machine. This famous test
was initially passed in 1966 by ELIZA software, though it had nothing to do with natural language
processing (NLP)

Still hardly perfect, NLP has gained a reputation embracing chatbots. According to Deloitte, 64% of
smartphone owners say they use their virtual assistant (Apple Siri, Google’s Assistant) compared to 53%
in 2017. Gartner has found that up to 25% of companies will have integrated a virtual customer assistant
or a chatbot into their customer service by 2020. That’s up from less than 2% in 2017.

Reducing Training Time
Expert augmented learning is one of most interesting ways to reduce the effort required to build
reinforcement-based models. Another way to reduce the time needed to train a model is to optimize
the hardware infrastructure required. Google Cloud Platform has presented a cloud based tailored
environment for building machine learning models without investing in on-site infrastructure. With the
recently developed GPipe infrastructure, Google has been able to impressively boost performance of
Generative Adversarial Networks on existing infrastructure. By using GPipe, researchers were able to
elevate performance to a new state-of-the-art status.

Rising Speed of Autonomous Vehicles
Machines can drive for hours without losing concentration. Road freight is globally the largest producer
of emissions and consumes more than 70% of all energy used for freight. Every optimization for fuel
usage and routes will improve energy and time management. Volvo has recently introduced Vera, the
driverless track aimed at short-haul transportation in logistics centers and ports. Its fleet of cars is able
to provide a constant logistics stream of goods with fractious human involvement. Other companies as
Krogers and Uber testing autonomous vehicles on the roads of real towns.

Democratizing Machine Learning and AI
Due to the popularization of big data, artificial intelligence and machine learning, the demand for data
science professionals continues to rise. According to O’Reilly data, 51% of surveyed organizations
already use data science teams to develop AI solutions for internal purposes.

AI Transparency
Ss the impact of machine learning on business grows, so does the social and legal impact transparency.
If a car is autonomous, and controlled by a virtual agent, all choices are made by a neural network, that
could question some morality. While machine learning-applications are receiving more attention as
supporting medical treatment. Medical data is expected to rise at an annual growth rate of 36%.
standardization within diagnostic data makes medical data ripe for utilizing machine learning models,
that can be augmented to support the treatment process.

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