1. The 5 Newest AI
Developments
ROSS PAMPHILON

2. Artificial intelligence is continuing to grow, only the newest
advancements are much more sophisticated and seem better suited for
science fiction movies than real life. Ready or not, the world is turning to
artificial intelligence in a big way, stretching the limits of what our most
educated minds can master and create.
Here, I’ve briefly outlined the 5 newest developments within the AI
community.

3. Listed as one of the top 10 advancements in AI by Forbes, this important
recognition tool will help computer applications be able to communicate.
This technology will transcribe human speech to a form in which any
computer can recognize, allowing the computer to then respond
appropriately. This is currently being used in some sample vendors,
including OpenText.
SPEECH RECOGNITION

4. Speech will play an important role in AI development in the coming years,
but not just with recognition. The ultimate goal of this developing speech
technology will be interaction in language; meaning, we could have
unscripted, free-flowing conversations with our smart tech.
Language Generation

5. In the most current stages of developments, fully optimized AI hardware is
mostly seen in deep learning applications. Companies like Google, Intel, and
IBM are using this tech within appliances specifically designed for AI-
oriented computational jobs, however, according to Forbes, the goal is to
see developments of AI-optimized hardware in the majority of our
applications, from simple uses to complex ones.
AI Hardware

6. This development may not be specifically technology related, but it is an
important development. China’s AI scene is currently booming as they race
to become the first to utilize AI technology in everyday life. It has also been
reported that China has pledged to spend $15 billion in AI industry
development by the year 2018.
China is Booming

7. An inventor by the name of Ian Goodfellow developed incredible technology
set to help computers process decisions and information with the use of
generative adversarial networks, or, GANs. This uses one neural set to
generate and process data, and another separate neural set to determine
whether that data is real or fake. This combination will generate very
realistic synthetic data.
Dual Neural Networks

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