This article details recent news about Google merging their Google Research and Google.ai divisions based on their May 7th announcement in preparation for Google I/O 2018.
It seems like every company wants “in” on the artificial intelligence and machine learning market. But, Google’s merger of Google.ai and Google Research portends something bigger.
This also comes on the heels of IBM’s launch of ART (the Adversarial Robustness Toolbox).
What are Google’s reasons for unifying their AI divisions?
Machine Learning Combines With Research Tools
Google sees AI as the future of consumer technology. In fact, Google CEO Sundar Pichai did state that Google is an “AI-first” company on several occasions. You can watch one of them in the above video.
In an effort to support this claim, Google wanted to add support for machine learning frameworks such as Tensor Flow. However, the new AI division encompasses non-AI research, as well.
Due to their previous interconnectivity, this merger won’t change much from Google’s current processes. But it does better serve the new “AI-first” mentality. This also supports other efforts Google initiated regarding the incorporation of AI.
You can see some of the new teams in the AI division like natural language understanding. Moving forward, that may be a key factor in developing more human-like AI.
Pro-tip: watch for some exclusive Edgy Labs coverage on natural language processing later this week about company Inbenta.
Concerns Over Vulnerabilities and Ethics
Despite the blog post from yesterday, not everyone seems to speak the praises of AI.
Google co-founder Sergey Brin expressed concerns about the threats of this “technological renaissance” AI sparked. Brin mentioned worries about vulnerabilities presented in AI tech, asking for caution moving forward.
But the new AI division hopes to ameliorate any concerns and push the AI needle ever-forward.
They hope to leverage all of their tools for state-of-the-art research initiatives like:
Google focuses on the possibilities of AI, but Microsoft cares more about ethics it seems.
Their FATE (Fairness, Accountability, Transparency, and Ethics in AI) program also seeks to harness the power of machine learning, AI, and data science. But they raise concerns over discrimination, prejudice, and other things AI develop as a result of machine learning.
As we pointed out in February, manners might become more important as machine learning ubiquity and abilities increase.