Every week, my team at Invector Labs publishes a newsletter to track the most recent developments in AI research and technology. You can find this week’s issue below. You can sign up for it below. Please do so, our guys worked really hard on this:
From the Editor
Is the enterprise ready for decentralized artificial intelligence(AI)? Most signs say no. Most organizations are just starting in their machine learning journey and there is certain security associated with centralization. However, the idea of decentralized AI in the enterprise might be as crazy as it sounds. Conceptually, the lifecycle of machine learning applications can be considered an intrinsically decentralized workflow. The entities publishing data are not necessary the same training a model or optimizing its hyperparameters. Whereas centralization brings a tighter level of control, it also introduces relevant fragility in enterprise AI systems.
The second aspect that makes decentralized AI relevant in the enterprise is technology alignment. The technology stacks of centralized and decentralized AI models are not as different as people might think and blockchain technologies are maturing rapidly and gaining footprint in the enterprise. The biggest obstacle of decentralized AI in enterprise environments is cultural and not technologically. This week, AI researcher Tarry Singh published an interesting analysis in Forbes about the value of decentralized AI in enterprise environments.
Now let’s take a look at the core developments in AI research and technology this week:
Facebook studied the relationship between how AI agents associated images with conceptual symbols and the results were surprising.
Microsoft Researchers proposed a method to handle chit-chat in natural language conversations
DeepMind researchers published a paper that discusses a method for modeling more effective reward functions in reinforcement learning agents.
Cool Tech Releases
IBM demonstrated Castor, a system for managing large scale predictions of time series data.
Microsoft calls for Project Malmo Competition, a challenge for researchers in multi-agent reinforcement learning applications.
Facebook discusses some of their testing practices that leverage machine learning to select regression tests for a particular code.
AI in the Real World
There is a growing case for exploring decentralized artificial intelligence in enterprise environments.
Researchers at the Massachusetts Institute of Technology (MIT) recently developed a deep learning system that can predict depression accurately.
IBM Watson was able to write a script for a Lexus commercial after being trained using award-winning advertisements as well as emotional- intelligence datasets.