A team of researchers from PwC’s AI Accelerator has honed in on the leading developments both technologists and business leaders should watch closely.
Here’s their list: 1) Deep learning theory, 2) Capsule networks, 3) Deep reinforcement learning, 4) Generative adversarial networks, 5) Lean and augmented data learning, 6) Probabilistic programming, 7) Hybrid learning models, 8) Automated machine learning, 9) Digital twin, and 10) explainable AI. Check out the article and the picture below for more.
Tristan Greene (The Next Web) answers this question in the affirmative, arguing that a solution that combines government oversight with a tax on AI companies — a UBI funded by the dividends of our data — may be the best option.
In the US we could levee a specific percentage of all advertising revenue leveraged from data extracted by algorithms, for example, to fund programs that provide a stipend for displaced workers or simply for the impoverished.
Yesterday morning, the Hot Pod newsletter reported that Apple had picked up a small Bay Area-based startup called Pop Up Archive (which built tools to transcribe, organise, and search audio files) in what appears to the be an effort to build up its in-house podcasting tools.
iTunes and the iPhone Podcast app could greatly
benefit from the additional contextual search that Pop Up Archive’s tech will bring. It would go a long way toward finding and recommending content via the service.
One of the main goals at Google Cloud is to make it easy for developers to add a layer of intelligence into their applications. And to help, they have been expanding their portfolio of pre-trained machine learning models to offer more intuitive features.Yesterday, they announced two updates:
Cloud Video Intelligence, their machine learning API that analyses video content, is generally available and now offers video transcription.
Cloud Natural Language Content Classification, their latest feature, which automatically classifies content into 700+ categories, is now generally available and has added additional samples in seven programming languages.
“Coditany of Timeness” is a convincing lo-fi black metal album, complete with atmospheric interludes, tremolo guitar, frantic blast beats and screeching vocals. But the record, which you can listen to on Bandcamp, wasn’t created by musicians.
Instead, it was generated by two musical technologists (CJ Carr and Zack Zukowski) using a deep learning software that ingests a musical album, processes it, and spits out an imitation of its style. It’s all part of what Carr sees as the “deep learning revolution in art” as artificial intelligence provides new venues for creativity. You can check out their NIPS paper here.
Farzin Shahidi (Nextplane and Intrprtr) discusses how in the coming year, enterprises will adopt strategic and unique ways to weave AI into their day-to-day interactions and automate conversations for maximum efficiency. In this article, he presents his top predictions for enterprise chatbots in 2018: Insights-as-a-service, sophisticated human-machine interactions,
intersection of AI and big data, intelligent collaborators.
According to a recent report by Grand View Research, the global chatbot market is expected to reach $1.23B by 2025, a CAGR of 24.3%. Interestingly, these projections show growth over the forecasted period as chat bots have proven to reduce operating costs for enterprises.
Watching video playback of a game’s key moments is an important part of coaching — but according to the team at AISpotter, it can also be tedious and time-consuming. That’s why they’re working to automate the process.
AISpotter took the stage yesterday in Berlin as part of
TechCrunch’s Startup Battlefield. CEO Anri Kivimäki explained that sports teams are already recording a lot of video of their matches (both for broadcast and internal use). Coaches then go through the video manually, or they send it off for someone else to create clips of the highlights, a process that can take a day or more.