Join journalist Pedro Pinto and Yuval Noah Harari as they delve into the future of artificial intelligence (A.I.). Together, they explore pressing questions in front of a live audience, such as: What will be the impact of A.I. on democracy and politics? How can we maintain human connection in the age of A.I.? What skills will be crucial for the future? And what does the future of education hold?

Filmed on May 19 2023 in Lisbon, Portugal and produced by the Fundação Francisco Manuel dos Santos (FFMS), in what marks the first live recording of the show: “It’s not that simple.”

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[Prof. Marvin Minsky] is a very well-known figure in the field of computing, having co-founded the MIT AI lab, published extensively on AI and computational intelligence, and, let’s not forget, inventing the confocal microscope and, of course, the useless machine. But did you know he also was a co-developer of the first Logo “turtle,” and developed a computer intended to run Logo applications in an educational environment? After dredging some PDP-10 tapes owned by the MIT Media Lab, the original schematics for his machine, the Turtle Terminal TT2500 (a reference to the target price of $2500, in 1970 terms), are now available for you to examine.

The machine itself was created in an interesting way; by affixing discrete socketed TTL chips to a large panel, some three hundred or so, the interconnect was performed automatically using a computer-controlled wiring machine that read the design from magnetic tape. The 2,500 used 16-bit user-definable instructions read from a tiny 4k control store. Instruction microcode was read from a 1k microcode store backed up with 64k of RAM. Unusually, it sported a dual display configuration, with one text display and a second vector display for rendering real-time graphics. The machine was intended to run the Logo programming language developed by [Seymour Papert] and others, but this was impossible due to its tiny control store. Instead, it became a display terminal for a connected computer with sufficient resources. You can read more about this fascinating period of time in AI, the life of [Minsky], and others in this New Yorker article.

[Lars Brinkhoff] has created a simulation of the TT2500 running atop a PDP11/45 emulator, a demo of which can be seen below. What a fun story! We covered the passing of the great man back in 2016, which is well worth another read, we reckon. If you want to relive the useless machine, we’ve seen them ranging from the simple to the complex.

Qualcomm Technologies, Inc. unveiled the Qualcomm Video Collaboration Platform, a new suite of video collaboration solutions that allows original equipment manufacturers (OEMs) to easily design and deploy video conferencing products featuring superior video, audio and customizable on-device AI to power engaging, immersive virtual meeting experiences across enterprise, healthcare, educational, and home environments. The Qualcomm® Video Collaboration Platform is a one-stop solution that provides essential hardware and software features specifically tailored for video conferencing so that customers can quickly design and deploy a wide variety of video conferencing products, from enterprise video collaboration systems and huddle room systems to digital whiteboards, to touch controllers and personal devices for the home.

With support for Android and Linux, the three AI-rich platforms offer greater flexibility and ability to customize and deploy video conferencing products across diverse environments. Qualcomm Technologies’ industry-leading innovations in connectivity, compute, AI, audio, and video work together to deliver features that eliminate distractions, enhance productivity, and allow remote meeting callers to feel more connected to conference room participants by providing individual views of everybody in the room, creating an equal viewing experience for all participants.

With the rapid advances in generative AI, future meeting experiences will offer even more advanced video, speech, and text capabilities. Collaboration devices with dedicated hardware support for on-chip AI acceleration will be able to optimize these experiences by splitting workloads between the cloud and edge-based device.