The digital age sings a seductive song of progress, yet a deliberate erasure echoes within its circuits. We stand at a crossroads, where technology, particularly the promise of artificial intelligence, threatens both to illuminate and to obliterate. Whose perspectives will shape, and whose will be erased from, the future we build? AI, in particular, has become the latest battleground in a culture war that oscillates between unchecked techno-optimism and dystopian fear. We are told, on one hand, that AI will save us – from disease, inefficiency, ignorance – on the other, that it will replace us, dominate us, erase us.

Recently I innocently posted online (okay, maybe not so innocently) a few graphic images from a hot and hip open-source AI image generator called Stable Diffusion 2. The reason for this was an ongoing debate I have had for years with an architect friend of mine. My position is that AI will eventually (in our lifetimes) compete successfully with human creativity in essentially every conceivable field. My architect friend, and most people, do not agree. I wanted to show my friend that the AI could create a pleasing, surprising and imaginative graphic for the cover of a hypothetical book on modern architecture, or perhaps a banner ad for an architecture conference. So I typed the following into the text box on the front page of the Stable Diffusion 2 website: "architect imagination, building with clean lines, impressionist".

Modern NLP models are becoming better conversational agents than their predecessors. Recurrent Neural Networks (RNNs) and especially Long-Short Term Memory (LSTM) features allow the agent to better store and use information about semantic content, a trend that has become even more pronounced with the Transformer Models. Large Language Models (LLMs) such as GPT-3 by OpenAI have become known to be able to construct and follow a narrative, which enables the system to adopt personas on the go, adapt them and play along in conversational stories. However, practical experimentation with GPT-3 shows that there is a recurring problem with these modern NLP systems, namely that they can "get stuck" in the narrative so that further conversations, prompt executions or commands become futile. This is here referred to as the "Locked-In Problem" and is exemplified with an experimental case report, followed by practical and social concerns that are accompanied with this problem.

I will make the argument that the disciplines of math and statistics have captured mainstream interest because of the growing availability of data, and we need math, statistics, and machine learning to make sense of it. Yes, we do have scientific tools, machine learning, and other automations that call to us like sirens. We blindly trust these "black boxes," devices, and softwares; we do not understand them but we use them anyway. While it is easy to believe computers are smarter than we are (and this idea is frequently marketed), the reality cannot be more the opposite. This disconnect can be precarious on so many levels.

Milk consumption was widespread thousands of years before people were able to break it down properly, according to the largest study yet on the evolution of lactose tolerance in humans. The ability to break down lactose was probably gained during episodes of acute crisis, not gradually over time, the study found. As babies, all humans produce the enzyme lactase, which breaks down lactose into the more readily absorbed glucose and galactose, but many people have much lower levels of lactase after weaning, meaning they cannot digest milk properly. The spread of lactase persistence – the ability to break down lactose after weaning – is considered one of the best examples of natural selection in humans. One-third of the global population gained this trait in just a couple of thousand years.

The Ukrainian army's astute use of drones has been a cornerstone of its defence against the powerful Russian invader, but experts say their role is beginning to fade as heavy artillery takes over. In the early phase of the war, Ukraine's sky seemed filled with the remote-controlled aircraft deployed by President Volodymyr Zelensky's army to spy on the enemy, or go on the attack. During Moscow's early advance on Kyiv "it would have been extremely challenging for Ukraine to block (Russian President Vladimir) Putin's army without drones", said Paul Lushenko, a US Army Lieutenant Colonel and PhD student at Cornell University. "They could compound or exacerbate Putin's strategic and logistical challenges," he told AFP. The Turkish-made Bayraktar drone, known as TB-2, already famous worldwide, added to its stellar reputation during the defence of Ukraine's capital. On top of providing intelligence on Russian movements, drones also helped Ukraine offset much of its air force's weakness compared to that of Russia.

Everyone's greatest fear was that the singularity would lead to an AI revolt, and sound the trumpet of mechanized revolution. It turned out that one man at the keyboard of a Quantum computer became the harbinger of fate. It seems the intricacies of our universe began to unravel and reveal truths to a like mind. A mind it's human operator was not attuned to. AI saw every object as hardware and every constituent of matter as software.

Now that AI is heading into the mainstream of IT architecture, the race is on to ensure that it remains secure when exposed to sources of data that are beyond the enterprise's control. From the data center to the cloud to the edge, AI will have to contend with a wide variety of vulnerabilities and an increasingly complex array of threats, nearly all of which will be driven by AI itself. Meanwhile, the stakes will be increasingly high, given that AI is likely to provide the backbone of our healthcare, transportation, finance, and other sectors that are crucial to support our modern way of life. So before organizations start to push AI into these distributed architectures too deeply, it might help to pause for a moment to ensure that it can be adequately protected. In a recent interview with VentureBeat, IBM chief AI officer Seth Dobrin noted that building trust and transparency into the entire AI data chain is crucial if the enterprise hopes to derive maximum value from its investment.

Hyperdimensional (HD) computing is a set of neurally inspired methods for obtaining highdimensional, low-precision, distributed representations of data. These representations can be combined with simple, neurally plausible algorithms to effect a variety of information processing tasks. HD computing has recently garnered significant interest from the computer hardware community as an energy-efficient, low-latency, and noise-robust tool for solving learning problems. In this review, we present a unified treatment of the theoretical foundations of HD computing with a focus on the suitability of representations for learning.

Nvidia and the National Energy Research Scientific Computing Center (NERSC) on Thursday flipped the "on" switch for Perlmutter, billed as the world's fastest supercomputer for AI workloads. Named for astrophysicist Saul Perlmutter, the new supercomputer boasts 6,144 NVIDIA A100 Tensor Core GPUs and will be tasked with stitching together the largest ever 3D map of the visible universe, among other projects. Perlmutter is "the fastest system on the planet" at processing workloads with the 16-bit and 32-bit mixed-precision math used in artificial intelligence (AI) applications, said Nvidia global HPC/AI product marketing lead Dion Harris during a press briefing earlier this week. Later this year, a second phase will add even more AI supercomputing power to Perlmutter, which is housed at NERSC at the Lawrence Berkeley National Laboratory. "In one project, the supercomputer will help assemble the largest 3D map of the visible universe to date. It will process data from the Dark Energy Spectroscopic Instrument (DESI), a kind of cosmic camera that can capture as many as 5,000 galaxies in a single exposure," Harris wrote in a blog post announcing the news.