Do you support sustainability, social responsibility, tech ethics, or trust and safety? In his new self-published Techno-Optimist Manifesto, Andreessen presents his case for the advancement of technology under capitalism as "virtuous" and capable of creating "abundance that lifts all humans". Along the way he champions trickle-down economics (famously effective at increasing inequality), claims technology can solve any problem and suggests that slowing AI development is akin to murder. If you think such proposals sound divorced from reality, you're right. The harms of the state of technology are many: rampant surveillance, consolidation of power, bias and discrimination in automated decision-making systems, worsening power dynamics and labour conditions as a result of automation, and threats to creative workers from generative AI.

The launch of ChatGPT and other generative AI tools has ushered in rapid advances in artificial intelligence and has increased global angst around the impact the technology will have on society. Policymakers are also increasingly concerned about the impact on democracy around the world, especially as the globe enters a critical year for elections.

Instead of building the next GPT or image maker DALL-E, Sutskever tells me his new priority is to figure out how to stop an artificial superintelligence (a hypothetical future technology he sees coming with the foresight of a true believer) from going rogue. Sutskever tells me a lot of other things too. He thinks ChatGPT just might be conscious (if you squint). He thinks the world needs to wake up to the true power of the technology his company and others are racing to create. And he thinks some humans will one day choose to merge with machines.

Google has been accused of censoring Israel-Palestine responses after its AI refused to call Hamas a terrorist organization. But the tech giant's rival, OpenAI's ChatGPT, had no issue condemning the ruling part of Gaza, saying ''Hamas is designated as a terrorist organization by several countries.' It comes as Israel has launched more than 700 airstrikes on Gaza this week in retaliation for Palestine carrying out an unprecedented attack on festival goers on October 7. The same queries were fed to OpenAI's ChatGPT, which returned with detailed information and answered that'Hamas is designated as a terrorist organization by several countries.' A Google spokesperson told DailyMail.com:

With concerns around generative AI ever-present, Google has announced an expansion of its Vulnerability Rewards Program (VRP) focused on AI-specific attacks and opportunities for malice. As such, the company released updated guidelines detailing which discoveries qualify for rewards and which fall out of scope. For example, discovering training data extraction that leaks private, sensitive information falls in scope, but if it only shows public, nonsensitive data, then it wouldn't qualify for a reward. Last year, Google gave security researchers $12 million for bug discoveries. Google explained that AI presents different security issues than their other technology -- such as model manipulation and unfair bias -- requiring new guidance to mirror this.

As the generative artificial intelligence gold rush intensifies, concerns about the data used to train machine learning tools have grown. Artists and writers are fighting for a say in how AI companies use their work, filing lawsuits and publicly agitating against the way these models scrape the internet and incorporate their art without consent. Some companies have responded to this pushback with "opt-out" programs that give people a choice to remove their work from future models. OpenAI, for example, debuted an opt-out feature with its latest version of the image-to-text generator Dall-E. This August, when Meta began allowing people to submit requests to delete personal data from third parties used to train Meta's generative AI models, many artists and journalists interpreted this new process as Meta's very limited version of an opt-out program.

A handful of tech companies are jeopardising humanity's future through unrestrained AI development and must stop their "race to the bottom", according to the scientist behind an influential letter calling for a pause in building powerful systems. Max Tegmark, a professor of physics and AI researcher at the Massachusetts Institute of Technology, said the world was "witnessing a race to the bottom that must be stopped". Tegmark organised an open letter published in April, signed by thousands of tech industry figures including Elon Musk and the Apple co-founder Steve Wozniak, that called for a six-month hiatus on giant AI experiments. "We're witnessing a race to the bottom that must be stopped," Tegmark told the Guardian. "We urgently need AI safety standards, so that this transforms into a race to the top. AI promises many incredible benefits, but the reckless and unchecked development of increasingly powerful systems, with no oversight, puts our economy, our society, and our lives at risk. Regulation is critical to safe innovation, so that a handful of AI corporations don't jeopardise our shared future."

Zuckerberg said that chief among the company's investment priorities in 2024 will be artificial intelligence, where Meta will hire more engineers and build up its computing resources. Last month, the company launched conversational chatbots that allows users to find information and generate images -- a partial attempt to compete with OpenAI's popular ChatGPT amid an industry-wide boom in generative AI. The company also announced this summer that its new Llama 2 "large language model" -- a highly complex algorithm trained on billions of words scraped from the open internet -- will be available for researchers and companies to use freely.

Academic writing is an indispensable yet laborious part of the research enterprise. This Perspective maps out principles and methods for using generative artificial intelligence (AI), specifically large language models (LLMs), to elevate the quality and efficiency of academic writing. We introduce a human-AI collaborative framework that delineates the rationale (why), process (how), and nature (what) of AI engagement in writing. The framework pinpoints both short-term and long-term reasons for engagement and their underlying mechanisms (e.g., cognitive offloading and imaginative stimulation). It reveals the role of AI throughout the writing process, conceptualized through a two-stage model for human-AI collaborative writing, and the nature of AI assistance in writing, represented through a model of writing-assistance types and levels. Building on this framework, we describe effective prompting techniques for incorporating AI into the writing routine (outlining, drafting, and editing) as well as strategies for maintaining rigorous scholarship, adhering to varied journal policies, and avoiding overreliance on AI. Ultimately, the prudent integration of AI into academic writing can ease the communication burden, empower authors, accelerate discovery, and promote diversity in science.

Recent advances in machine learning have made revolutionary breakthroughs in computer games, image and natural language understanding, and scientific discovery. Foundation models and large-scale language models (LLMs) have recently achieved human-like intelligence thanks to BigData. With the help of self-supervised learning (SSL) and transfer learning, these models may potentially reshape the landscapes of neuroscience research and make a significant impact on the future. Here we present a mini-review on recent advances in foundation models and generative AI models as well as their applications in neuroscience, including natural language and speech, semantic memory, brain-machine interfaces (BMIs), and data augmentation. We argue that this paradigm-shift framework will open new avenues for many neuroscience research directions and discuss the accompanying challenges and opportunities.