An aerial view of an Amazon Web Services Data Center known as U.S. East 1 in Ashburn, Virginia, on Oct. 20 | REUTERS STOCKHOLM/LONDON - The global boom in data centers as companies increasingly outsource information storage and ramp up use of energy-intensive artificial intelligence is creating a key challenge for the industry -- how to keep cool. An outage at the world's biggest exchange operator CME Group from late Thursday that halted trade on its popular currency platform and in futures spanning foreign exchange, commodities, Treasuries and stocks has put a spotlight on data centers overheating. The problem was a cooling issue at data centers operated by Dallas-headquartered CyrusOne, which operates more than 55 centers in the U.S., Europe and Japan. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

This paper introduces the Large Memory Model (LM2), a decoder-only Transformer architecture enhanced with an auxiliary memory module that aims to address the limitations of standard Transformers in multi-step reasoning, relational argumentation, and synthesizing information distributed over long contexts. The proposed LM2 incorporates a memory module that acts as a contextual representation repository, interacting with input tokens via cross attention and updating through gating mechanisms. To preserve the Transformers general-purpose capabilities, LM2 maintains the original information flow while integrating a complementary memory pathway. Experimental results on the BABILong benchmark demonstrate that the LM2model outperforms both the memory-augmented RMT model by 37.1% and the baseline Llama-3.2 model by 86.3% on average across tasks. LM2 exhibits exceptional capabilities in multi-hop inference, numerical reasoning, and large-context question-answering. On the MMLU dataset, it achieves a 5.0% improvement over a pre-trained vanilla model, demonstrating that its memory module does not degrade performance on general tasks. Further, in our analysis, we explore the memory interpretability, effectiveness of memory modules, and test-time behavior. Our findings emphasize the importance of explicit memory in enhancing Transformer architectures.

The rapid advancement and adoption of generative AI (GenAI) technologies like ChatGPT signify the dawn of "The Age of AI." (Gates, 2023; Kissinger, Schmidt, & Huttenlocher, 2021) These developments mark a significant leap in the capabilities and adoption of AI systems. However, for many people, the swift and disorienting integration of AI into daily life raises many issues (Cugurullo & Acheampong, 2023; Fietta, Zecchinato, Stasi, Polato, & Monaro, 2022; Qasem, 2023). Concerns include the potential impacts on employment, privacy, and inequality, along with broader societal implications like human rights, mental health, and the preservation of democratic norms (Future of Life Institute, 2023; Prabhakaran, Mitchell, Gebru, & Gabriel, 2022; Shahriari & Shahriari, 2017; Stray, 2020). This article argues for the importance of wellbeing as a key objective in AI and for human-centered design (HCD) as a key methodology. Based on this framing, it shares a set of key challenges that will face designers of AI for wellbeing, or Positive AI. The idea that AI should support wellbeing is not uncommon. In 2018, Zuckerberg (2018) (CEO of Meta, previously Facebook) publicly stated that wellbeing should be the goal of AI. Further, in an interview Jan Leike (Wiblin, n.d.) (head of the'Superalignment' research lab at OpenAI) said AI optimization should align to "flourishing."

The European Parliament has taken two years to come up with a definition of an AI system - software that can "for a given set of human-defined objectives, generate outputs such as content, predictions, recommendations or decisions influencing the environments they interact with".

It's a business pain point most know only too well, and new research confirms that integration challenges are not just a pain, they're slowing companies' digital ambitions and causing infrastructure issues and risks. MuleSoft's eighth annual Connectivity Benchmark Report shows the number of applications in Australian organisations (sorry, New Zealand, there are no Kiwi results in this one) have increased nearly 10 percent in the past year, to 1,032, highlighting the complexity of the digital landscape. But 68 percent of those applications are not integrated with other applications used by the business, creating data silos and the flow on effects, including increased costs, duplicated work, productivity bottlenecks and disconnected experiences. It's a situation that's proving costly – not just in terms of money spent building custom integrations (read on for those eye-watering figures) but also in the slowing of digital transformation efforts – something 84 percent of Australians said was happening, causing infrastructure and major risks as IT budgets come under increased scrutiny. And the cost of failing to complete digital transformation initiatives successfully?

One of the main challenges of deploying your model into production is, concept and data drift. Loosely, this means what if your data changes after your system has already been deployed? Let's take two examples of this before defining them specifically to have a better intuition of how this might look in real life. For the first example, assume that you are working at a mobile manufacturing company and you have trained a learning algorithm to detect scratches on smartphones under one set of lighting conditions, and then maybe the lighting in the factory changes. Let's walk through a second example using a speech recognition task.

On June 8, 2022, Accenture presented The Art of A.I. Maturity report. The report revealed that only 12 percent of companies surveyed use A.I. at maturity level, achieving superior growth and business transformation. While A.I. can provide significant benefits for Enterprise organizations across any sector, the potential of the technology is still far from reaching its peak. While multiple problems can trip up your Enterprise AI adoption, there are four key challenges that companies will face as they move into 2023. Understanding these challenges can help organizations build a road map and their A.I. strategies.

Before getting into what polymers are on a molecular level, let's see some familiar materials that are good examples. Some examples of polymers include: plastic, nylon, rubber, wood, protein, and DNA. In this case, we will focus primarily on synthetic polymers like plastic and nylon. At the molecular level, polymers are composed of long chains of repeating molecules. The molecule that repeats in this chain is known as a monomer (or subunit).

While the idea of a robot taking your job may sound like the plot from the latest episode of Black Mirror, a new study has warned that it could become a reality for many people in the future. Researchers from the Ecole Polytechnique Fédérale de Lausanne have revealed which jobs are most and least likely to be taken by robots. Their findings suggest that meat packers, cleaners and builders face the highest risk of being replaced by machines, while teachers, lawyers and physicists are safe. 'The key challenge for society today is how to become resilient against automation,' explained Professor Rafael Lalive, who co-led the study. 'Our work provides detailed career advice for workers who face high risks of automation, which allows them to take on more secure jobs while re-using many of the skills acquired on the old job.' Based on the findings, the researchers have developed a tool (below) that reveals the automation risk of your job, and how you could reuse your abilities.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - August 3. Join AI and data leaders for insightful talks and exciting networking opportunities. By this point, it's become reflexive: When searching for something on Google, Wikipedia is the de facto go-to first page. The website is consistently among the top 10 most-visited websites in the world. Yet, not all changemakers and historical figures are equally represented on the dominant web encyclopedia. Just 20% of Wikipedia biographies are about women.