A key question in artificial intelligence is how often models go beyond just regurgitating and remixing what they have learned and produce truly novel ideas or insights. A new project from Google DeepMind shows that with a few clever tweaks these models can at least surpass human expertise designing certain types of algorithms--including ones that are useful for advancing AI itself. The company's latest AI project, called AlphaEvolve, combines the coding skills of its Gemini AI model with a method for testing the effectiveness of new algorithms and an evolutionary method for producing new designs. AlphaEvolve came up with more efficient algorithms for several kinds of computation, including a method for calculations involving matrices that betters an approach called the Strassen algorithm that has been relied upon for 56 years. The new approach improves the computational efficiency by reducing the number of calculations required to produce a result.

The fast-moving, dynamic world of artificial intelligence (AI) stands in stark contrast to the slow-moving, conservative world of academia.11 This is particularly clear in the world of AI ethics, where in addition to the industry-academia contrast we also have the meeting of very different academic disciplines, including computer science, philosophy, ethics, and social sciences. The traditions, norms, and values of these disciplines are often at odds with one another, making interdisciplinarity challenging. Take, for example, preprinting, the practice of quickly disseminating research before potentially--but not necessarily--seeking publication in traditional academic journals.a Interdisciplinary conflicts appear when, for example, researchers from a computer science background, where rapid publication of preprints on servers such as arXiv is the norm,2 meet researchers from the social sciences and humanities, where this is less common.1,30

This paper presents a novel conversational AI-enabled active ideation interface as a creative idea-generation tool to assist novice designers in mitigating the initial latency and ideation bottlenecks that are commonly observed. It is a dynamic, interactive, and contextually responsive approach, actively involving a large language model (LLM) from the domain of natural language processing (NLP) in artificial intelligence (AI) to produce multiple statements of potential ideas for different design problems. Integrating such AI models with ideation creates what we refer to as an Active Ideation scenario, which helps foster continuous dialogue-based interaction, context-sensitive conversation, and prolific idea generation. A pilot study was conducted with thirty novice designers to generate ideas for given problems using traditional methods and the new CAI-based interface. The key parameters of fluency, novelty, and variety were used to compare the outcomes qualitatively by a panel of experts. The findings demonstrated the effectiveness of the proposed tool for generating prolific, diverse and novel ideas. The interface was enhanced by incorporating a prompt-engineered structured dialogue style for each ideation stage to make it uniform and more convenient for the designers. The resulting responses of such a structured CAI interface were found to be more succinct and aligned towards the subsequent design stage, namely conceptualization. The paper thus established the rich potential of using Generative AI (Gen-AI) for the early ill-structured phase of the creative product design process.

What would you think about a mask that allows you to enjoy the synthesized smells of fine cuisine? How about a floating house for people displaced by higher sea levels due to climate change? These are among the ideas that Sony Group Corp. have dreamed up -- all based on stories created through a collaboration between science fiction writers and Sony's young in-house designers. Such stories offer a peek into the future envisioned through an author's bold imagination, with details of the imagined futures used to develop prospective products and services. This method is called sci-fi prototyping. While the COVID-19 pandemic has presented scenes like those from a sci-fi movie, such as eerily empty cities due to lockdowns, Japanese companies such as Sony are starting to explore unconventional methods to come up with novel ideas.

Malicious cyber activity is ubiquitous and its harmful effects have dramatic and often irreversible impacts on society. Given the shortage of cybersecurity professionals, the ever-evolving adversary, the massive amounts of data which could contain evidence of an attack, and the speed at which defensive actions must be taken, innovations which enable autonomy in cybersecurity must continue to expand, in order to move away from a reactive defense posture and towards a more proactive one. The challenges in this space are quite different from those associated with applying AI in other domains such as computer vision. The environment suffers from an incredibly high degree of uncertainty, stemming from the intractability of ingesting all the available data, as well as the possibility that malicious actors are manipulating the data. Another unique challenge in this space is the dynamism of the adversary causes the indicators of compromise to change frequently and without warning. In spite of these challenges, machine learning has been applied to this domain and has achieved some success in the realm of detection. While this aspect of the problem is far from solved, a growing part of the commercial sector is providing ML-enhanced capabilities as a service. Many of these entities also provide platforms which facilitate the deployment of these automated solutions. Academic research in this space is growing and continues to influence current solutions, as well as strengthen foundational knowledge which will make autonomous agents in this space a possibility.

Encouraging children to use gestures can boost their creativity, reveals a new study. What the polar vortex looks like from space: NASA satellite... Incredible video shows how just one tablespoon of olive oil... Earthquake-detecting app MyShake recorded nearly 400 quakes... Are algorithms controlling humans? What the polar vortex looks like from space: NASA satellite... Incredible video shows how just one tablespoon of olive oil... Earthquake-detecting app MyShake recorded nearly 400 quakes... Are algorithms controlling humans? The team found that Children who gestured normally produced 13 gestures, on average, while those who were specifically prompted to gesture produced about 53 gestures, on average. Caught on camera: Checkout line fight erupts over couponing Drag race ends in Lamborghini crashing into other cars Angry motorist challenges traffic warden over'illegal parking' Couponing mom attacked inside store for holding up the line'Scumbag unions': Chants outside Brighton rail station Amir Khan's wife Faryal Makhdoom snapchats an'apology' Real-life Amazon Drone delivery begin trials with no human pilot Feliks Zemdegs breaks Rubik's cube speed-solving world record'We talked about life': Trump and Kanye discuss surprise meet Watch woman get dragged off jet by police in Detroit Impressive fireball lights up Spain's Costa del Sol night sky'We talked about life': Trump and Kanye discuss surprise meet Growing Pains star Alan Thicke, 69, dies suddenly after... Moment an extreme couponer is attacked by a furious customer... IBM to hire 25,000 more workers in the US in the next four... Kanye 2024: Rapper makes VIP trip to Trump Tower to meet... 'I can't feel anything Meg.

A law professor at the University of Surrey is arguing that it should be possible for computer-based artificial intelligence (AI) systems to be formally considered as inventors for any invention they contribute to, much in the same way a person would. The argument forms part of a paper, which has been published in the Boston College Law Review, entitled I Think, Therefore I Invent: Creative Computers and the Future of Patent Law. In its introduction the report makes the point that while inventions by computers have been granted patents previously, the concept of computer inventorship has never actually been considered by the courts. The concept of giving creative computers the credit for their own inventions may sound surreal but, in reality, they have been generating potentially patentable ideas for decades without acknowledgment. As Professor Ryan Abbott points out in his paper, 'machines have been autonomously generating patentable results for at least twenty years and the pace of such invention is likely increasing.'