Large language model (LLM) AI chatbots may be able to outperform the average human at a creative thinking task where the participant devises alternative uses for everyday objects (an example of divergent thinking), suggests a study published in Scientific Reports. However, the human participants with the highest scores still outperformed the best chatbot responses.

Divergent thinking is a type of thought process commonly associated with creativity that involves generating many different ideas or solutions for a given task. It is commonly assessed with the Alternate Uses Task (AUT), in which participants are asked to come up with as many alternative uses for an everyday object as possible within a short time period. The responses are scored for four different categories: fluency, flexibility, originality, and elaboration.

Mika Koivisto and Simone Grassini compared 256 human participants’ responses with those of three AI chatbots’ (ChatGPT3, ChatGPT4, and Copy. Ai) to AUTs for four objects—a rope, a box, a pencil, and a candle. The authors assessed the originality of the responses by rating them on semantic distance (how closely related the response was to the object’s original use) and creativity.

Large language models are getting better at mimicking human creativity. That doesn’t mean they’re actually being creative, though.

AI is getting better at passing tests designed to measure human creativity. In a study published in Nature Scientific Reports today, AI chatbots achieved higher average scores than humans in the Alternate Uses Task, a test commonly used to assess this ability.

This study will add fuel to an ongoing debate among AI researchers about what it even means for a computer to pass tests devised for humans. The findings do not necessarily indicate that AIs are developing an ability to do something uniquely human. It could just be that AIs can pass creativity tests, not that… More.

Among the ideas discussed was whether there should be an independent agency to oversee certain aspects of the rapidly-developing technology, how companies could be more transparent and how the United States can stay ahead of China and other countries.

“The key point was really that it’s important for us to have a referee,” said Elon Musk, CEO of Tesla and X, during a break in the daylong forum. “It was a very civilized discussion, actually, among some of the smartest people in the world.”

Schumer will not necessarily take the tech executives’ advice as he works with colleagues on the politically difficult task of ensuring some oversight of the burgeoning sector. But he invited them to the meeting in hopes that they would give senators some realistic direction for meaningful regulation.

The versatile robot will help create a virtual representation of the facilities.

In a first for the UK’s National Trust, Boston Dynamics’ robotic dog Spot is being used to survey two Cold War weapons testing sites located in Orford Ness, Suffolk.

This is according to a report by the *BBC* published on Thursday.

Spot, a versatile quadruped robot, has drawn a lot of interest as well as clients in recent years for its cutting-edge capabilities and its many potential uses.

## Efficiency and speed.

The machine has been designed to tackle a variety of tasks with efficiency and speed. Its lightweight and agile design allow it to navigate rough terrains, climb stairs, and function effectively both indoors and outdoors.

Spot can change its speed and posture to adjust to different environments, can execute dynamic movements and can trot, walk, and crawl.

“Here is how Generative AI can help in the Overall Generative AI in Software Development Life Cycle (SDLC) stages. Overall, we want to treat Generative AI as senior developer/architect with more accessibility.

- Requirements gathering: ChatGPT can significantly simplify the requirements gathering phase by building quick prototypes of complex applications. It also can minimize the risks of miscommunication in the process since the analyst and customer can align on the prototype before proceeding to the build phase.

- Design: DALL-E, another deep learning model developed by OpenAI to generate digital images from natural language descriptions, can contribute to the design of applications. In addition to providing user interface (UI) templates for common use cases, it also may eventually be deployed to ensure that the design of a given application meets regulatory criteria such as accessibility.

- Build: ChatGPT has the capability to generate code in different languages. It could be used to supplement developers by writing small components of code, thus enhancing the productivity of developers and software quality. It even can enable citizen developers to write code without the knowledge of programming language.

- Test: ChatGPT has a major role in the testing phase. It can be used to generate various test cases and to test the application just by giving prompts in natural language. It can be leveraged to fix any vulnerabilities that could be identified through processes such as Dynamic Code Analysis (DCA) and perform chaos testing to simulate worst-case scenarios to test the integrity of the application in a faster and cost-effective way.

- Maintenance: ChatGPT can significantly improve First Contact Resolution (FCR) by helping clients with basic queries. In the process, it ensures that issue resolution times are significantly reduced while also freeing up service personnel to focus their attention selectively on more complex cases.

https://medium.com/mlearning-ai/generative-ai-in-software-de…90e466eb91

Sony Semiconductor Solutions Corporation (SSS) has developed an energy harvesting module that uses electromagnetic wave noise energy to power IoT devices.

The new module leverages Sony’s tuner development process to generate power from electromagnetic wave noise from robots inside factories, monitors and lighting in offices, monitors and TVs in stores and homes, etc. in order to provide a stable power supply needed to run low-power IoT sensors and communications equipment.