Lifeboat Foundation

Alphabet Inc is combining Google Brain and DeepMind, as it doubles down on artificial intelligence research in its race to compete with rival systems like OpenAI’s ChatGPT chatbot.

The new division will be led by DeepMind CEO Demis Hassabis and its setting up will ensure “bold and responsible development of general AI,” Alphabet CEO Sundar Pichai said in a blog post on Thursday (20 April).

Continue reading “Google merges Brain and DeepMind units to accelerate AI research” »

The first 2 minutes includes the best layman description of how ChapGPT works that I’ve heard yet:

Ready to blast off into a new world of gaming? In this exciting video, we’re taking AI to the next level as we install ChatGPT as a co-pilot in my SimPit game station. But this isn’t just your average AI installation — get ready for a hilarious space adventure as we explore the ups and downs of integrating ChatGPT into our gaming setup.

Continue reading “I put an AI in my Spaceship Gaming Setup?!? You can too!!! (Chat GPT)” »

Jeremy Hunt tells POLITICO the West needs to be ‘super smart’ about artificial intelligence rules, but pushes back at doom-laden warnings.

As Google looks to maintain pace in AI with the rest of the tech giants, it’s consolidating its AI research divisions.

Today Google announced Google DeepMind, a new unit made up of the DeepMind team and the Google Brain team from Google Research. In a blog post, DeepMind co-founder and CEO Demis Hassabis said that Google DeepMind will work “in close collaboration… cross the Google product areas” to “deliver AI research and products.”

As a part of Google DeepMind’s formation, Google says that it’ll create a new scientific board to oversee research progress and the direction of the unit, which will be led by Koray Kavukcuoglu, VP of research at DeepMind. Eli Collins, VP of product at Google Research, will join Google DeepMind as VP of product, while Google Brain lead Zoubin Ghahramani will become a member of the Google DeepMind research leadership team, reporting to Kavukcuoglu.

Not only does generative AI threaten originality in art, as some argue, it undeniably makes who gets the credit for intellectual property a lot murkier.

Autism spectrum disorder (ASD) is a developmental disorder associated with difficulties in interacting with others, repetitive behaviors, restricted interests and other symptoms that can impact academic or professional performance. People diagnosed with ASD can present varying symptoms that differ in both their behavioral manifestations and intensity.

As a result, some autistic individuals often require far more support than others to complete their studies, learn new skills and lead a fulfilling life. Neuroscientists have been investigating the high variability of ASD for several decades, with the hope that this will aid the development of more effective therapeutic strategies tailored around the unique experiences of different patients.

Researchers at Weill Cornell Medicine have recently used machine learning to investigate the molecular and neural mechanisms that could underlie these differences among individuals diagnosed with ASD. Their paper, published in Nature Neuroscience, identifies different subgroups of ASD associated with distinct functional connections in the brain and symptomatology, which could be related to the expression of different ASD-related genes.

People are outsourcing home-based tasks like meal prep and bedtime stories to artificial intelligence, too.

Synthesis AI, a startup that specializes in synthetic data technologies, announced that they have developed a new technology for digital human creation, which enables one to create highly realistic 3D digital humans from text prompts using generative AI and VFX pipelines.

The technology showcased by Synthesis AI allows users to input specific text descriptions such as age, gender, ethnicity, hairstyle, and clothing to generate a 3D model that matches the specifications. Users can also edit the 3D model by changing text prompts or using sliders to adjust features like facial expressions and lighting.

The earliest artificial neural network, the Perceptron, was introduced approximately 65 years ago and consisted of just one layer. However, to address solutions for more complex classification tasks, more advanced neural network architectures consisting of numerous feedforward (consecutive) layers were later introduced. This is the essential component of the current implementation of deep learning algorithms. It improves the performance of analytical and physical tasks without human intervention, and lies behind everyday automation products such as the emerging technologies for self-driving cars and autonomous chat bots.

The key question driving new research published today in Scientific Reports is whether efficient learning of non-trivial classification tasks can be achieved using brain-inspired shallow feedforward networks, while potentially requiring less computational complexity.

Continue reading “Is deep learning a necessary ingredient for artificial intelligence?” »