"Cognitive science is the interdisciplinary study of mind and intelligence, embracing philosophy, psychology, artificial intelligence, neuroscience, linguistics, and anthropology. Its intellectual origins are in the mid-1950s when researchers in several fields began to develop theories of mind based on complex representations and computational procedures."
– Paul Thagard. Cognitive Science , in The Stanford Encyclopedia of Philosophy.
When you work with IT staff and data scientists, they're going to use acronyms that you might not be familiar with. It's important to know some of the basic terms and acronyms so you can communicate. Business users should make themselves familiar with these common AI terms to communicate well with the data teams. Artificial intelligence is a form of intelligence demonstrated by a computer. A computer can be programmed with logic and business rules that will enable it to "reason" through situations and come up with a conclusion.
In a time when the pace of change is accelerating, the presence of creative excellence for businesses is crucial for success. However, it is easier said than done. Creative excellence with humans alone has its setbacks, preventing it from reaching its full potential. That's where artificial intelligence comes in. AI is an extraordinary force for creative excellence.
There currently exists no machine capable of superhuman feats of intelligence. I'm sorry to be the one who bursts your bubble but, at least for the time being, that's the way the universe works. It might seem like AI is smarter than humans in some ways. For example, the powerful neural networks used by big tech can sort through millions of files in a matter of seconds, a feat that would take humans more than a single lifetime. On a file per file basis, there is no attention-based task a machine can beat a human at barring chance.
Interspecies was once a technical term used in science to describe how one species got along with another. Now it is a word of more consequence: it evokes the new connections between humans and non-humans that are being made possible by technology. Whether it is satellite footage tracking geese at continental scale, or a smartphone video of squirrels in a park, people are seeing the 8.7m other species on the planet in new lights. In "Ways of Being", James Bridle, a British artist and technology writer, explores what this means for understanding the many non-human intelligences on Earth. Your browser does not support the audio element.
Inside the womb, fetuses can begin to hear some sounds around 20 weeks of gestation. However, the input they are exposed to is limited to low-frequency sounds because of the muffling effect of the amniotic fluid and surrounding tissues. A new MIT-led study suggests that this degraded sensory input is beneficial, and perhaps necessary, for auditory development. Using simple computer models of the human auditory processing, the researchers showed that initially limiting input to low-frequency sounds as the models learned to perform certain tasks actually improved their performance. Along with an earlier study from the same team, which showed that early exposure to blurry faces improves computer models' subsequent generalization ability to recognize faces, the findings suggest that receiving low-quality sensory input may be key to some aspects of brain development.
Artificial intelligence is not like us. For all of AI's diverse applications, human intelligence is not at risk of losing its most distinctive characteristics to its artificial creations. Yet, when AI applications are brought to bear on matters of national security, they are often subjected to an anthropomorphizing tendency that inappropriately associates human intellectual abilities with AI-enabled machines. A rigorous AI military education should recognize that this anthropomorphizing is irrational and problematic, reflecting a poor understanding of both human and artificial intelligence. The most effective way to mitigate this anthropomorphic bias is through engagement with the study of human cognition -- cognitive science.
AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner What are the current AI or machine learning research trends? NLP AI, large neural networks trained for language understanding and generation, the best shortcuts to artificial general intelligence. Large language models, such as PaLM, GLaM, GPT-3, Megatron-Turing NLG, Gopher, Chinchilla, LaMDA, are led by WuDao 2.0 model trained by studying 1.2TB of text and 4.9TB of images using 1.75tn parameters to simulate conversations, understand pictures, write poems and create recipes. It all is relying on unlimited brute force scaling, tens of gigabytes in size and trained on enormous amounts of text data, sometimes at the petabyte scale. The Pathways Language Model (PaLM), a 540-billion parameter, dense decoder-only Transformer model trained with the Pathways system, which enabled us to efficiently train a single model across multiple TPU v4 Pods.
DeepMind, a Google-owned British company, might be on the verge of creating human-level artificial intelligence. The revelation was made by the company's lead researcher Dr. Nando de Freitas in response to The Next Web columnist Tristan Greene who claimed humans will never achieve AGI. For anyone who doesn't know, AGI refers to a machine or program that can understand or learn any intellectual task that humans can. It can also do so without training. Addressing the somewhat pessimistic op-ed, and the decades-long quest to develop artificial general intelligence, Dr de Freitas said the game is over.