An AI with a Human Brain
No other technology, like artificial intelligence, has recently created so many hopes and fears, aspirations, and trepidation, celebration, and condemnation (AI). There has been an endless array of reports about technological promises that are too strong for humanity to refuse in just the past few months.
We see a modern form of extractivism at this moment in the 21st century that is well underway: one that reaches into the farthest corners of the biosphere and the deepest layers of cognitive and affective human beings. Many of the assumptions made by machine learning systems about human life are limited, conventional, and laden with mistakes. Yet these assumptions are being inscribed and built into a new world, and will gradually play a role in the distribution of opportunities, income, and information.
Even in its current form, AI has become an indispensable part of our daily life. From understanding what you are saying to virtual assistants, such as Amazon’s Alexa and Apple’s Siri, to identify who and what is in a picture, to spot spam, or to detect credit card fraud, AI is ubiquitous today, used to suggest what you should buy next online.
What AI does for the World
While the consequences of integrating AI into our lives may sound overwhelming enough to fully eradicate its applications, this is why AI is a blessing to humanity and not a curse that might destroy it in the future!
Enhanced Automation
Without the need for human interference, AI can perform intensive human labor and backbreaking tasks easily. In industries as well as in various fields, this has significantly automated many applications and activities.
In order to reduce the workload of humans, machine learning, deep learning, and other AI technologies are being rapidly implemented and integrated into industries and organizations.
Eliminates Tedious Tasks
AI frees humans and helps them to accomplish things that they excel in. In order to achieve varying outcomes, we should base the need for AI and its implementations on the claim that this technology takes care of all the repetitive tasks that humans must perform.
Machines succeed at taking care of cumbersome jobs, allowing ample space and time for people to work on their life’s more imaginative and interpersonal aspects.
Disaster Response
Because of the impact of climate change, more and more organizations are now adopting artificial intelligence with algorithms to tackle disasters. Therefore, in analyzing smart disaster responses and providing real-time disaster and weather event data, AI has aptly shown its indispensability.
For humans, this is extremely useful as they can recognize an area’s weaknesses and thus help improve disaster preparedness.
These are just a few sectors in which AI has revolutionized our a very short span of time. To truly list all its applications and implications would be a truly tiresome task.
The Human AI
There has been a new breakthrough in AI which would help AI get better at everything it does already by learning faster. When trained to use a much faster method for learning new objects, computer-based artificial intelligence can act more like human intelligence. this was achieved by programming such a model that was designed to mirror human visual learning.
This model provides artificial neural networks with a biologically feasible way to learn new visual concepts from a small number of examples. Humans can learn new visual concepts easily and accurately from sparse data, often just a single example. And babies aged three to four months will quickly learn to identify zebras and differentiate them from cats, horses, and giraffes. But generally, computers need to “see” several samples of the same object to understand what it is.
This was revolutionary was published in the journal “Frontiers in Computational Neuroscience”, by Maximilian Riesenhuber, Ph.D., professor of neuroscience, at Georgetown University Medical Center, and Joshua Rule, Ph.D., a postdoctoral scholar at UC Berkeley.
“The computational power of the brain’s hierarchy lies in the potential to simplify learning by leveraging previously learned representations from a databank, as it were, full of concepts about objects,”
Instead of trying the more traditional approach of defining an object using only low-level and intermediate details, such as shape and color, the major change required was in developing software to distinguish relationships between entire visual categories.
The brain design behind the learning of human visual concepts builds on the neural networks involved in the identification of objects. It is assumed that the anterior temporal lobe of the brain includes “abstract” mental representations that go beyond structure. These complex visual recognition neural hierarchies allow humans to learn new tasks and, crucially, exploit prior learning.
By executing combinations of various types of information processes, the brain carries out cognitive learning and processing. Forms of knowledge processes are performed and applied in physiology by various anatomical structures. The information processes carried out by various major anatomical structures, including the cortex, basal ganglia, thalamus, and cerebellum, aids in learning faster.
These findings suggest techniques that could help computers learn more quickly and efficiently, this kind of design would make AI faster, smarter, and more accurate. Allowing AI to shatter the ceiling and reach new heights
