Today's artificial intelligence (AI) can do some extraordinary things. Its functionality, though, has very little to do with the way in which a human brain works to achieve the same tasks. For AI to overcome its inherent limitations and advance to artificial general intelligence, we must recognize the differences between the brain and its artificial counterparts. With that in mind, this nine-part series examines the capabilities and limitations of biological neurons and how these relate to machine learning (ML). In the first two parts of this series, we examined how a neuron's slowness makes an ML approach to learning implausible in neurons, and how the fundamental algorithm of the perceptron differs from a biological neuron model involving spikes.

From medical imaging and language translation to facial recognition and self-driving cars, examples of artificial intelligence (AI) are everywhere. And let's face it: although not perfect, AI's capabilities are pretty impressive. Even something as seemingly simple and routine as a Google search represents one of AI's most successful examples, capable of searching vastly more information at a vastly greater rate than humanly possible and consistently providing results that are (at least most of the time) exactly what you were looking for. The problem with all of these AI examples, though, is that the artificial intelligence on display is not really all that intelligent. While today's AI can do some extraordinary things, the functionality underlying its accomplishments works by analyzing massive data sets and looking for patterns and correlations without understanding the data it is processing.

By Charles Simon, a nationally recognized entrepreneur and software developer. Let's look into a future in which thinking machines have surpassed humans in mental powers. Will computers be our partners or our masters? And how will computers see us? To answer, let's consider thinking machines after the initial creation of Artificial General Intelligence (AGI) – the ability of an artificial entity to learn and understand any intellectual task that a human can.

The company's Brain Simulator II is an open-source software platform for proving the evolution of artificial intelligence (AI) to artificial general intelligence (AGI). In what the company says is a significant advance in the development of AGI, the system - comprising an AMD Ryzen Threadripper 3990X CPU running at 2.9 GHz (not overclocked) and 128 Gigabytes of RAM - processed three billion synapses per second. Seen as another step toward creating brain-level functionality on computers, the spiking neural models used by the Brain Simulator II, says the company, are more like biological neurons than traditional AI models and contribute immensely to the efficiency of the program. Energy analysis of the the higher-level thinking part of the brain - the neocortex - shows that neurons spike on average, only once every six seconds - meaning that its 16 billion neurons generate only 2.5 billion spikes per second in total. The Brain Simulator's spiking neural model, says the company, only processes neurons that spike in a specific time interval, rather than processing all of them, and so can be thousands of times faster than traditional artificial neural networks.

The open-source Brain Simulator II neuron engine has been successfully tested with one billion neurons on a desktop computer comprised completely of off-the-shelf components. From a performance perspective, this system processes more than 2.5 billion synapses per second. For this test, the network included 10 million neurons with 500 synapses per neuron, for a total of 5 billion neurons which could be fully processed in 1,900 milliseconds. For comparison, the identical program on a four-year-old, four-core CPU can only achieve 0.7 billion synapses per second. The computer in this example included an AMD Ryzen Threadripper 3990X CPU running at 2.9Ghz (not overclocked) and 128 Gigabytes of RAM.

In a significant advance in the development of Artificial General Intelligence (AGI), the Brain Simulator II neural simulator successfully tested one billion neurons on a desktop computer comprised completely of off-the-shelf components. From a performance perspective, the system processed three billion synapses per second. Brain Simulator II is an open-source software platform for proving the evolution of artificial intelligence (AI) to AGI. Seen as another step toward creating brain-level functionality on computers, the spiking neural models used by the Brain Simulator II are more like biological neurons than traditional AI models and contribute immensely to the efficiency of the program. The computer used for this achievement included an AMD Ryzen Threadripper 3990X CPU running at 2.9Ghz (not overclocked) and 128 Gigabytes of RAM.

In a significant advance in the development of Artificial General Intelligence (AGI), the Brain Simulator II neural simulator successfully tested one billion neurons on a desktop computer comprised completely of off-the-shelf components. From a performance perspective, the system processed three billion synapses per second. Brain Simulator II is an open-source software platform for proving the evolution of artificial intelligence (AI) to AGI. Seen as another step toward creating brain-level functionality on computers, the spiking neural models used by the Brain Simulator II are more like biological neurons than traditional AI models and contribute immensely to the efficiency of the program. The computer used for this achievement included an AMD Ryzen Threadripper 3990X CPU running at 2.9Ghz (not overclocked) and 128 Gigabytes of RAM.