Artificial general intelligence company Future AI, which raised $2 million in initial funding to accelerate the development of its technology and algorithms including its Universal Knowledge Store, is using unique graph algorithms and structures that are self-adaptive.
Looking to upend AI technologies that use and are bound by the limitations of machine learning, Future AI launched its prototype and artificial entity, Sallie.
Sallie uses mobile sensory pods with multiple senses and abilities that enable the system to learn from a real-world environment and gain a fundamental understanding of physical objects, cause and effect and the passage of time. This gives Sallie the ability to draw conclusions – a facet of genuine thinking and a necessary component to ushering in true artificial general intelligence.
“The first component of being able to understand like a person is learning about immediate surroundings,” said Charles Simon, founder and CEO, Future AI. “Our work advances new algorithms which simulate biological neuron circuits with high-level artificial intelligence techniques. Sallie can infer information about objects she doesn’t understand.”
Sallie’s technology and knowledge can be incorporated into existing AI applications. The result will be more effective and more human AI applications. For instance, it can help create better personal assistants like Alexa and Siri, language translation, computer vision, automated customer service systems and other human-interactive systems.
“With the latest advancements in symbolic AI and neuromorphic computing, adding real-world understanding to AI and achieving human-like intelligence is gradually transitioning to the realm of possibility,” said Ritu Jyoti, group vice president AI and automation market research and advisory services, IDC. “This decade will play a crucial role in accelerating the development of AGI.”
Future AI will be beta testing Sallie starting later this year.
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