Author: Zach Winn | MIT News Office<\/p>\n
For all the progress that\u2019s been made in the field of artificial intelligence, the world\u2019s most flexible, efficient information processor remains the human brain. Although we can quickly make decisions based on incomplete and changing information, many of today\u2019s artificial intelligence systems only work after being trained on well-labeled data, and when new information is available, a complete retraining is often required to incorporate it.<\/p>\n
Now the startup Nara Logics, co-founded by an MIT alumnus, is trying to take artificial intelligence to the next level by more closely mimicking the brain. The company\u2019s AI engine uses recent discoveries in neuroscience to replicate brain structure and function at the circuit level.<\/p>\n
The result is an AI platform that holds a number of advantages over traditional neural network-based systems. While other systems use meticulously tuned, fixed algorithms, users can interact with Nara Logics\u2019 platform, changing variables and goals to further explore their data. The platform can also begin working without labeled training data, and can incorporate new datasets as they become available. Perhaps most importantly, Nara Logics\u2019 platform can provide the reasons behind every recommendation it makes \u2014 a key driver of adoption in sectors like health care.<\/p>\n
\u201cA lot of our health care customers say they\u2019ve had AI systems that give the likelihood of somebody being readmitted to the hospital, for example, but they\u2019ve never had those \u2018but why?\u2019 reasons to be able to know what they can do about it,\u201d says Nara Logics CEO Jana Eggers, who leads the company with CTO and founder Nathan Wilson PhD \u201905.<\/p>\n
Nara Logics\u2019 AI is currently being used by health care organizations, consumer companies, manufacturers, and the federal government to do things like lower costs and better engage with customers.<\/p>\n
\u201cIt\u2019s for people whose decisions are getting complicated because there\u2019s more factors [and data] being added, and for people that are looking at complex decisions differently because there’s novel information available,\u201d Eggers says.<\/p>\n
The platform\u2019s architecture is the result of Wilson\u2019s decision to embrace the complexities of neuroscience rather than abstract away from them. He developed that approach over more than a decade working in MIT\u2019s Department of Brain and Cognitive Sciences, which has long held the mission of reverse engineering the human mind.<\/p>\n
\u201cAt Nara Logics, we think neuroscience is on a really good track that\u2019s going to lead to really exciting ways to make decisions that we haven’t seen before,\u201d Wilson says.<\/p>\n
Following a passion<\/strong><\/p>\n Wilson attended Cornell University for his undergraduate and master\u2019s degrees, but once he got to MIT in 2000, he stuck around. Over the course of a five-year PhD and a seven-year postdoc, he created mathematical frameworks to simulate brain function.<\/p>\n \u201cThe community at MIT is really focused on coming up with new models of computation that go beyond what computer science offers,\u201d Wilson says. \u201cThe work is connected with computer science, but also considers what our brain is doing that could teach us how computers work, or how computers could work.\u201d<\/p>\n On nights and weekends during the final years of his postdoc, from 2010 to 2012, Wilson was also beginning to translate his algorithms into a commercial system in work that would be the foundation of Nara Logics. In 2014, his work caught the attention of Eggers, who had led a number of successful businesses but had grown jaded about the hype around artificial intelligence.<\/p>\n Eggers became convinced Nara Logics\u2019 AI engine offered a superior way to help businesses. Even back then the engine, which the company refers to as Nara Logics Synaptic Intelligence, had properties that made it unique in the field.<\/p>\n In the engine, objects in customers\u2019 data, such as patients and treatments, organize into matrices based on features they share with other objects, in a structure similar to what has been observed in biological systems. Relationships between objects also form through a series of local functions the company calls synaptic learning rules, adapted from cell- and circuit-based neuroscience studies.<\/p>\n \u201cWhat we do is catalog all the metadata and what we call our Connectomes go in and mine the database of unstructured data and build links across all of it that relate these things,\u201d Wilson explains. \u201cOnce you have that background, you can go in and say, \u2018I like this, this, and this,\u2019 and you let the engine crunch the data and give you matches to those parameters. What you didn\u2019t have to do is have any notion of what the right answer was for lots of similar people. You skip that whole step.\u201d<\/p>\n Each object in Nara Logics\u2019 Synaptic Intelligence stores its properties and rules locally, allowing the platform to adjust to new data by updating only a small number of associated objects. The bottom-up approach is believed to be used by the brain.<\/p>\n \u201cThat\u2019s totally different than deep learning or other approaches that just say, \u2018We\u2019re going to globally optimize everything, and each cell does what the global algorithm tells it,\u2019\u201d Wilson explains. \u201cNeuroscientists are telling us each cell is making decisions on its own accord to an extent.\u201d<\/p>\n The design allows users to explore relationships in data by \u201cactivating\u201d certain objects or features and seeing what else gets activated or suppressed.<\/p>\n To give an answer, Nara Logics\u2019 engine only activates a small number of objects in its dataset. The company says this is similar to the \u201csparse coding\u201d believed to be used in higher brain regions, in which only a small number of neurons are activated in any given moment. The sparse coding principal allows the company to retrace its platform\u2019s path and give users the reasons behind its decisions.<\/p>\n As the company has matured, Wilson has stayed plugged in to the MIT community\u2019s research, and Nara Logics participated in the STEX25 startup accelerator, run by the MIT Industrial Liaison Program, where Wilson says the company made many contacts that have turned into customers.<\/p>\n Leveraging a mind-like AI<\/strong><\/p>\n Manufacturers are already using Nara Logics\u2019 platform to better understand data from internet-of-things devices, consumer companies are using it to better connect with customers, and health care groups are using it to make better treatment decisions.<\/p>\n \u201cWe\u2019re focused on a specific algorithm, which is the mechanics of decision making,\u201d Wilson says. \u201cWe believe it\u2019s something you can codify, and we believe it\u2019s something that\u2019ll be insanely valuable if you can get that process right.\u201d<\/p>\n As Covid-19 disrupted industries and underscored the need for organizations to invest in adaptive software tools, Nara Logics nearly doubled its customer base. The founders are thrilled to be scaling a solution they feel is more collaborative and responsive to humans than other AI systems.<\/p>\n \u201cWe think the most important difference we\u2019re contributing to is building an AI where people participate and people are in the loop \u2014 they\u2019re cognizant and understanding and aware of what it\u2019s doing,\u201d Wilson says. \u201cThat helps them make smarter decisions every day, and those add up to make a big difference.\u201d<\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"