Learn how our unique, massively distributed AI platform allows us to create breakthrough goods, patents, and research.
We have built the world most scalable AI to help customers make smarter decisions faster.
Sentient is applying its patented artificial intelligence technologies to create different solutions in verticals such as investment, medical diagnosis and e-commerce. These systems are one of a kind as we are able to deal with enormous stockpiles of data, quickly learn from it and evolve solutions where none existed before.
How? By identifying and deriving intelligence from previously unseen habits in large amounts of info. Using massively scaled, distributed evolutionary computation, our patented remedy mimics biological evolution. This enables it to learn, adapt and react more rapidly to provide our clients with the answers and decisions they need.
Our deep learning technologies, working at scale, may swiftly understand human intent using visual and other non-verbal interactions, resulting in the rapid and relevant delivery of content, products and various other decisions customers desire. It’s delight sent with effect, on an unprecedented level.
THE DNA OF BETTER DECISIONS
Our patented, massively scaled and distributed AI solution mimics biological evolution, enabling it to quickly learn, adapt and react.
Through the use of Sentient massively scaled computation infrastructure and advancements in evolutionary computation, we have created a smart platform called LEAF: Learning Evolutionary Algorithm Framework.
LEAF is breaking latest ground in the modern AI period where we are applying previously academic do the job to create breakthroughs in the fields of purchase, digital commerce, agriculture and medical exploration.
Inspired by biological development, LEAF (learning evolutionary algorithm framework) can evolve virtually any data to fix specific problems. One example of this is evolved neural systems (ENNs), but isn’t limited to this sort of function. Because LEAF can operate on scaled compute, it can work semi-independently on specific CPU and/or GPU cores, loosely coordinated through asynchronous communications between various nodes.
During the past, EC typically converged quickly, within a couple of hours on a few CPUs. Using our patented technology, we’ve were able to scale EC to perform through trillions of generations across an incredible number of CPUs to make a system powerful plenty of to solve some of the world most complex concerns.
But how will our massively distributed EC program work?
Our AI system starts by generating-and then comparing-a diversity of prospect agents (genes) to distinguish which kinds are better suitable for solve a specific problem. A fitness rating is designated to each prospect based on how very well it performs in comparison to its peers. Remember that that is a relative measure, rendering it unnecessary to learn the fitness rating of the best possible candidate.
The first population of prospect agents all likely perform poorly because they are generated randomly (step 1 1). But just as the machine evaluates them against certain training data, some prove much less bad than others (step two 2), so the program retains these and eliminates the remaining candidate pool. After that it makes use of parts of the better prospects as a way to generate a fresh population (step three 3). This process is repeated an incredible number of moments. Through masses of generations, massively distributed over millions of CPUs, the program slowly but surely converges on solutions, leading to code that gets results to fix the complex complications we check out in trading and health care research today.
Sentient EC allows the health score to comprise a lot more than only a single measure (target), as not absolutely all devices are optimized about the same axis. In trading, for example, we are enthusiastic about returns, and also the dispersion of the returns. The machine may also measure and prize prospects on diversity or novelty of their patterns.
Sentient EC can be geared for tackling challenges where the fitness score isn’t convenient to compute, such as data problems where the only approach to assess a candidate is to use it to as much data samples as possible, aggregating the benefits. In Sentient EC, this assessment is manufactured incrementally, in a highly distributed fashion, with an increase of promising applicants being validated on extra info samples, increasing the dependability of the solutions.
How exactly we use deep understanding how to perceive and act in the internet.
Inspired incidentally the mind works, Sentient intent driven cleverness technology analyse image-structured interactions in order to deeply figure out a user’s intent and preferences. It then curates articles and recommends actions predicated on that understanding. By merging deep learning and proprietary systems, Sentient is transforming the user experience in from e-commerce to social media.
At the core of this experience is an intent-detection and content curation engine unlike any other AI program available today. It continually refines, organizes and gives experiences customized to a user’s specific needs for the reason that point in time. It detects the user’s immediate intent, instead of relying on text message search, metadata, historical info derived from previous classes or advertising cohorts. The effect is an improved customer experience and considerably more relevant content.
Backed by the utilization of idle or perhaps dark GPU cycles that define the proprietary and massively distributed compute useful resource, this intent-detection and content curation engine uses visual perception to take any amount of images and easily integrates in to existing work-flows, so that it is ideal for a broad range of markets beyond e-commerce. As we put more sensory receptors to the engine, it’ll figure out many different articles types, including extended visual media (images and training video), sound (tone of voice, music) and text.
Imagine applying Intent-based Cleverness technology to films, where our bodies can intuit what a person wants to view. Or in travel, where our bodies can effectively identify the vacation bundle one is most likely to select. The possibilities of such systems will be limitless and we will be excited to start to see the improvements and results it generates for our buyers and their consumers.
CREATING Highly effective DISTRIBUTED COMPUTE FOR AI
How exactly we create massive compute potential.
Cutting-edge AI techniques require incredible compute power. At Sentient, we’re created a unique network of distributed compute that allows us to resolve complex problems, create breakthrough goods, and patent ground breaking AI tactics across disciplines.
Our compute infrastructure connects over several million CPU cores and 5,000 GPU cards in thousands of locations around the world. But it’s not only the size that makes it work. It’s the proprietary middle-ware which allows us to leverage our compute information differently for each and every problem we’re working on.
For instance, in evolutionary computation, our compute infrastructure gives us the opportunity to distribute a dataset to myriad resources and allow every to evolve and mutate into feasible solutions. That middle-ware as well lets us accumulate the good generations of alternatives, redistribute them to considerably more information, and continue evolving smarter and smarter algorithms.
And that’s simply a single example. Scale we can parcel massive data sets. It allows us to research new avenues inside our preferred disciplines. It offers our AI the freedom to make more observations, to try extra novel ideas, and, eventually, to make better decisions. Scale may be the backbone of our AI platform.