Who says artificial intelligence doesn't involve humans? Try telling that to Silicon Valley startup Unanimous AI. After recently achieving the rare "superfecta" -- picking the top four finishers in the Kentucky Derby -- using UNU, a new form of human-based AI using algorithms, the company is ready to share its formula with the public. After more than a year of testing, the online platform is now available in open beta. UNU relies on an artificial "swarm" of human group intelligence that comes together in real time to make predictions, said Louis Rosenberg, its creator.

Who says artificial intelligence doesn't involve humans? Try telling that to Silicon Valley startup Unanimous AI. After recently achieving the rare "superfecta" -- picking the top four finishers in the Kentucky Derby -- using UNU, a new form of human-based AI using algorithms, the company is ready to share its formula with the public. After more than a year of testing, the online platform is now available in open beta. UNU relies on an artificial "swarm" of human group intelligence that come together in real time to make predictions, said Louis Rosenberg, its creator.

Please make Pull Requests for good resources, or create Issues for any feedback! PyEvolve is a genetic algorithm library, which is a biologically-inspired optimization technique. This library enables us to solve search problems such as hyperparameter tuning. The goal of this section is to get you hands on ASAP. If the hello world example confuses you, try out the theory section first.

In this article, we present the elitist particle filter based on evolutionary strategies (EPFES) as an efficient approach for nonlinear system identification. The EPFES is derived from the frequently-employed state-space model, where the relevant information of the nonlinear system is captured by an unknown state vector. Similar to classical particle filtering, the EPFES consists of a set of particles and respective weights which represent different realizations of the latent state vector and their likelihood of being the solution of the optimization problem. As main innovation, the EPFES includes an evolutionary elitist-particle selection which combines long-term information with instantaneous sampling from an approximated continuous posterior distribution. In this article, we propose two advancements of the previously-published elitist-particle selection process. Further, the EPFES is shown to be a generalization of the widely-used Gaussian particle filter and thus evaluated with respect to the latter for two completely different scenarios: First, we consider the so-called univariate nonstationary growth model with time-variant latent state variable, where the evolutionary selection of elitist particles is evaluated for non-recursively calculated particle weights. Second, the problem of nonlinear acoustic echo cancellation is addressed in a simulated scenario with speech as input signal: By using long-term fitness measures, we highlight the efficacy of the well-generalizing EPFES in estimating the nonlinear system even for large search spaces. Finally, we illustrate similarities between the EPFES and evolutionary algorithms to outline future improvements by fusing the achievements of both fields of research.

Horse betting is harder than it looks. At the 142nd Kentucky Derby last week, only one of five experts from Churchill Downs Racetrack correctly predicted the winner. None of them correctly predicted the top four horses. Known as a superfecta, this latter bet came with 540 to 1 odds, meaning 100 down would return 540,000. And although the experts failed to predict the finishing order, an anonymous group of internet users did.

Automatic modulation classification (AMC) is an important task for modern communication systems; however, it is a challenging problem when signal features and precise models for generating each modulation may be unknown. We present a new biologically-inspired AMC method without the need for models or manually specified features --- thus removing the requirement for expert prior knowledge. We accomplish this task using regularized stacked sparse denoising autoencoders (SSDAs). Our method selects efficient classification features directly from raw in-phase/quadrature (I/Q) radio signals in an unsupervised manner. These features are then used to construct higher-complexity abstract features which can be used for automatic modulation classification. We demonstrate this process using a dataset generated with a software defined radio, consisting of random input bits encoded in 100-sample segments of various common digital radio modulations. Our results show correct classification rates of > 99% at 7.5 dB signal-to-noise ratio (SNR) and > 92% at 0 dB SNR in a 6-way classification test. Our experiments demonstrate a dramatically new and broadly applicable mechanism for performing AMC and related tasks without the need for expert-defined or modulation-specific signal information.

UNU allows groups to chat online in a new way by forming a Swarm Intelligence that can answer questions, make predictions, and each decisions. Artificial intelligence (unanimous) UNU has not only conquered the Oscars and Super Bowl, but also the famous Kentucky derby. Its Swarm Intelligence software has reportedly made betting gamblers richer, with correct predictions owing to its successful forecasting methodologies. According to News Discovery, UNU is a software program that harnesses the collective power of horse racing professionals to correctly predict the first four horses that cross the finish line and in which order. At last weekend's Kentucky Derby, one experimental player witnessed the power of this software at first hand.

If you're going down to the racetrack, you might want to have an AI by your side. An artificial intelligence program developed by Unanimous A.I. successfully predicted the Superfecta at the 142nd Kentucky Derby last Saturday, turning a 20 bet into nearly 11,000. Using'Swarm Intelligence,' the AI was able to correctly choose the winning horse, Nyquist – along with the second, third, and fourth finishers. Unanimous AI's platform works by using the knowledge a group of people online, all logged into an the same interface where they can answer a series of questions. This animation shows how UNU's swarm intelligence makes its predictions Unanimous AI's platform works by using the knowledge a group of people online, all logged into an the same interface where they can answer a series of questions.

Picking the winner from the 20 horse field at the Kentucky Derby is hard. So hard, in fact, that no expert polled by SBNation was able to do it. That's why the holy grail at the racetrack is the Superfecta, where bettors are asked not only to pick the winner, but the second, third and fourth horses to finish the Derby. This is fiendishly difficult task that, not surprisingly, defeated every expert at Churchill Downs, where no one predicted the top four horses correctly, much less in the correct order. In the world of AI, even Bing Predicts blew it, picking only heavily favored Nyquist to win the race, but missing the other 3 picks entirely.

We present an approach to creating conversational agents that are capable of returning appropriate responses to natural language input. The approach described consists of a genetic algorithm used as a feature selection technique to evolve a subset of random features towards a set of features that are more relevant to the language used in the domain; therefore improving the conversational agent's ability to return appropriate responses. The results show that over multiple iterations of the evolutionary process the genetic algorithm was able to filter out unfit features. After the evolutionary process the features that were found to be relevant were tested on an unseen test set and the algorithm achieved an accuracy of 72.678%