Artificial intelligence (AI) has captured the public’s imagination for over 60 years, but it has proceeded in fits and starts leading to what has become known as an “AI winter” – a long period of diminished research and funding activity. Until recently, the conventional wisdom has been that new algorithms were the limiting factor in making steady progress towards artificial intelligence. However, recent advances in machine learning, have established that historical algorithms in conjunction with high-performance computers can be used to achieve nearly human-level performance on diverse tasks such as image and speech recognition, language translation, and video game play. In each of these instances rapid progress was facilitated by the availability of massive amounts of training data well-suited to the problem under study. This realization raises the prospect that many additional artificial intelligence problems may be solvable in the near-term if the right training resources become widely available.
That is why Intelligence Advanced Research Projects Activity (IARPA) is seeking information on novel training datasets and environments to advance AI. IARPA anticipates that responses to this RFI will be used to inform future funding opportunities for creating novel training resources for artificial intelligence algorithms.
Respondents are asked to answer one or more of the following questions:
1. Which problem domain(s) has the greatest potential to benefit from the availability of new training resources and why?
2. What new training resources are needed to achieve significant progress in this domain? How should these resources be structured? How do the proposed resources compare with currently available resources?
3. What kind of effort is needed to create and/or curate these training resources? What technical, logistical, and/or legal challenges would be associated with such an effort? How much would such an effort cost, and how long would it take? How much effort and money would be required to store, maintain, distribute, and/or utilize the proposed training resources?
4. Who would be the major stakeholders in the proposed training resources? How would these stakeholders use the proposed resources?
5. Annual challenges (e.g. ImageNet Large Scale Visual Recognition Challenge) employing a standard set of data for training and/or evaluation have helped to catalyze progress in many machine learning problem domains. Should a challenge be created in the proposed problem domain, and if so, how should it be designed, implemented, and judged?
Responses to this Request for Information (RFI) are due no later than 5:00pm Eastern Time on Friday, April 1, 2016. All submissions must be electronically submitted to email@example.com as a single PDF document.
See the RFI for the preparation instructions, disclaimers, and important notes.