Hive, the productivity platform provider, today announced the public release of HiveMind that uses Artificial Intelligence (AI) to automatically create a project plan in a matter of seconds. As Artificial Intelligence models are increasingly being integrated into content and note-taking platforms, Hive is pioneering the usage of the models' capacity for continuous learning and logical decision-making based on in-depth data. Modeled on six years of successful customer projects, HiveMind automatically sets out the steps to accomplish any goal, expediting project planning and execution. It has the ability to create project tasks based on simple suggestions, set next steps from received emails and reply based on the inbound email's content. "Today, superior performance in the marketplace comes from the depth of data you possess, and the ability to apply it quickly," said John Furneaux, Hive co-founder and CEO.

Hive, the productivity platform provider, announced the public release of HiveMind that uses Artificial Intelligence (AI) to automatically create a project plan in a matter of seconds. As Artificial Intelligence models are increasingly being integrated into content and note-taking platforms, Hive is pioneering the usage of the models' capacity for continuous learning and logical decision-making based on in-depth data. Modeled on six years of successful customer projects, HiveMind automatically sets out the steps to accomplish any goal, expediting project planning and execution. It has the ability to create project tasks based on simple suggestions, set next steps from received emails and reply based on the inbound email's content. "Today, superior performance in the marketplace comes from the depth of data you possess, and the ability to apply it quickly," said John Furneaux, Hive co-founder and CEO.

Shield AI, a San Diego startup that's building artificial intelligence "pilots" for military aircraft and drones, has pulled in an additional $60 million in venture capital funding. The money is follow-on investment to a financing that Shield AI announced in June. It brings the total amount raised in the Series E round to $225 million -- made up of $150 million in equity and $75 million in debt. The additional capital came from the U.S. Innovative Technology Fund. Founded in 2015, Shield AI has raised just under $575 million since inception.

Training the popular GPT-3 from scratch can cost millions of dollars. But, what if an individual researcher wants to experiment on such a large scale? It is almost impossible to generate funds for toying with networks. To address this large gap between innovation and computation in ML, a team of researchers from Russia have introduced Learning@home -- a neural network training paradigm that handles large amounts of poorly connected participants. "Hypothetically, a researcher could crowdsource the training of large neural networks with thousands of regular PCs provided by volunteers. The raw computing power of a hundred thousand $2500 desktops dwarfs that of a $250M server pod," wrote the researchers.

Welcome to the latest article in TNW's guide to the AI apocalypse. In this series we'll examine some of the most popular doomsday scenarios prognosticated by modern AI experts. It's pretty easy to think up new ways for robots to destroy us. But what if AI doesn't want us dead? Maybe our future overlords will see our weaknesses and, in their infinite benevolence, choose to upgrade us.

A study released at the 2019 Open Data Science Conference (ODSC) in Boston demonstrated that managed teams outperformed crowdsourced workers on accuracy and overall cost on a series of the same data labeling tasks. Data science platform developer Hivemind hired a managed workforce and a leading crowdsourcing platform workforce to determine which team delivered higher quality, and at what relative cost. If you're building AI anywhere in your organization, you're in a "race to useable data," according to a 2019 report released by Cognilytica. In its report, the analyst firm specializing in AI, evaluated requirements for data preparation, engineering, and labeling solutions. They found 80 percent of AI project time is spent on aggregating, cleaning, labeling, and augmenting data to be used in machine learning models (ML).