Pecan.ai Automates Feature Engineering and Predictive Analytics
When it comes to developing deep learning predictive models, there are several stages to building a model from raw data. In the first stage, it must be determined if AI can be of use and how, thus, what specific problems
Distributed Machine Learning with Run.ai
The AI industry is making progress at simplifying distributed machine learning, defined as the process of scheduling AI workloads across multiple GPUs and machines (nodes). One of the primary benefits of distributed machine learning is its ability to reduce model
AI Startup: Weights & Biases Eases Hyperparameter Tracking
San Francisco startup Weights & Biases (WB) is making a name for itself in the AI tools market. As an “experiment tracking platform for deep learning”, WB allows users to collaborate, “visualize model performance”, and track model versions, code, workflows,
Cool AI Startups: AnotherBrain and OWKIN
The AI race in different regions of the world continues. The US leads in both quantitative and qualitative metrics, where quantitative is measured in the number of startups and qualitative is based on a value system that assigns points to
Neural Magic’s AI Engine To Disrupt The GPU Industry
It was only a matter of time before an AI startup would tackle the GPU problem head-on, the problem being the GPU itself and why it’s needed in the first place. That startup is Neural Magic and they’ve developed an
Ople Simplifies AI for the Data Scientist
Ople, the San Mateo startup is on a mission to provide AI solutions that are “easy, cheap, and ubiquitous” to data scientists around the world. Pedro Alves, who is working on his Ph.D. in computational biology founded the startup in
Deepen AI’s Image Labeling Tool for Autonomous Systems
Santa Clara startup Deepen AI has developed an annotation and labeling tool for autonomous systems using deep learning and advanced computer vision. The tool works with 2D, 3D, and 4D environments, that can annotate and label each scene and frame
25 Public Datasets for Machine Learning
Datasets are a critical part of machine learning. Any company of sufficient size will have unique domain-specific data in which they can create private datasets. The right data is helpful in building effective AI models that can improve efficiencies and
RealityEngines.AI Ready To Tackle Noisy Datasets using GANs
AI research startup RealityEngines.AI is in the midst of introducing an AI cloud-based service focused on helping organizations deploy machine learning. The startup stands out for three reasons. First, Eric Schmidt is an investor, along with Ram Shriram (early Google
12 Disruptive AI Startups
The AI startup ecosystem is thriving. Find any problem in any industry, whether it’s in eCommerce, manufacturing, insurance, medicine, airlines, or anywhere else, and there are likely to be a dozen AI startups that can address the problem in some
Cool ML Startups: DataRobot and H2O.ai
In 2018, a total of 466 AI companies raised $9.3B in funding, up from $5.4B in 2017, an increase of 72%, according to a PWC Moneytree report. The AI industry is evolving rapidly and it’s much bigger than any single
AI-based Intelligent Software Stack
The rapid pace of technology innovation is simply proving too much for some vendors. With so many new technologies hitting the market at once, as in those technologies that become the building blocks for new features, trying to build the
OpenAI Making Significant Advances in AGI
OpenAI was founded in San Francisco in 2015 with a mission “to ensure that artificial general intelligence benefits all of humanity”. Its founders, who include Elon Musk and entrepreneur and ex-Y Combinator President, Sam Altman, set up the organization partly
Battle of AI Chips Shifts from the Cloud to the Edge and IoT Frontier
AI, the Edge and the IoT Frontier IoT has shifted away from connecting simple everyday devices such as cameras, thermostats and light bulbs to smart assistants, smart appliances and live streaming baby monitors. The data gleaned from the new wave