AI models can benefit as much from soft data such as personal anecdotes as much as hard data. It's well known among data science circles that the more diverse your set of training data, the more accurate your model will be. This includes structured, unstructured, and semistructured data. However, not all data is treated equally, especially when it comes to unstructured data. Soft data such as collective memory and personal anecdotes can be challenging to access, but they can help build better decision-making systems.

If you're finding it harder to get access to GPUs in the cloud to train your AI model, you're not alone. The combination of a global chip shortage and increased demand for AI model training may be leading to longer wait times for some cloud GPU users. Some GPU users are waiting longer to access cloud-based GPUs than they are accustomed to waiting, according Gigaom AI analyst Anand Joshi. While Joshi doesn't have any firsthand knowledge of cloud platform's GPU expansion plans, he said the wait times customers are experiencing are an indication that the cloud platforms have not been able to obtain new GPUs at the pace they had expected or wanted. That, he says, may be impacting their ability to expand GPU cloud environments to keep up with increasing demand for model training, which is the most computational demanding component of the AI lifecycle.

HPE has released a pair of systems designed to broaden the uptake and speed deployment of machine learning among enterprises. Swarm Learning is aimed at bringing the wisdom of crowds to machine learning modeling without sacrificing security, while the Machine Learning Development System is meant to offer a one-box training solution for companies that would otherwise have had to design and build their own machine learning infrastructure. The Machine Learning Development System is available in physical footprints of several different sizes, but the company says a "small configuration" uses an Apollo 6500 Gen10 compute server to provide the horsepower for machine learning training, HPE ProLiant DL325 servers and Aruba CX 6300 switches for management of system components, and NVIDIA's Quantum InfiniBand networking platform, along with HPE's specialist Machine Learning Development Environment and Performance Cluster management software suites. According to IDC research vice president Peter Rutten, it's essentially bringing HPC (high performance computing) capabilities to enterprise machine learning, something that would usually require enterprises to architect their own systems. "It is the kind of system that businesses are really looking for, now that AI is more mature," he said.

Did you miss a session from the Future of Work Summit? Austria-based Mostly AI, a startup that simulates synthetic data for AI model training and testing, today announced it has raised $25 million in a series B round from Molten Ventures. The company plans to use the investment to accelerate its work in setting the groundwork for responsible and unbiased AI, hiring fresh talent, and strengthening its presence across Europe and North America. For any modern-day enterprise, the biggest challenge associated with leveraging data for AI/ML is ensuring the privacy of its consumers -- the original source of the data -- and eliminating the possibility of any sort of bias due to historical or social inequities in that data. Organizations often find a hard time dealing with the two problems and either end up facing fines for privacy violations (under regulations such as GDPR) or train a model which is unfair on one or more parameters.

By now it's evident that artificial intelligence (AI) is the singular most definitive technology of this generation, and it's powering broad industrial transformation across critical use cases. Ronald van Loon is a NVIDIA partner and had the opportunity to apply his expertise as an industry analyst to explore the implications of MLPerf benchmarking results on the next generation of AI. Enterprises are facing an unprecedented moment as they strive to leverage AI for competitive advantage. This means optimizing training and inferencing for AI models to gain differentiating benefits, like significantly improved productivity for their data science teams and achieving faster time to market for new products and services. However, AI is advancing incredibly quickly and AI model size is dramatically increasing in such areas as Natural Language Processing (NLP), which has grown 275 times every two years using the Transformer neural network architecture.

While current artificial intelligence (AI) technology holds strategic and transformative potential, it isn't always environmentally-friendly due to high energy consumption. To the rescue are researchers from Massachusetts Institute of Technology (MIT), who have devised a solution that not only lowers costs but, more importantly, reduces the AI model training's carbon footprint. Back in June 2019, the University of Massachusetts at Amherst revealed that the amount of energy utilized in AI model training equaled 626,000 pounds of carbon dioxide. Contemporary AI isn't just run on a personal laptop or simple server. Rather, deep neural networks are deployed on diverse arrays of specialized hardware platforms. The level of energy consumption required to power such AI technologies is approximately five times the lifetime carbon emissions from an average American car, including its manufacturing.

In a newly published paper, MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers propose a system for training and running AI models in a way that's more environmentally friendly than previous approaches. They claim it can cut down on the pounds of carbon emissions involved to "low triple digits" in some cases, mainly by improving the computational efficiency of the aforementioned models. Impressive feats have been achieved with AI across domains like image synthesis, protein modeling, and autonomous driving, but the technology's sustainability issues remain largely unresolved. Last June, researchers at the University of Massachusetts at Amherst released a report estimating that the amount of power required for training and searching a certain model involves the emissions of roughly 626,000 pounds of carbon dioxide -- equivalent to nearly five times the lifetime emissions of the average U.S. car. The researchers' solution, a "once-for-all" network, trains a large model comprising many pretrained sub-models of different sizes that can be tailored to a range of platforms without retraining.

A preprint paper coauthored by Uber AI scientists and Jeff Clune, a research team leader at San Francisco startup OpenAI, describes Fiber, an AI development and distributed training platform for methods including reinforcement learning (which spurs AI agents to complete goals via rewards) and population-based learning. The team says that Fiber expands the accessibility of large-scale parallel computation without the need for specialized hardware or equipment, enabling non-experts to reap the benefits of genetic algorithms in which populations of agents evolve rather than individual members. Fiber -- which was developed to power large-scale parallel scientific computation projects like POET -- is available in open source as of this week, on Github. It supports Linux systems running Python 3.6 and up and Kubernetes running on public cloud environments like Google Cloud, and the research team says that it can scale to hundreds or even thousands of machines. As the researchers point out, increasing computation underlies many recent advances in machine learning, with more and more algorithms relying on distributed training for processing an enormous amount of data.

With the consumerization of enterprise IT, employees are expecting the same level of service excellence from IT service management (ITSM) solutions. Users expect enterprise services to be delivered instantly, in the device and channel of their preference, with a high degree of personalization and without having to talk to someone. Leading enterprises are now transforming toward ITSM by embracing conversational AI, which promises to boost productivity while using fewer resources, achieving greater cost savings and delivering an engaging customer experience for enhanced satisfaction. The obvious question is, "How do I get started with conversational AI?" The following discussion highlights some erroneous--but unfortunately common--misbeliefs about conversational AI solutions. It shows how any enterprise can embrace the ITSM transformational journey irrespective of their size, level of deployed automation and personnel on staff.