The T-shirts sold by Cross & Freckle, a New York–based fashion upstart, don't look revolutionary at first glance. They come in black or white, they're cut for a unisex fit, and they sell for $25. Each of them has a little design embroidered into the cotton that references staples of New York City life: pigeons, dollar pizza slices, subway rats. They were designed instead by a neural network, which crunched doodle data from millions of people and spit out the original art that makes up the embroidery. Cross & Freckle isn't the first company to use AI to generate art--people have been doing that for years.

The AI in Advancement Advisory Council (AAAC), a first-of-its-kind organization committed to open discussion about where artificial intelligence (AI) technology can and should have an impact on advancement, today released the first State of AI in Advancement Report. The study is the first benchmark on the adoption rate, uses, opinions, of AI in the advancement industry, and more, and comes at a time when artificial intelligence is beginning to revolutionize philanthropy. "We've entered an amazing chapter of innovation and progress with artificial intelligence," said AAAC member Reed Sheard, Vice President for College Advancement and Chief Information Officer, Westmont College. "In order to truly harness this technology in a way that helps us build successful organizations, it will be critical to utilize AI in a manner that always seeks to benefit society. It is important to ask both'What can we do?' as well as'What should we do?'. The early results we've seen represent new opportunity and I am excited about what the future holds."

To help identify depression early, scientists have now enhanced a technology that uses Artificial Intelligence (AI) to sift through the sound of your voice to gauge whether you are depressed or not. Computing science researchers from the University of Alberta in Canada have improved technology for identifying depression through vocal cues. The study, conducted by Mashrura Tasnim and Professor Eleni Stroulia, builds on past research that suggests that the timbre of our voice contains information about our mood. Using standard benchmark data sets, Tasnim and Stroulia developed a methodology that combines several Machine Learning (ML) algorithms to recognize depression more accurately using acoustic cues. A realistic scenario is to have people use an app that will collect voice samples as they speak naturally. "The app, running on the user's phone, will recognize and track indicators of mood, such as depression, over time.

If artificial intelligence confuses you, think about what happens when it's not leveraged properly. For context, consider the London-based VC firm MMC who found that about 40% of European AI startups don't use AI in any tangible way. MMC (and TheVerge) are saying that companies just want to take advantage of the AI hype. Meaning that the company or startup "talks" about AI, but they are unwilling or unable to put in the resources to deploy the process, store the data, or make any meaningful use of the information. So begs the question, is artificial intelligence still relevant for startups?

In the ever changing world of information technology, business organizations are left with humongous amount of data with them. This data includes very critical information for business use, but business organizations are only able to utilize 20% of whole data available with them with the use of traditional data analytics technology. To process and interpret the reaming 80% of the data that is in the form of videos, images, and human voice (also called as dark data), there is a need of cognitive computing systems. Cognitive computing systems are typical combination of hardware and software that constitute natural language processing (NLP) and machine language, and have capability to collect, process, and interpret the dark data available with business organizations. Cognitive computing systems works exactly the phenomena of how a human brain works.

Artificial Intelligence (AI) and Machine Learning (ML) technologies deliver intelligent functionalities to pre-existing solutions across various verticals including automotive, health care and business analytics. AI and ML collectively offer optimal solutions to the existing problem, and further imparting self-learning logic in the system to instinctively address any similar complications in the future. Statistic abilities of machine learning to develop capabilities and enhance the performance of models have delivered significant traction for the technology in start-ups across the world. Companies are highly attracted to the investment opportunities for integrating machine learning in their services and products across the AI landscape. These unprecedented stages of investment are emblematic of the hype surrounding artificial intelligence and machine learning and have also accentuated the value that technology leaders believe AI and ML can bring to the world.

Banks are now offering an ever-growing number of digital functionalities1 targeted at delivering a superior client experience, exploring so-called "new" technologies to serve clients and providing more integrated and seamless services. However, the true yardstick for differentiation in an emerging banking ecosystem will be not the number of digitised services or banking apps but the value added or enhanced experience for clients. This leads to questions about how banking services should be digitised, what they should consist of in the short- and long-term, and more importantly, what capabilities and technologies banks should focus on to maintain their leading roles in emerging banking ecosystems in the future. With a new generation of'digital native' clients, core banking values and banking preferences will change entirely. According to Deloitte research, already today, 29% of millennials trust global technology firms to provide financial services.

As many folks continue to look skyward in expectation of drone delivery services, they're missing the development of an increasingly popular delivery technology back on terra firma. Wheel-based delivery robots may not be quite as sexy as their flying counterparts, but with fewer regulatory hoops to jump through than drone services, the technology is likely to become more widely used sooner rather than later by companies looking to make their delivery operations more efficient. The latest outfit to join the race is U.S. startup Refraction A.I. with its autonomous Rev-1 machine. The brainchild of University of Michigan professors Matthew Johnson-Roberson and Ram Vasudevan, the Rev-1 tootles along on three wheels instead of the four or six that we see with most of the competition. The wheels and storage compartment mean the look of the Rev-1 is similar to efforts from dominant player Starship Technologies, as well as delivery robots from FedEx and Amazon.

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.

At HPE Discover 2019, TechRepublic's James Sanders spoke with Aruba's Larry Lunetta about what is needed to deploy AI in security products. The following is an edited transcript of the interview. Larry Lunetta: So the AI washing is absolutely true--and you can reach back to the late '90s, it was web washing, right, as the internet became popular and then roll forward, it's cloud washing. There's a set of waves that the marketers ride, and AI is certainly one now. The interesting thing is as you go through those phases, the technologies get tougher and tougher to actually execute.