Every company may want to put artificial intelligence to work, but most companies aren't blessed with the ability to hire battalions of data scientists–nor is that necessarily the right approach. As Gartner analyst Svetlana Sicular once argued, often the best possible data scientist is the person you already employ who knows your data and simply needs help figuring out how to unlock it. For many business line owners, it's this kind of approach that may make the most sense, as they seek to be smarter with the data they already have. One company working to enable this vision is Cambridge, Massachusetts-based machine learning startup Akkio, which pairs AI with low code in an attempt to democratize AI. I caught up with company co-founder and COO Jon Reilly to learn more.
I have not posted a lot about Natural Language Processing, NLP, because there are not a lot of data science competitions concerning this genre of machine learning. I have, however, discovered that Kaggle, the premier data science website, does have text based datasets in their dataset section of their website.
All the sessions from Transform 2021 are available on-demand now. Eighty-one percent of employees believe AI improves their overall performance at work. As a result, more than two-thirds (68%) are calling on their employers to deploy more AI-based technologies to help them execute tasks. That's the top-level finding from a study published today by 3GEM on behalf of SnapLogic, which surveyed 400 office workers across the U.S. and U.K. about their opinions on AI in the workplace. "In recent years, there was concern among office workers that AI would drive job losses, but employee opinions seem to have changed. The more they've been exposed to AI and see it in action, the more they've realized how much it can assist them with their daily work," SnapLogic CTO Craig Stewart said in a statement.
As of July 25, get the 12-course bundle for $39.96. Aspiring to be a software engineer is admirable. Aspiring to be a software engineer at Google is extraordinarily ambitious -- but that doesn't mean it's impossible to achieve. Just like any other dream job, the only way to get there is by simply taking the first step. And this 2021 Google Software Engineering Manager Prep Bundle offers the perfect stepping stone.
Financial institutions are using AI-powered solutions to unlock revenue growth opportunities, minimise operating expenses, and automate manually intensive processes. Many in the financial services industry believe strongly in the potential of AI. A recent survey by NVIDIA of financial services professionals showed 83% of respondents agreeing that AI is important to their company's future success. The survey, titled'State of AI in Financial Services', also showed a substantial financial impact of AI for enterprises with 34% of those who replied agreeing that AI will increase their company's annual revenue by at least 20%. The approach to using AI differed based on the type of financial firm.
The new AI system takes its inspiration from humans: when a human sees a color from one object, we can easily apply it to any other object by substituting the original color with the new one. Now, imagine the same cat, but with coal-black fur. Now, imagine the cat strutting along the Great Wall of China. Doing this, a quick series of neuron activations in your brain will come up with variations of the picture presented, based on your previous knowledge of the world. In other words, as humans, it's easy to envision an object with different attributes.
Researchers Zhi Wang, Chaoge Liu, and Xiang Cui published a paper last Monday demonstrating a new technique for slipping malware past automated detection tools--in this case, by hiding it inside a neural network. The three embedded 36.9MiB of malware into a 178MiB AlexNet model without significantly altering the function of the model itself. The malware-embedded model classified images with near-identical accuracy, within 1% of the malware-free model. Just as importantly, squirreling the malware away into the model broke it up in ways that prevented detection by standard antivirus engines. VirusTotal, a service that "inspects items with over 70 antivirus scanners and URL/domain blocklisting services, in addition to a myriad of tools to extract signals from the studied content," did not raise any suspicions about the malware-embedded model.
This is a guest post by Kirk Borne, Ph.D., Chief Science Officer at DataPrime.ai, Kirk is also a consultant, astrophysicist, data scientist, blogger, data literacy advocate and renowned speaker, and is one of the most recognized names in the industry. A survey of 1,100 data practitioners and business leaders reported that 84% of organizations consider data literacy to be a core business skill, agreeing with the statement that the inability of the workforce to use and analyze data effectively can hamper their business success. In addition, 36% said data literacy is crucial to future-proofing their business. Another survey found that 75% of employees are not comfortable using data.
All the sessions from Transform 2021 are available on-demand now. Women in the AI field are making research breakthroughs, spearheading vital ethical discussions, and inspiring the next generation of AI professionals. We created the VentureBeat Women in AI Awards to emphasize the importance of their voices, work, and experience, and to shine a light on some of these leaders. In this series, publishing Fridays, we're diving deeper into conversations with this year's winners, whom we honored recently at Transform 2021. Briana Brownell, winner of VentureBeat's Women in AI entrepreneur award, didn't enter this field to earn accolades.
Artificial intelligence could be used to predict who is at risk of developing type 2 diabetes – information that could be used to improve the lives of millions of Canadians. Researchers at the University of Toronto used a machine learning model to analyze health data, collected between 2006 to 2016, of 2.1 million people living in Ontario. They found that they were able to use the model to accurately predict the number of people who would develop type 2 diabetes within a five-year time period. The machine learning model was also able to analyze different factors that would influence whether people were high or low risk to develop the disease. The results of the study were recently published in the journal JAMA Network Open.