Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! ManageEngine commissioned Vanson Bourne to conduct a global study to examine the role of IT and how it will continue to evolve in the future of work. The report discovered that everyone across the enterprise, not just IT, has a stake in how technology is chosen, deployed, configured and used. Most departments outside IT -- particularly quality control (24%) and finance (21%) -- are using artificial intelligence/machine learning (AI/ML).

Deep neural networks (DNNs) for supervised learning can be viewed as a pipeline of a feature extractor (i.e. last hidden layer) and a linear classifier (i.e. output layer) that is trained jointly with stochastic gradient descent (SGD). In each iteration of SGD, a mini-batch from the training data is sampled and the true gradient of the loss function is estimated as the noisy gradient calculated on this mini-batch. From the feature learning perspective, the feature extractor should be updated to learn meaningful features with respect to the entire data, and reduce the accommodation to noise in the mini-batch. With this motivation, we propose In-Training Distribution Matching (ITDM) to improve DNN training and reduce overfitting. Specifically, along with the loss function, ITDM regularizes the feature extractor by matching the moments of distributions of different mini-batches in each iteration of SGD, which is fulfilled by minimizing the maximum mean discrepancy. As such, ITDM does not assume any explicit parametric form of data distribution in the latent feature space. Extensive experiments are conducted to demonstrate the effectiveness of our proposed strategy.

Research has found that inadequate access to skilled talent, technology, and data is holding back AI initiatives. Most organisations are fully invested in AI but more than half don't have the required in-house skilled talent to execute their strategy, according to new research from SnapLogic. The study found that 93% of US and UK organisations consider AI to be a business priority and have projects planned or already in production. However, more than half of them (51%) acknowledge that they don't have the right mix of skilled AI talent in-house to bring their strategies to life. Indeed, a lack of skilled talent was cited as the number one barrier to progressing their AI initiatives, followed by, in order, lack of budget, lack of access to the right technology and tools, and lack of access to useful data.

AI is not a silver bullet, but the technology can positively impact different sectors in a number of ways. Most organisations, as a result, are fully invested in AI, but they are hampered by a lack of AI skills. In fact, more than half don't have the required in house skilled talent to execute their strategy, according to new research from SnapLogic. The study found that 93% of US and UK organisations consider AI to be a business priority and have projects planned or already in production. However, more than half of them (51%) acknowledge that they don't have the right mix of AI skills in-house to bring their strategies to life.

In case you missed it, Gen Y surpassed Gen X's share of the workforce a couple of years ago. IDG recently published a report, Marketing Your Technology to Millennials, on communication preferences of 20–30-somethings as IT decision-makers (ITDMs). Here are a few highlights from IDG's findings with respect to videos for marketing to millennials in IT. In general, millennials use fewer sources of information than their colleagues. It's not that they're uninterested or negligent -- they may spend more time doing research, but they're less likely to read your white paper or visit your website.