Modern machine learning models are able to outperform humans on a variety of non-trivial tasks. However, as the complexity of the models increases, they consume significant amounts of power and still struggle to generalize effectively to unseen data. Local learning, which focuses on updating subsets of a model's parameters at a time, has emerged as a promising technique to address these issues. Recently, a novel local learning algorithm, called Forward-Forward, has received widespread attention due to its innovative approach to learning. Unfortunately, its application has been limited to smaller datasets due to scalability issues. To this end, we propose The Trifecta, a collection of three simple techniques that synergize exceptionally well and drastically improve the Forward-Forward algorithm on deeper networks. Our experiments demonstrate that our models are on par with similarly structured, backpropagation-based models in both training speed and test accuracy on simple datasets. This is achieved by the ability to learn representations that are informative locally, on a layer-by-layer basis, and retain their informativeness when propagated to deeper layers in the architecture. This leads to around 84% accuracy on CIFAR-10, a notable improvement (25%) over the original FF algorithm. These results highlight the potential of Forward-Forward as a genuine competitor to backpropagation and as a promising research avenue.

If you read through the human history, especially the last 2,000 years, you will notice that famines, diseases and wars were the three most formidable challenges that we had to face (or as I call it -- the trifecta of human sufferings). Our response to the trifecta was mostly to seek safety and pray for our survival. History also tells us that when faced with external threats and no possible solutions in sight, we turned inwards and sought solace in numbers to overcome some serious challenges to our species' survival. Gradually, these cycles of sufferings led to emergence and prevalence of the unifying behavior of collaboration amongst humans. Tremendous efforts and resources, collaborated at extraordinary scale, were thrown at solving the trifecta of problems with some amount of success.

We can finally draw a line under the market that was RPA as we transition to the broader value of integrated automation, AI and analysts solutions (the "HFS Triple A Trifecta" - see definition below): So without further ado, let's hear from our lead analyst, Elena Christopher, for this Top 10 exercise: Phil Fersht, CEO & Chief Analyst HFS Research: Elena... 7 years on, is the RPA bubble bursting or is something else happening? Elena Christopher, SVP HFS Research: In 2012, HFS launched the concept of robotic process automation (RPA) to the world via a seminal report and blog. In the seven years since, the ugly truth is that we've simply succeeded in using RPA to move data around enterprises faster with less manual intervention rather than to rewire our business processes and create new thresholds of value. We are largely missing the opportunity to transform business operations. RPA gets loads of guff for creating technical debt.

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The pace of change in today's world of business and IT operations is astounding. "Digital," "Platform," and "Automation" are most frequently used words that clients hear from their service providers and advisors but there is no common understanding or definition behind these. While our industry continues to accelerate the build-out of these capabilities, client adoption and satisfaction[1] is still lagging. One of the primary reasons is the lack of a common understanding or a unifying framework. The "Triple A Trifecta" (See Exhibit 1) of Robotic Automation, Smart Analytics, and Artificial Intelligence aims to provide a clear and crisp articulation of the emerging change agents for clients to optimize, renovate, or transform their business operations.

The Green Bay Packers celebrate after Mason Crosby #2 kicked a last-second field goal to defeat the Dallas Cowboys in the NFC Divisional Playoff Game at AT&T Stadium on January 15, 2017 in Arlington, Texas. It's fair to say that traditional polling has recently had its problems with accurate forecasts, and no wonder: simply reaching lots of people to get their predictions, let alone managing that data, has never been easy. Being called a human-computer hybrid or "'artificial' artificial intelligence," UNU is a digital'hive-mind' on a hot streak for the books, which has both investors and gamblers looking on. Based on input from user "swarms," the beta-stage interactive platform has correctly predicted some of the last year's most surprising and complicated outcomes, including Sunday's last-minute "instant classic" win for the Packers, who UNU favored for the Lombardi Trophy in August, ESPN reports. The platform was launched last June by Unanimous AI, an experientially well-heeled California start-up, and harnesses users' opinions (if not their advertising eyeballs) like other topical sites: by making it social.

The Green Bay Packers celebrate after Mason Crosby #2 kicked a last-second field goal to defeat the Dallas Cowboys in the NFC Divisional Playoff Game at AT&T Stadium on January 15, 2017 in Arlington, Texas. It's fair to say that traditional polling has recently had its problems with accurate forecasts, and no wonder: simply reaching lots of people to get their predictions, let alone managing that data, has never been easy. Being called a human-computer hybrid or "'artificial' artificial intelligence," UNU is a digital'hive-mind' on a hot streak for the books, which has both investors and gamblers looking on. Based on input from user "swarms," the beta-stage interactive platform has correctly predicted some of the last year's most surprising and complicated outcomes, including Sunday's last-minute "instant classic" win for the Packers, who UNU favored for the Lombardi Trophy in August, ESPN reports. The platform was launched last June by Unanimous AI, an experientially well-heeled California start-up, and harnesses users' opinions (if not their advertising eyeballs) like other topical sites: by making it social.

Fashion, on the whole, has always been about glamour – perfect, unattainable, stuck inside the glossy pages of a magazine. It has also been the canvas for change – (the word fashion and trend do go so well together!), With the advent of tech in almost all aspects of a consumer's life, it is bewildering that the fashion community and haute couture designers have still been largely resistant to innovating with tech. At a fashion tech panel held during Wills India Fashion Week in Mumbai, a group of ecommerce honchos interspersed with tech experts discussed, among other things, the future of tech in fashion and where they see it headed in the next few years. The less far-fetched ideas were using 3D printing machines in factories, workshops and ateliers to create new and unheard of designs to present to the lovers of haute couture, the use of robots to automate manufacturing processes (although the fashionista on the panel disagreed passionately on this point).