When Hugging Face first announced itself to the world five years ago, it came in the form of an iPhone chatbot app for bored teenagers. It shared selfies of its computer-generated face, cracked jokes and gossiped about its crush on Siri. It hardly made any money. The viral moment came in 2018--not among teens, but developers. The founders of Hugging Face had begun to share bits of the app's underlying code online for free.

When Hugging Face first announced itself to the world five years ago, it came in the form of an iPhone chatbot app for bored teenagers. It shared selfies of its computer-generated face, cracked jokes and gossiped about its crush on Siri. It hardly made any money. The viral moment came in 2018--not among teens, but developers. The founders of Hugging Face had begun to share bits of the app's underlying code online for free.

Let's talk about our current tech revolution and women's involvement in it. These are troubling times, on multiple fronts. Something we need to remind ourselves on a regular basis especially for those in the tech industry and tech leadership. I was reading a few articles about this topic and would like to discuss two points. I dug a little more to find the source of that 97% and found a more complete list of all the jobs that could go away with automation in an article in The Telegraph entitled "These are the jobs most at risk of automation according to Oxford University: Is yours one of them?" Word to the wise, if yours is on the top of the list, start looking for a safer alternative by scrolling down, way down.

With a technology as impactful as machine learning (ML), it can be difficult to avoid hyperbole. Sure, billions of dollars in investment are pouring into ML projects. Yes, machine learning is a centerpiece of digital transformation strategies. And, to be certain, machine learning is often what people are talking about when they use the umbrella term "AI." So it's worth taking the time to look at real-world ML capabilities being developed and deployed at digitally nimble companies around the globe.

With a technology as impactful as machine learning (ML), it can be difficult to avoid hyperbole. Sure, billions of dollars in investment are pouring into ML projects. Yes, machine learning is a centerpiece of digital transformation strategies. And, to be certain, machine learning is often what people are talking about when they use the umbrella term "AI." So it's worth taking the time to look at real-world ML capabilities being developed and deployed at digitally nimble companies around the globe.

Big data is disrupting the retail industry, ushering in a new era of hyper-targeting and hyper-personalization. Just as the Internet disrupted traditional retail storefronts 20 years ago, the era of big data is now upending conventional retail marketing models that relied on demographics. A wealth of structured and unstructured customer data creates the potential to understand customer intent in real time and target customers for instant sales. For many retailers, however, the true potential of this data remains untapped with no practical application. Retailers lack the capability and expertise to analyze this data fully and then translate findings into action.

Many people worry that "AI" will usher in a new Industrial revolution where machines replace humans. My take is that it will be more like the Printing press revolution that launched the Age of Enlightenment! The effect will be less of soaring productivity but more of better decision-making leading to a SMARTER society. Part of the problem is the misnomer, "AI or artificial intelligence" (which may be great marketing). Instead, if we called it what it is, i.e., "ML or machine learning", things will sound less ominous and convey a more realistic picture.

There's no doubt that we're entering the age of AI, with Machine Learning touching almost everything we're involved in on a day-to-day basis. Spurred on by step innovations in data storage and computing power, Neural Nets are back from the 70's with a bang. Medicine, security, customer service, fraud detection, you name it -- there are well funded companies applying Machine Learning to improve and augment it. Heck, you might have even found this post through Medium's Machine Learning-based recommender systems. Deep Learning, for whatever reason, seems to work really well for a number of problems with immediate impact.

An excellent lineup of financial practitioners and academics presented research and insights on big data and machine learning in finance. Access a copy of the presentations and watch all available sessions: https://goo.gl/5pAMqx RavenPack's prestigious annual event has experienced growing interest, with attendance exceeding 260 buy-side professionals. Word on the street is RavenPack's research symposium is a "must attend event" for quantitative investors and financial professionals that are serious about Big Data.

Have you noticed that the better you know someone, the easier it is to communicate with them? When we are particularly close, this can border on the telepathic as we start to anticipate what the other person is going to say and finish their sentences. Unconsciously, our brains are collecting, processing, storing, and recalling a huge range of verbal and nonverbal signals, then translating this learning and familiarity into actions. Of course, we're a long way from understanding – let alone replicating – the infinite complexities of the human brain. But in the simplest of terms, this is how machines can learn to interact with people.