If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
AI has started to feel like an old conversation. But the reality is that it's only just off the starting blocks in many industries. In the last year, contact centres and organisations focused on customer engagement have moved beyond the AI hype into practical implementation. As such, businesses that want to stop talking and start doing should be following in the footsteps of these success stories. There are tangible examples of AI applications already in full swing in the contact centre industry, ranging from Natural Language Processing (NLP) to image recognition.
Machine learning technologies and techniques are giving organizations powerful new ways to utilize the vast amounts of data they're collecting. According to several reports, ML spending is increasing at a compound annual growth rate (CAGR) of around 25%. That's benefitting vendors providing ML solutions, which appears to be mostly cloud vendors outside of the HPC segment. According to Zion Market Research's July report, the global market for ML was valued at $1.6 billion in 2017 and is expected to account for $20.8 billion in spending by 2024, which translates into a rather healthy 44% compound annual growth rate (CAGR). That was the outlier in a recent roundup of ML market reports. Market Reports World came up with a similar number in its global tally on ML spending.
Two pixilated vowels, together, represent the hopes and fears of a healthcare industry seeking more intelligent solutions. "AI," artificial intelligence, has been around since 1956 but has made precious few contributions to medical practice so far. Each year, hundreds of plucky new startups hop aboard the AI hype wagon, each promising sophisticated new solutions, from nurse-bots to virtual assistants to AI-powered wearables for the elderly, just to name a few. Most are titillating but not transformative. Nearly all have failed to move the needle on quality outcomes or life expectancy.
Finally, artificial intelligence and machine learning are moving past hype and into the tools you actually depend on to keep operations humming. And now that machine learning has arrived in SolarWinds products, we know you've got some questions – probably a lot of questions. Join SolarWinds Distinguished Engineer Karlo Zatylny and SolarWinds Head Geek Thomas LaRock, along with Microsoft Senior Cloud Advocate Anthony Bartolo, for an in-depth whiteboard discussion to strip away AI/ML hype and explain what machine learning really is. Learn how to use ML to solve real-world challenges, like simplifying monitoring and resolving issues faster. As a Sr. Cloud Advocate for Microsoft, Anthony takes great pride in architecting and conducting "science experiments" to incorporate Microsoft technology and services to address a customer problem or opportunity.
We've been marketing AI as a key differentiator for nearly five years at Dynatrace. Problem is, these days everyone else is too – making it even harder to figure out if your message is getting through. And, to make matters worse, the technical audience we market to are skeptical of marketing jargon, which means, no matter what us marketers say – they probably won't believe it anyway. When the software and product(s) they've been working with for years operate "so well" and gets the business from A to B. That's why it's so easy to dismiss new technologies today. Companies are skeptical and resistant to change.
Garry Kasparav, the chess legend defeated Deep Blue', the powerful computer built by IBM in a historic match in 1996. A year later, in a re-match, much to the surprise of many, a vastly improved computer beat the International Grandmaster. What happened was Deep Blue-2' had used the same heuristics as Deep Blue-1, but it was empowered with more CPU power. Today, two decades later, we see significant advances in Artificial Intelligence (AI). IBM's Watson built jeopardy, combined by speech recognition, search and speech generation.
Think about the most common reason you find yourself reaching out to customer service in banking: it's rarely good news. Much of the time it's to resolve a problem, and you're likely not having a good customer experience. As much hype as there is on how traditional banks are "behind the times" in implementing technology to improve customer experience, I don't think full-on artificial intelligence-powered chatbots are necessarily the answer. Here's an example: Recently I received a text from a fintech P2P player regarding a password change I had not requested. I immediately was alarmed, since that is a red flag that someone is trying hack my account, and this account is directly linked to my Chase checking account.
The short answer to this question is quite easy: it is practically impossible to predict the future of machine learning; one of the most dynamic, complex, and challenging fields mankind has ever created! The history of Machine Learning is tightly linked to the history of Artificial Intelligence and teaches us that its evolution was marked by ups and downs, periods of high interest and hype followed (usually in a rather unexpected way) by periods of "oblivion" – the so-called "AI winters". While its future is impossible to be accurately predicted, most of the trends and developments that have a serious impact on Machine Learning today can be identified and analyzed. One might even dare to assume these trends and developments will play a major role in shaping its future. For me, the best way to describe the future of Machine Learning is to use some of Sci-Fi cinematography's most famous words: "Clouded this boy's future is".