In the last two articles in this 3-part series, we discussed how AI priorities will shift in response to the coronavirus pandemic, as well as how companies can further leverage the advantages they have (and can create) to overcome the challenges they are facing in this uncertain time.
We have gotten a lot of responses regarding our first article in this series on new AI priorities in the COVID-19 era.
The COVID19 outbreak has changed the world faster than anyone could have imagined.
Forced isolation has shifted meetings and activities to go on through web collaboration tools.
The AI conversation has made it into the C-suite at large banks. Leaders from Citi to JP Morgan are considering how to respond to their competitors' press releases and looking to craft winning AI strategies and adopt low-hanging fruit AI applications in their business.
The artificial intelligence space is increasingly competitive with new AI companies and products being developed every day. Every industry is getting more and more crowded with products from startups and from established companies.
In the 1990's, Ben Horrowitz described a product manager as follows:
"A good product manager is the CEO of a product."
That definition isn't always a perfect fit, but it can be a good way of summarizing the responsibilities of a product manager; they are wholly in charge of bringing an in-house product from inception to generating an ROI.
Many business leaders make the mistake of believing that AI and machine learning are like regular IT, but this could not be further from the truth. In large part, this is because, unlike simple software solutions for discreet business problems, it can be very difficult to measure the ROI of machine learning.
You might not know it when reading AI vendor websites or press releases from enterprises, but when you dig deep enough into why enterprises actual adopt AI, the pattern is clear:
Companies looking to apply AI are looking for a competitive advantage in their industry, something that will give them an edge in the market and help them grow. However, not every AI application can give a company a competitive advantage. Many AI applications are simply going to become the new normal.
One of the many concerns that business leaders have around automation is how they're going to adapt their workforce to new technology. The digital age has brought a rapid shift in the skillsets employees need to go about their jobs effectively, leaving little time for business leaders to come up with strategies for how to reskill and retrain their employees, prevent their companies from drawing disdain from the public, and how to compete with their peers.
There are numerous AI initiatives in progress across the healthcare industry; some of these are for mental health and well-being. In this article, we offer an overview of how AI is facilitating mental healthcare.
Over the last four years, interviewing hundreds of AI researchers and AI enterprise leaders, we've consistently heard the same frustrations about AI adoption said time and time again.
"Culture is hard to change."
"Leadership doesn't know what they're trying to accomplish."
"Nobody knows what to do with these data scientists we've hired."
In our one-to-one work with enterprise clients, we've taken the most prevalent, recurring challenges to AI deployment and put them together into a framework of "prerequisites" to AI deployment.
Emerj Technical Advisor, German Sanchis Trilles, PhD, defines natural language processing as:
“...everything which is related to human language. If you have a system that needs to recognize what a human wrote, that’s NLP. If you have a system that tries to understand what a human said with his voice or with her voice, that’s NLP as well. If you want a system to speak and to do some speech synthesis, that’s NLP as well.
Content marketing encompasses many ways to advertise and attract new potential customers. At Emerj, we've worked on hundreds of campaigns for AI-related products and services, and what we've learned is that content marketing for AI vendors boils down to two things:
Companies often emphasize artificial intelligence on their homepage or on services pages. While AI is currently an important topic, a simple mention of it on a website’s homepage will not get potential customers interested. Instead, it is more important to focus on the direct needs of the stakeholders who find an AI vendor website and their online properties in order to get them closer to a sale.
Event Title: Symposium Artificial Intelligence for Science, Industry and Society
In this article, I showcase 6 examples of nontechnical professionals who used their business and subject-matter expertise (not their coding ability) to have more exciting careers in AI, and I respond directly a number of questions and comments from Emerj subscribers about AI knowledge for career advancement
This is the second article in our "AI for Career Acceleration" series - be sure to read the first installment (and watch the video on that page).
Last month we ran a podcast series on the AI in Industry podcast on the theme of “Advancing Your Career in the Era of AI”, with a focus on how non-technical professionals can become more valuable in the market, and can become involved in AI projects and initiatives, without ever learning to code.
I received twice as much feedback on this series as any other series we’ve ever run on the podcast - which surprised me.
It surprised me because I think about everything on Emerj.com as being useful for nontechnical professionals. We’ve built our editorial calendar and our products around the needs of nontechnical professionals who want to make the most of their careers, but this recent series spoke to that topic directly.
But hitting directly on the theme of “Advancing Your Career in the Era of AI” clearly hit a cord.
For that reason, I’ve decided to release a three-part video and article series on that same topic, breaking down the lessons that were most important for me - and sharing a bit of my own story going from small-town martial arts instructor to international AI speaker and strategist.
Before getting into the small-town martial arts instructor part, I’d like the share a pivotal Silicon Valley conversation that changed the course of my career:
While it is difficult for people to agree on a vision of utopia, it is relatively easy to agree on what a “better world” might look like.
The United Nations “Sustainable Development Goals," for example, are an important set of agreed-upon global priorities in the near-term:
The financial sector was one of the first to start experimenting with machine learning applications for a variety of use-cases. In 2019, banks and other lenders are looking to machine learning as a way to win market share and stay competitive in a changing landscape, one in which people are no longer exclusively going to banks to handle all of their banking needs.
Four months ago we launched our AI in Banking podcast where we covered some of the most critical topics related to AI adoption and implementation in banks and financial institutions each month. Our series was based on interviews with AI industry experts, many of whom also shared their valuable insights during our first comprehensive banking research project, the AI Vendor Scorecard and Capability Map.
Businesses still don't have a clear understanding of what to expect when it comes to the ROI of AI. Many believe that AI is just like any other software solution: the returns should, in theory, be immediate. But this is not the case. In addition, business leaders are often duped into thinking the path to ROI is a lot smoother than it is when it comes to AI because AI vendors tend to exaggerate the results their software generates.
We spoke with David Carmona, the GM of Artificial Intelligence at Microsoft about his approach to AI ROI with the enterprise clients he works with at Microsoft. The biggest takeaway from this episode comes right at the beginning. David talks about how to think about artificial intelligence ROI in the long-term and the near-term.
It's clear that there's a revolution in how artificial intelligence is done with neural networks as opposed to the old school systems of the '80s and the '90s. It's clear that hardware is beginning to evolve, and it's also quite clear that the way that we power these hardware systems is going to have to change.
We spoke with Jonathan Ross, CEO and founder of Groq, an AI hardware company, about software defined compute. This interview is part of a series we did in collaboration with Kisaco Research for the AI Hardware Summit happening in Mountain View, California on September 17 and 18.
The general premise of this article is different from most of my previous AI Power articles.
While most of the articles in this series have related to the near-term struggles for power between organizations and governments with regards to regulation, data, and international policy, this article will focus on the long-term trajectory that AI and technology are headed towards and what that means for the most powerful nations and organizations.
We spoke to Moe Tanabian, General Manager of Intelligent Devices at Microsoft, who is speaking at the AI Edge Summit in Mountain View, California on September 17 and 18. Tanabian discusses how to think about and reframe business problems to make them more accessible for AI, as well as AI at the edge, which involves doing AI processing on individual devices rather than in the cloud. The edge could open up new potential for business problems to be solved with AI. Tanabian also provides representative use cases of intelligent devices.
The advent of machine learning in finance ushered in a keen interest in using AI to automate processes from fraud detection to customer service. While some use-cases aren’t nearly as established as others, our research leads us to believe that in the coming five years, banks will continue to invest in machine learning for risk-related processes, including underwriting.