Share on facebook
Share on linkedin
Share on twitter
Share on facebook
Share on linkedin
Share on twitter

A Conversation With AI Talent: The Best and the Brightest (Part 1)

To stay relevant and competitive, every type of business needs to become technical in part if not all of their operations. The world is moving too fast to stay analogue. This means however that almost everywhere in the world, there is great demand for technology talent and limited supply, particularly in areas such as artificial intelligence (AI).

What can we learn from those who are already the leading minds in this space? Recently, our own Chief AI Scientist Dr. Min Sun sat down with Dr. Masashi Sugiyama, faculty at the University of Tokyo and Director of the RIKEN Center for Advanced Intelligence Project (AIP), to discuss a variety of AI-related topics.

Check out our video of their insightful conversation below, and hear our experts’ points of view on how AI research has advanced over the past 10-20 years in Japan and beyond, and particularly in the past three to four. They discussed advancements in machine learning, and the goal to ultimately reduce the amount of supervision required to ‘teach’ the machine, which would indicate significant progression in AI capabilities. This is coupled with what these advancements might look like in the future, such as in the area of healthcare, allowing for faster and more accurate medical diagnoses.

Sun and Sugiyama also tackled the aforementioned ‘talent shortage’ issue, discussing the responsibility of both Japanese educators and employers in encouraging advanced AI qualifications and in training staff to use new AI tools most effectively in their jobs.

Also, stay tuned for the upcoming Part 2 of this discussion, this time with Dr. Greg Mori, professor at Simon Fraser University and Director of Borealis AI.

 

WE ARE HERE TO HELP

Let us know the marketing challenges that you’re facing, and how you want to improve your marketing strategy.

YOU MIGHT ALSO LIKE

5 Types of Regression Analysis And When To Use Them

Regression analysis is an incredibly powerful machine learning tool used for analyzing data. Here we will explore how it works, what the main types are and what it can do for your business.   What Is Regression in Machine Learning? Regression analysis is a way of predicting future happenings between a dependent (target) and one or more independent variables (also known as a predictor). For example, it can be used to predict the relationship between reckless driving and the total number of road accidents caused by a driver, or, to use a business example, the effect on sales and spending a certain amount of money on advertising. Regression is one of the most common models of machine learning. It differs from classification models because it estimates a numerical value, whereas classification models identify which category an observation belongs to. The main uses of regression analysis are forecasting, time series modeling and finding the cause and effect relationship between variables.   Why Is It Important? Regression has a wide range of real-life applications. It is essential for any machine learning problem that involves continuous numbers – this includes, but is not limited to, a host of examples, including:   · Financial

How to Make Every Ad Dollar Count With AI-Based Budget Management

The global pandemic has significantly boosted mobile app usage, with the latest figures showing that global app downloads reached 37.8 billion in the second quarter of 2020 – 9.7 billion higher than the same period in 2019. People are also spending more time on social media. A GlobalWebIndex survey shows a rise of 10.5 percent in social media usage in July this year compared with a year ago. To ride the wave of growth, marketers engaging in app install advertising will want to prioritize social media and paid search platforms. But to ensure the best return on advertising spend (ROAS), you need to target users who are most likely to engage with your ads. By leveraging AI-powered predictive segmentation and keyword targeting, you can boost your chances of reaching and converting those users. Once you know your target audience, you need to figure out how much you want to spend on each campaign and ad in order to meet your goal while keeping your cost per install (CPI) down. As technology advances, using data and artificial intelligence (AI) might be your best bet to maximize ad dollars and optimize campaign management.   The Key Challenges of Ad Campaign Management One

Predictive Audience Segmentation: Take the Shortcut to Identify Your Target Audience_image

Predictive Audience Segmentation: Take the Shortcut to Identify Your Target Audience

The days of general advertising or marketing campaigns targeting the masses are numbered. Whether you are selling a bank loan or clothing online, you have to know your specific target audience. The hard part? Identifying the right audience to help you maximize your marketing return on investment (ROI). Analyzing and segmenting online traffic can be a painfully manual process. Efforts can range from educated guesses to applying simple analysis tools on data. While these will work when you are analyzing a handful of dimensions, the real challenge is when there is complex data or a combination of over 80 dimensions to analyze.   Powerful AI tools to target your audience Today, one of the most exciting tools available to marketers is predictive audience segmentation powered by artificial intelligence (AI). As part of the larger category of predictive analytics, predictive audience segmentation has the power to help companies identify a target audience with the highest potential for conversion to a sale or click or install, whatever your KPIs (key performance indicators) are. Commonwealth Magazine, one of the most influential magazines in Taiwan, experienced dramatic results when it used the powerful predictive audience segmentation capabilities in Appier’s Aixon platform. Not only did