AI Articles and Analysis about Data management

When we search on Google or Amazon, we’re reminded of the improved capabilities of artificial intelligence over the last half decade.
What we often don’t realize is the role that human beings have in tagging and manually working through near-infinite reams of data to develop genuinely relevant search.
Sure, data scientists and ML specialists must construct a search system - but for the time being - much of the “human-like” results that we get in the world of social media, search engines, and Commerce - comes from - well, humans.
This week on AI in Industry we interview Vito Vishnepolsky of ClickWorker. Clickworker is a large and well-rated microtasking marketplace. Clickworker cloud technology platform caters data management and web research services as well as AI algorithms training. The firm claims to have over one million workers on its global platform as of September 2017.
Vito’s perspective is valuable because he has a finger on the pulse of crowdsourced demand, handing business development for various crowdsourced AI support services - both for tech giants and startups.

If you combine the hype-factor of both "blockchain" and "artificial intelligence" you often get a supernova of jargon. This week on the AI in Industry podcast, we aim to get beyond the hype to discuss how blockchain might make AI more accessible for small and mid-sized businesses in the years ahead. Dr. Ben Goertzel - CEO of SingularityNET - is our guest this week.

With internet penetration on the rise, the e-commerce sector is booming in India. According to research by Boston Consulting Group, the number of internet users in  India will jump upwards of 550 million in 2018 from 190 million as of June 2014. India has over 300 million smartphone users which has surpassed the US to become the second largest smartphone market in the world. As per estimates, the Gross Merchandise Value (GMV) sold by eCommerce companies in India is expected to grow to around $80 billion by 2020 up from around $4 billion in 2009.

Episode summary: Guests Will Jack and Nikhil Buduma co-founders of Remedy Health Inc discuss the challenges involved in collecting, setting up and structuring data in order to implement AI in healthcare. By the end of this episode, listeners will have gained insight into the challenges of healthcare data systems, and the potential solutions to cleaning and organizing this data for healthcare AI applications.

Episode Summary: If you're going to apply machine learning (ML) in a business context, you need a lot of data, and algorithms across the board perform better with more recent, rich, and relevant data. Today, there are companies whose entire business models are predicated on helping others make sense of and use of this type of information, as more entities look for the first place to apply ML in their organization.  In this episode, we speak with the CTO and Co-Founder of one such company—Palo Alto-based Cloudera. CTO Amr Awadallah, PhD, speaks with us this week about where he sees "data lakes" (or "data hubs", Cloudera's preferred term) and warehouses play an important role in ML applications in business. Based on his experiences helping a variety of companies in many countries set up data lakes, Amwadallah is able to distill and communicate these uses in three broad categories that apply across industries as companies look to apply ML applications to solve tough problems and ask more complex questions using unstructured data.

Episode Summary: What does it mean to tune an algorithm, how does it matter in a business context, and what are the approaches being developed today when it comes to tuning algorithms? This week's guest helps us answer these questions and more. CEO and Co-Founder Scott Clark of SigOpt takes time to explain the dynamics of tuning machine learning algorithms, goes into some of the cutting-edge methods for getting tuning done, and shares advice on how businesses using machine learning algorithms can continue to refine and adjust their parameters in order to glean greater results.

Episode Summary: What are executives missing the boat on and what do they need to think about when it comes to AI and machine learning? This week, we speak with John Straw, who has had a number of businesses in the UK and US and is currently a senior advisor to McKinsey & Co.
John works with a lot of executive teams in finding new applications for AI and machine learning and pinpointing ROI for those technologies in industry. This week, Straw shares his insights on how to solve business problems with machine learning. Straw also touches on aspects he believes are most commonly overlooked when executives learn machine learning, specifically in finding applications that can keep them up to speed with their competitors in the field.

It's easy to see the massive rise in popularity for venture investment, conferences, and business-related queries for "machine learning" since 2012 - but most technology executives often have trouble identifying where their business might actually apply machine learning (ML) to business problems.