Algorithms for Hire - Machine Learnings

Awesome

“For commuters, the [autonomous bus] technology is likely to provide a cheaper and more convenient way to get around... If it’s raining and cold outside and the robo-bus knows that you’re headed for an outdoor train station–but you’re going to miss the train–it could automatically take you to the next station so you could stay on the warm bus, for example. And these small adjustments to make public transportation less painless could get more people out of cars." - Adele Peters, Writer Learn More on Fast Company

Not Awesome

“I have pretty strong opinions on people [spreading fear surrounding the potential of artificial intelligence]. I am optimistic. I think you can build things and the world gets better. But with AI especially, I am really optimistic... I think people who are naysayers and try to drum up these doomsday scenarios — I just, I don't understand it. It's really negative and in some ways I actually think it is pretty irresponsible....Whenever I hear people saying AI is going to hurt people in the future, I think yeah, you know, technology can generally always be used for good and bad, and you need to be careful about how you build it and you need to be careful about what you build and how it is going to be used." - Mark Zuckerberg, CEO Learn More on MSNBC

What we're reading.

1/ Don't read too much into Mark Zuckerberg and Elon Musk's recent quarrel over the potential threat of AI - it reveals more about the support they are trying to win for their companies in the eyes of investors, policymakers, and the general public than anything else. Learn More on The Atlantic

2/ An engineering professor at NYU creates a framework to help people make sense of AI media coverage and cut through the nonsense and misunderstandings that find their way into so many articles. Learn More on Togelius

3/ Expect bikes of the future to come with built-in technology that tells a self-driving car when it needs to brake in order to avoid collisions with cyclists. Learn More on NPR

4/ Developers are ditching the gendered-stereotypes that often accompany the personalities and voices of digital assistants. Learn More on Quartz

5/ Amazon's purchase of Whole Foods continues to shake up the retail world, and large companies like Wal-Mart are responding with tech that replaces cashiers, predicts what products a customer might want to buy, and detects their level of satisfaction with facial recognition tools. Learn More on The Wall Street Journal

6/ Professor Fei-Fei Li's final ImageNet competition reminds us that the "thankless work" of building a unique dataset is as much at the core of AI research as is building a smart algorithm. Learn More on Quartz

7/ Voice assistants have been laughably awful at interpreting Australian accents, so Amazon creates a job posting to hire Australian linguists who can solve the problem once and for all. Learn More on The New York Times

Where are we going?

Algorithms for Hire

Whether as an interviewer or interviewee, it is a well-known reality that the hiring process is fraught with inconsistency and inherent human bias. The fact that we have not evolved this process is magnified by three key shifts:

• Given the volume of applicants in a global market, companies cannot properly evaluate all incoming job candidates, resulting in filtering processes that are biased and/or random.
• Most U.S. companies take several months to hire, losing out on top talent that is snatched off the market in days or weeks.
• The world of work has become more fluid and complex, making resumes, degrees, and GPAs much less predictive of performance in the dynamic jobs of today.

Finance and marketing are among the industries leveraging huge data sets to make more predictive decisions. So with data collection at historically unprecedented levels, it is time to transform candidate hiring and assessment as well. 

Consider the classic pre-hire assessment: 100+ questions that seem repetitive to the test taker. Yet, to the assessment creator, this is the bare minimum required to make an accurate prediction about future performance, as each new data point decreases the likelihood of a mistake and increases the predictive power of the assessment.

Enter AI. Machine Learning is transforming how data is leveraged in this space by transforming a company’s hiring power as well as the candidate’s experience. As opposed to multiple choice tests, large amounts of valuable data can be gathered from sources like video interviews, coding challenges, and games... Learn More on Machine Learnings

Where else are we going?

How dialogue systems learn

We’re seeing more applications of conversational AI, through in-home speakers, in car assistants and on our phones. These dialogue systems are an interface in which users interact using words. To be effective, dialogue systems need to master language to the extent that they need to understand the users’ sentences but they also need to be able to communicate and generate new sentences.

To achieve these capabilities, the research community has been exploring a two-step process. First, given dialogue history, the system learns to generate sentences that are sensible in this context. If I’m asking several questions about movies playing at my local theatre (films, times, ticket availability), the system needs to remember and use this contextual information... Learn More on Machine Learnings

Links from the community.

"The AI Revolution: The Road to Superintelligence" submitted by Nick Frost. Learn More on waitbutwhy

"Why the future of deep learning depends on finding good data" submitted by Ophir Tanz. Learn More on TechCrunch

"HubSpot acquires Kemvi to bring more AI into its sales and marketing platform" submitted by Sara DeBrule. Learn More on TechCrunch

"Amy now responds to your emails 2x faster" submitted by Stefanie Syman. Learn More on x.ai

"Daisy's Artificial Intelligence Powered Promotional Optimization Delivers Results for North Carolina Based Organic Grocer" submitted by David Hochman. Learn More on PRWeb

"Deep Learning for NLP Best Practices" submitted by Mark Philpot. Learn More on ruder.io

"Meet Gravyty 'First Draft'" submitted by Adam Martel. Learn More on Gravyty

"The Future of Empathetic AI" submitted by Ben Virdee-Chapman. Learn More on Medium

"Polanyi's Paradox" submitted by Abhishek Kothari. Learn More on Medium

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