VCs, Entrepreneurs See Midwestern Cities as Potential Startup Hubs

VCs, Entrepreneurs See Midwestern Cities as Potential Startup Hubs

Once the industrial base of the US, the Midwest has struggled in the high-tech era to capture the talent-driven growth enjoyed by coastal cities like Boston and San Francisco, but the region’s fortunes are changing fast. In the past year or so, a burgeoning Midwestern tech scene has begun attracting more attention from venture capitalists and Silicon Valley giants, with many local startups and big-company expansions focusing on the middle-skill roles for which the tech sector’s demand is insatiable, but that are still in short supply nationwide. These “mid-tech” or “new-collar” jobs are described as a 21st century analog to the factory jobs of the past—and as such, a promising path to revival for the industrial Midwest.

High-tech industries including major international firms have been making some big bets in the region: The Indian IT services and business process outsourcing giant Infosys is planning a sprawling campus near Indianapolis, which aims to create 3,000 new jobs within five years, while the Taiwanese multinational Foxconn Technology Group made a deal with the Wisconsin state government last year to build a display panel factory there, which will see the company invest as much as $10 billion and hire as many as 13,000 people. Several midwestern cities are on the list of finalists in the competition to host Amazon’s second headquarters, though Detroit, for example, didn’t make the cut, partly due to a lack of readily available talent.

Yet “mid-tech” companies and regional outposts of tech giants are just one side of the Midwest’s high-tech renaissance. Over the weekend, VentureBeat reporter Anna Hensel took a look at the growing community of AI and machine learning startups in the heartland:

“The real benefit of artificial intelligence is the application to traditional problems and products that the world needs, and the really successful companies have that domain knowledge that they can understand how to apply this technology,” [Chris Olsen, a partner at Columbus, Ohio VC firm Drive Capital,] told VentureBeat in a phone interview. “We see more of those domain experts in these industries [with] massive chunks of GDP that exist here in the Midwest.”

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LinkedIn Courts Smaller Businesses with Candidate Matching, Job Targeting Features

LinkedIn Courts Smaller Businesses with Candidate Matching, Job Targeting Features

LinkedIn’s latest round of updates to its job posting tool includes features designed to help smaller organizations without dedicated recruiting functions to more easily source and track qualified candidates, Monica Lewis, Head of Product at Linkedin Jobs, announced on the professional networking platform’s Talent Blog last week:

Now, when you post a job on LinkedIn, these new features will work to deliver a pool of relevant candidates who you can’t find anywhere else. … Once you’ve posted a job on LinkedIn, Recommended Matches will scour our network to find candidates who have the experience and skills you’re looking for. And, most of these candidates are exclusively on LinkedIn: 57% of our users in the US did not visit the top three job boards last month.

We put these potential candidates right in front of you, giving you access to their full profiles. In one click, you can indicate if you’re interested in a candidate and start a conversation with them about the job opportunity. Based on how you rate candidates, our algorithm learns your preferences and delivers increasingly relevant candidates.

LinkedIn, which is owned by Microsoft, has also reconfigured its matching algorithm and given organizations the ability to add their own targeting preferences, giving them more control over who sees a job post. The update also makes it easier for users to keep track of candidates they are considering or wish to contact.

The new features are deliberately designed to encourage smaller and medium-sized enterprises to use LinkedIn as a job board. ERE’s Joel Cheesman calls this “a smart move at the right time”:

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Google Updates Hire With New AI Tools

Google Updates Hire With New AI Tools

In the latest release of its applicant tracking system, Google Hire, the tech giant has added three new features that use Google AI to reduce repetitive and time-consuming tasks, Senior Project Manager Berit Hoffmann wrote in a blog post announcing the update on Tuesday. From measuring user activity, Hoffmann noted, Google determined that Hire has already cut down the amount of time recruiters spend on common tasks like reviewing applications or scheduling interviews by up to 84 percent; the new features are intended to simplify the process even further. The new features include:

  • Interview scheduling: When a user wants to schedule an interview with a candidate, Hire now uses AI to automatically suggest interviewers and time slots. The AI will also alert the recruiter if an interviewer cancels at the last minute and recommend a replacement. “This means hiring teams can invest time in preparing for interviews and building relationships with candidates instead of scheduling rooms and checking calendars,” Hoffmann writes.
  • Résumé highlighting: To reduce the amount of time recruiters spend scanning candidates’ résumés for key terms, Hire will now automatically analyze the terms in a job description or search query and automatically highlight them on résumés. Google introduced this feature after observing that users were frequently using “Ctrl+F” to search for the right skills—an easily automated process.
  • One-click candidate phone calls: The final feature is designed to make it easier for recruiters to reach out to candidates by phone with a click-to-call functionality. Users can call a candidate simply by clicking on their phone number, while the system will automatically log calls to keep track of which candidates have already been contacted by whom.

Google Hire was launched last July as part of the company’s G Suite of enterprise software offerings, but only for US businesses with under 1,000 employees: a deliberate decision to help level the playing field for small and mid-sized businesses that lack the dedicated recruiting resources and bespoke applicant tracking systems of their larger peers. In April, Google Hire expanded into the sourcing realm with a new “candidate discovery” feature that allows users to more easily keep track of past candidates who might be good fits for newly open positions, along with more advanced search capability to provide more relevant results based on what recruiters are actually looking for.

ERE’s Joel Cheesman sees these new AI enhancements as further evidence of the massive edge large tech companies like Google, Facebook, and Microsoft enjoy in the new era of online recruiting: “Deep integration into technologies that so many people already use daily, such as Gmail and Google Calendar, must drive traditional recruiting technology solutions crazy. Build all the Chrome extensions you want, but nothing’s ever going to be better than the stuff Google has baked itself.”

 

Several Companies Developing Tools to Address Algorithmic Bias

Several Companies Developing Tools to Address Algorithmic Bias

As machine learning algorithms are called upon to make more decisions for organizations, including talent decisions like recruiting and assessment, it’s becoming even more crucial to make sure that the performance of these algorithms is regularly monitored and reviewed just like the performance of an employee. While automation has been held up as a way to eliminate errors of human judgment from bias-prone processes like hiring, in reality, algorithms are only as good as the data from which they learn, and if that data contains biases, the algorithm will learn to emulate those biases.

The risk of algorithmic bias is a matter of pressing concern for organizations taking the leap into AI- and machine learning-enhanced HR processes. The most straightforward solution to algorithmic bias is to rigorously scrutinize the data you are feeding your algorithm and develop checks against biases that might arise based on past practices. Diversifying the teams that design and deploy these algorithms can help ensure that the organization is sensitive to the biases that might arise. As large technology companies make massive investments in these emerging technologies, they are also becoming aware of these challenges and looking for technological solutions to the problem as well. At Fast Company last week, Adele Peters took a look at Accenture’s new Fairness Tool, a program “designed to quickly identify and then help fix problems in algorithms”:

The tool uses statistical methods to identify when groups of people are treated unfairly by an algorithm–defining unfairness as predictive parity, meaning that the algorithm is equally likely to be correct or incorrect for each group. “In the past, we have found models that are highly accurate overall, but when you look at how that error breaks down over subgroups, you’ll see a huge difference between how correct the model is for, say, a white man versus a black woman,” [Rumman Chowdhury, Accenture’s global responsible AI lead,] says.

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People Are More Open to Algorithmic Judgment Than You Might Think

People Are More Open to Algorithmic Judgment Than You Might Think

When it comes to making judgments based on large data sets, machines are often superior to humans, but many business leaders remain skeptical of the guidance produced by their organizations’ data analytics programs, particularly when it comes to talent analytics. That skepticism derives largely from doubts about the quality of the data the organization is collecting, but there is also a natural tendency among people who make strategic decisions for a living to reject the notion that an algorithm could do parts of their job as well as or better than they can.

While this may be true of executives and high-level professionals, some recent research suggests that most people are actually comfortable with the decisions algorithms make and even more trusting of them than of judgments made by humans. A new study from the Harvard Business School, led by post-doctoral fellow Jennifer M. Logg, finds that “lay people adhere more to advice when they think it comes from an algorithm than from a person”:

People showed this sort of algorithm appreciation when making numeric estimates about a visual stimulus (Experiment 1A) and forecasts about the popularity of songs and romantic matches (Experiments 1B and 1C). Yet, researchers predicted the opposite result (Experiment 1D). Algorithm appreciation persisted when advice appeared jointly or separately (Experiment 2). However, algorithm appreciation waned when people chose between an algorithm’s estimate and their own (versus an external advisor’s—Experiment 3) and they had expertise in forecasting (Experiment 4). Paradoxically, experienced professionals, who make forecasts on a regular basis, relied less on algorithmic advice than lay people did, which hurt their accuracy.

Our colleagues here at Gartner have also investigated consumers’ attitudes toward AI and found that these attitudes are more welcoming than conventional wisdom might lead you to believe. The 2018 Gartner Consumer AI Perceptions Study found that overall, consumers are not skeptical of the potential usefulness of AI, though they do have some concerns about its impact on their skills, social relationships, and privacy. The study was conducted online during January and February 2018 among 4,019 respondents in the US and UK. Respondents ranged in age from 18 through 74 years old, with quotas and weighting applied for age, gender, region, and income.

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Automakers’ AI Startups Expand, Pulling Talent from Silicon Valley

Automakers’ AI Startups Expand, Pulling Talent from Silicon Valley

In a sign of how serious the US automobile industry is about beating Silicon Valley to marketable self-driving cars, several AI startups working on this technology have multiplied in size since being bought by legacy automakers over the past two years, Christina Rogers reports at the Wall Street Journal. Argo AI, an artificial intelligence startup founded in Pittsburgh by former top engineers from the self-driving vehicle divisions of Alphabet and Uber, had fewer than a dozen employees when Ford Motor Company bought a $1 billion majority stake in it early last year. Today, it has 330 employees, including a number of software engineers and robotics researchers formerly employed by major tech companies like Apple and Uber.

Argo attracted these employees with an equity offer for new hires, which big tech companies can’t offer, Chief Executive Bryan Salesky tells Rogers. This ensures that each employee is “able to benefit from the upside being created in a direct way”—a potentially massive payoff given that Argo is helping Ford prepare to bring a fully autonomous car to market in 2021 while also developing a system it can sell to other companies. Being backed by a major company, but not owned outright or micromanaged by that company, gives Argo the agility to continue operating like a tech startup, while also benefitting from Ford’s economies of scale to manufacture and market the products it designs.

General Motors also bought a self-driving car startup, Cruise Automation, as part of a series of high-tech investments in 2016 that signaled the company’s intent to develop autonomous vehicles and made it a more attractive employer for tech talent. San Francisco-based Cruise, which GM also spent $1 billion to acquire, has staffed up to 740 employees and got $2.25 billion investment from Japan’s SoftBank Group last month, Rogers adds. Japanese automakers like Toyota and Nissan are also investing in the development of robotics and autonomous driving technology.

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Google, Coursera Launch Machine Learning Course Series

Google, Coursera Launch Machine Learning Course Series

Google and the online learning platform Coursera are launching a five-course machine learning specialization to teach developers how to build machine learning models using the TensorFlow framework, Frederic Lardinois reports at TechCrunch:

The new specialization, called “Machine Learning with TensorFlow on Google Cloud Platform,” has students build real-world machine learning models. It takes them from setting up their environment to learning how to create and sanitize datasets to writing distributed models in TensorFlow, improving the accuracy of those models and tuning them to find the right parameters.

As Google’s Big Data and Machine Learning Tech Lead Lak Lakshmanan told me, his team heard that students and companies really liked the original machine learning course but wanted an option to dig deeper into the material. Students wanted to know not just how to build a basic model but also how to then use it in production in the cloud, for example, or how to build the data pipeline for it and figure out how to tune the parameters to get better results. …

It’s worth noting that these courses expect that you are already a somewhat competent programmer. While it has gotten much easier to start with machine learning thanks to new frameworks like TensorFlow, this is still an advanced skill.

The new series is a continuation of Google’s longstanding partnership with Coursera, through which the tech giant went public with its internal IT support training curriculum earlier this year.

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