The artificial intelligence lessons we’ve learned in 2018
(Editor’s note: A version of this interview previously appeared on our blog at OrangeSV.com.)
Artificial intelligence is one of the most lucrative areas for tech talent in Silicon Valley right now, and companies across industries and sectors are looking for every advantage to get ahead. Already in 2018, Orange Silicon Valley has hosted a learning event about AI in human resources operations, discussed the impact of voice-AI interfaces in the enterprise, and assessed GenZ’s readiness to embrace AI in our new GenZ handbook. Next, we’ll delve into a wide array of business applications in our July 11 event “Artificial Intelligence, real business,” which will feature speakers from Nvidia, Microsoft, and other tech companies that are making big bets on AI and machine learning.
As the conversation on AI continues, expect to see more about conversational AI, applications in cybersecurity, and more. To get up to speed ahead of these talks, take a quick look at these takeaways from what we’ve done so far this year:
1. AI will shape management and career-building.
In Orange Silicon Valley’s new handbook address how GenZ will work, we highlighted AI’s emerging role management and human resources: As the use of additional voice detection and language processing tech is more deeply embedded into workflows and workplaces, the collection of data and application of machine learning for talent analytics — perhaps at the team level as well as individuals — will be a big part of how GenZ gets ahead.
2. GenZ will be comfortable with voice AI in the workplace.
As voice assistants get ready to serve offices, we’ve seen experts articulate how primed GenZ is to related to them in a variety of circumstances: SoundHound VP of Product Marketing Mike Zagorsek made the point during a January event at Orange Silicon Valley that GenZ will be the “Voice First” generation, and the data and anecdotal evidence backs this up. A recent Walker Sands survey showed 22% of 18–25-year-olds have an Alexa for Google Home device — and that rate goes up to 46% for 36–45-year-olds.
3. Voice AI’s arrival to the enterprise will reshape office communications.
Comfort with using voice assistants is just the beginning. The technology has the potential to replace existing channels of communication and make work discussions more efficient: [Voicera CPO David] Weiner describes meetings as the biggest productivity killer in the Enterprise — worse than email itself. They see meetings not just as words but as collections of meanings: action items, follow-ups, sentiment. Having recruited from Facebook’s Applied AI group, they are preaching the idea of extracting follow-ups and insights for the team post-meeting. The use cases for mined conversations are potentially rich: summarization, coaching, customer analytics, and hiring interviews are just some examples that make AI’s business case.
4. Significant budgets for machine learning already exist.
At our recent event about innovation in HR departments, Blumberg Capital founder David J. Blumberg noted that there is already a lot of money being spent on AI and AI-related positions: Even if many roles for employees who use AI don’t need to command top-tier salaries, some do, and companies are investing in AI capabilities. Between $26 billion and $39 billion was invested by companies in AI-related technologies in 2016, according to Blumberg, who noted that recent tax law changes in the US have led to an increased appetite for tech such as artificial intelligence and deep learning.
5. AI is not just going to help us understand data; it’s going to help us learn how to use data in better ways.
Writing about his “20/80 Rule of Big Data” on Medium, Orange Silicon Valley IoT Studio Co-Founder Mike Vladimer explained how AI and machine learning will provide tremendous value by understanding how to weigh context and preferences: Conventional products provide incomplete solutions because they are limited by what they can measure and how they use data. A conventional thermometer only measures the temperature right now; it can’t store previous measurements or consider additional factors like weather patterns or personal preferences. A big data “clothing assistant” considers all of those factors. Underlying that big data product are new tools, such as artificial intelligence and machine learning, which allow us to answer even deeper questions.
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Disclaimer: The views and opinions expressed in this article belong to the author and do not necessarily reflect the position or views of Orange or Orange Silicon Valley.
