When Apple’s chief Artificial Intelligence researcher Russ Salakhutdinov announced yesterday the company’s intention to start publishing its AI research, the news generated no small amount of media attention.
After all, the tech giant is known for the strict regime of secrecy imposed on its research by founder Steve Jobs. Until now, Apple had traditionally related to any innovations as trade secrets, to be jealously protected lest they fall into rival hands.
But the latest decision shouldn’t have come as any surprise.
As most commentators noted, from the day Apple decided to delve into the world of AI, this moment was all but inevitable. AI as a science is at an incredibly exciting point, with breakthroughs and innovations occurring, it seems, every other week. No researcher worth his or her salt would agree to muzzle themselves, even for a job at Apple.
As Facebook’s AI director Yann LeCun noted to Business Insider, to do so would amount to committing career suicide:
“…[when] you’re a researcher, you assume that you’re going to publish your work. It’s very important for a scientist because the currency of the career as a scientist is the intellectual impact. So you can’t tell people ‘come work for us but you can’t tell people what you’re doing’ because you basically ruin their career. That’s a big element.”
Indeed, when Salakhutdinov was first head-hunted from the deep learning department at Carnegie Mellon University, it was already clear that his role would be to attract new talent. Lifting Apple’s veil of secrecy was always going to be his major priority in that regard.
Nevertheless, it can be easy to forget that Apple’s culture of secrecy was made for real, calculated business motives. Job’s brainchild hasn’t become the most valuable company in the world by sharing its secrets to success.
So the decision to open up in this way is worth analyzing a little further – particularly as it relates to the application of AI as a business solution.
From Apple AI to B2B marketing: a key to success
Of course, there are some obvious gains to be made for Apple here. Most notably, they are now likely to be included in the “Partnership on AI to Benefit People and Society,” the powerful research coalition founded by fellow tech giants Amazon, Google, Facebook, IBM, and Microsoft. Joining such illustrious fellow innovators can only be good news for Apple.
The fear of missing out on the valuable research of so many industry leaders clearly outweighed Apple’s fear of revealing its own research. In fact, considering how far Apple is lagging in the AI race – playing second fiddle to the likes of Microsoft, Baidu, Google, IBM and Facebook, among others – it almost seems like a no-brainer.
But there’s another motivation behind Apple’s move towards greater openness which is more relevant to the B2B context.
As Leadspace CEO Doug Bewsher pointed out earlier this year, following the much awaited launch of Salesforce Einstein, data quality – more so than the sophistication of any AI algorithm – is what defines success in implementing AI for B2B sales and marketing.
A relatively basic machine-learning algorithm with good data will outperform even a far more advanced deep-learning model, if the latter is working off of bad data.
No, we haven’t come full circle back to the “big data” craze – this is about data quality as much as, or even more than, it is about quantity.
The one thing more important than your company data…
The other caveat to effectively implementing AI is even more basic: your staff.
Regular readers of this blog will already be familiar with the pitfalls of a “black box” AI or predictive analytics models. For AI solutions like predictive analytics to work – particularly at mid market and enterprise-scale companies – it’s not enough to simply deliver an “answer” in the form of predictive scoring or an Ideal Customer Profile. Customers need to have access to – and human input into – the hows and whys. Artificial Intelligence can only supplement your company’s human intelligence – it can’t replace it.
In fact, involving sales and marketing (as well as technical staff) in the process of implementing AI can have other benefits too, such as greater marketing-sales alignment.
Unlike AI research, no one expects you to share your data or the practical expertise of your staff.
This is something which Apple CEO Tim Cook clearly appreciates. Quality data, bright minds and experienced professionals are certainly not things Apple is lacking or lagging behind anyone in. (Expect, perhaps, in the field of AI – a problem which can now be remedied thanks to Salakhutdinov’s announcement.) If his company can combine those strengths with greater AI know-how, it could be well positioned to seize the competitive edge.
The continued, meteoric rise of Artificial Intelligence is inevitable in 2017, and will be for years to come. It isn’t contingent on any one company or experts – those who shut themselves off will ultimately get left behind, as Apple now clearly understands.
For those businesses adopting AI during that period, the emphasis must be on ensuring they are actually ready to adopt such a powerful demand generation tool.
That means something of a return to basics, namely enriching and constantly maintaining the quality of their databases, as well as, of course, preparing themselves organizationally to implement AI.
If you’re interested in what Artificial Intelligence could do for your business, AI-powered predictive analytics is a good place to start.
Or, for more information, contact us for a free demo.
Image from Pixabay | CC0 Public Domain
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