The updates are “timely, if arguably overdue, particularly for businesses outside of the U.S.,” said Alan Pelz-Sharpe, founder and principal analyst at Deep Analysis. “Governments worldwide are starting to bring in more regulations and guidelines for the ethical and lawful use of AI and data and clearly there is mounting public concerns,” he continued.
Core for Federated Compliance might not be as glamorous as massive new cloud services, said Alan Pelz-Sharpe, founder of Deep Analysis, but he predicted it will be widely used. “Records management doesn’t get the headlines — it’s not very exciting, but it’s important,” Pelz-Sharpe said. “That’s quite an announcement.”
“The KKR move is probably the most important strategic move Box has made since it IPO’d. KKR doesn’t just bring a lot of money to the deal, it gives Box the ability to shake off some naysayers and invest in further acquisitions,” Pelz-Sharpe told me, adding “Box is no longer a startup its a rapidly maturing company and organic growth will only take you so far. Inorganic growth is what will take Box to the next level.”
Artificial intelligence has made unchecked advances in an unregulated environment, and while technically impressive, many of these advances impinge upon the public’s privacy and raise ethical concerns, he continued. Governments and the public are realizing that needs to change, however, Pelz-Sharpe said. “Questions such as whether an AI system is biased or explainable, for example, and who to hold responsible if they are biased and discriminate or simply get things wrong are being asked,” Pelz-Sharpe said. Even so, he added that AI technology companies are motivated by growth, which can be at odds with designing AI systems that respect human rights, fairness and diversity.
Typically, nonprofits are at a technical disadvantage, often running dated and cheap or free systems, said Alan Pelz-Sharpe, founder and principal analyst at Deep Analysis. “As [machine learning and AI] take hold over the coming years, that gap will grow further,” he said. “So, there is no doubt that there is a need to help them to bridge that gap. The merger in that regard makes a great deal of sense.” Still, Pelz-Sharpe noted, machine learning and AI are increasingly bundled with and embedded in standard business applications; Google and Microsoft, for example, build the technologies into their office suites.
Google’s partnership with Automation Anywhere makes sense, said Alan Pelz-Sharpe, founder and principal analyst at Deep Analysis. One way to look at RPA is the reincarnation of macros, he said; it’s the low-code, no-code ability to automate repetitive tasks. For Google, the partnership with Automation Anywhere will lead to a focus on common back-office administrative tasks, before building out bots that can tackle similar tasks within specific industry verticals, he said.
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“I think what is interesting here is that Box is going to integrate SignRequest and bundle it as part of the standard service. That’s what really caught my eye as the challenge with e-sig is that it’s typically a separate product and so gets limited use. They bought it partly in response to Dropbox, but it was a hole that needed fixing regardless so would have done so anyway,” Pelz-Sharpe explained.
In general, bias in an AI system largely results from the data the system is trained on. The model itself “does not come with built-in discrimination, it comes as a blank canvas of sorts that learns from and with you,” said Alan Pelz-Sharpe, founder and principal analyst at Deep Analysis. Yet, many vendors sell pre-trained models as a way to save their clients the time and know-how it normally takes to train a model. That’s ordinarily uncontroversial if the model is used to, say, detect the difference between an invoice and a purchase order, Pelz-Sharpe continued.