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CIOs: Focus on applying AI and machine learning, not defining it

For CIOs and CTOs, asking which computing approaches add up to artificial intelligence and which are simply automation or BI is probably not a very useful question. The better question to think about: Do the latest developments in AI and machine learning provide a step change for solving problems and building new products or processes?

David Gledhill, group CIO and head of technology and operations at DBS Bank in Singapore, put it this way: “There’s a continuum. And moving along that continuum is what we care about,” he said. “We’ll leave it to the philosophers to determine what intelligence is.”

Gledhill made his point during a panel discussion at the recent MIT Sloan CIO Symposium. Moderator Michael Schrage, a research fellow at the MIT Sloan School of Management’s Initiative on the Digital Economy, asked about the amorphous definition of artificial intelligence that, today, often includes advanced statistical analysis, predictive modeling and algorithms.

“Do you think we’re falsely aggregating all of these things or is that false aggregation the real truth of what AI and [machine learning] is going to look like,” he asked.

It’s a blurry line, Gledhill said, adding that’s why he shirks from defining the term in the first place. But, more importantly, ontological discussions can distract from the bigger picture — the actual business value cutting-edge technologies, be they AI and machine learning or BI, can create.

To drive the point home, he shared a quick rule of thumb with the audience. “Just the same way that we have the Turing test,” he said, referencing the classical method for testing a machine’s intelligence, “I have this kind of car park barrier test for AI.”

The automatic security gate that lets you in and out of a garage or parking lot works without human intervention: A car comes out, and the barrier autonomously goes up. But, Gledhill asked, is it AI? “Well, no,” he said, answering his own question.

“Because it’s rule-based,” Schrage interrupted.

“Rule-based, yeah,” Gledhill said. “But who cares if it’s AI or not.”

Gledhill said he uses his toolbox, which happens to include artificial intelligence technologies, to solve the problems in front of him. “I know I’ve got a set of tools and a set of algorithms that I can apply to problems and create solutions,” he said. Whether they’re labeled AI and machine learning is moot.

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What do you think is more secure -- a packet-filtering or a proxy firewall? Why?
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This is an open ended question as it depends on the situation, mostly a better way is to use proxy firewall but not to disregard the packet filters as it do its job great. With time and new adaptability it has been seen and increase in the efficiency of packet filters but yet still are vulnerable to attacks on the other hand the proxy firewall has a tradeoff by having a higher horsepower to process request and to allow data in the network with memory usage for a system.

It depends but for me in situation which I have implemented are mostly proxy firewalls. As the company pay for the system and want their data to be safe and secure let them incur the cost afterall
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