I have an academic background via a PhD in statistics and have been working with mostly digital data for the past 10 years. Almost half of that was focused on digital advertising, first in Operam where we optimized advertising for Hollywood studios using data for blockbusters such as Deadpool 2 and Kingsmen 2, or Oscar winning titles such as Shape of Water. We did some really cool projects such as analyzing movie scripts as an input into the marketing pipeline. Later on I worked on an Operam spin off called Panoramic that focused on the marketing reporting part. I have then rediscovered my passion for augmented analytics during these projects and am currently working on the Reveal product by Adverity that aims to surface insights automagically to marketers. I have also been engaged in both technical and business communities giving talks and organising events. For me the most interesting part of what I do is take the sophisticated outputs of Data Science teams and translate them into something that empowers business users.<\/p>\n
Artificial Intelligence is a very wide term, but when it comes to the day to day of my team, we employ various techniques coming from both modern approaches as well as traditional statistics to automating the search for a needle in a haystack for our end users.<\/p>\n
I think that especially with artificial intelligence in marketing there is still a significant effort between the recommendation and the action. Sure we can optimize budgets or bids automatically, there\u2019s already automated analysis of images\/creatives, but I haven\u2019t yet seen a tool that would make the decision which one is the better course of action. I am not saying it\u2019s impossible, but AI currently focuses on solving partial problems (since most algorithms are in fact idiot savants) and needs human oversight to piece these things together.<\/p>\n
I am pretty conservative with my information depending on the platform<\/a>. No\u00a0information out there is perfectly safe. The reason for the lack of safety is how\u00a0interconnected everything is and it\u2019s close to impossible to keep track of all the\u00a0possible entry points. A good example is what happened to Maerks where\u00a0their whole system was wiped and the worm was thought by some to have\u00a0 got in\u00a0through an accounting software one of their Ukrainian subsidiaries was using.<\/p>\n I think it\u2019s about finding the right balance. I\u2019ve already mentioned that you can\u2019t preemptively protect all possible vectors of attack<\/a>. At the same time this doesn\u2019t mean that nothing should be done. Every company should have some precautions in place and a recovery plan handy. We have heard ad nauseum how the end user is the weakest link, but I\u2019ve also recently listened to a podcast that conveyed an interesting message – if you have a company of 300 people with a small team of security professional, it\u2019s easier for the professionals to set up precautions so that you can\u2019t gain access\u00a0 to the whole system through a receptionist\u2019s account as opposed to expecting to perfectly educate every employee. People that deal with business have their deadlines and issues to worry about, they\u2019ll never be able to do everything perfectly. This appeals to me personally. I am using a password manager as well as am pretty vary when it comes to scams and phishing, yet I\u2019ve had my credit card info stolen because one of the subscription services I used was compromised. How do I prevent that as a user? Is my own security perfect? Of course not, but the most high impact events came through a business I trusted.<\/p>\nIn your opinion, how could cybercrime be restricted? What kind of measures can be taken by the user?<\/h3>\n
What would the future of AI and Cyber Security look like, in your personal opinion?<\/h3>\n