In some ways, the algorithms are anticipating tomorrow’s hardware. For example, quantum algorithms are becoming hot because they “allow you to do some of what quantum computers would do if they were available, and these algorithms are coming of age,” said Anthony Scriffignano, chief data scientist for Dun & Bradstreet. Deep belief networks are another hot emerging approach. Scriffignano describes it as “a non-regressive way to modify your goals and objectives while you are still learning — as such, it has characteristics of tomorrow’s neuromorphic computers,” systems geared to mimic the human brain. At Stanford, the DeepDive algorithms developed by Chris Ré have been getting traction. ... “Much of existing data is un- or semi-structured. For example, we can read a datasheet with ease, but it’s hard for a computer to make sense of it.”
Project management should be viewed as a tool that helps organisations to execute designated projects effectively and efficiently. The use of this tool does not automatically guarantee project success. (project success will be discussed in a subsequent issue). However, in preparation for the next issue, I would like you to think about the distinction between project success and project management success. This distinction will provide further insight to the questions: Why are some projects perceived as failures when they have met all the traditional standards of success, namely, completed on time, completed within budget, and meeting all the technical specifications? Why are some projects perceived to be successful when they have failed to meet two important criteria that are traditionally associated with success, namely, not completed on time and not completed within budget?
Edge or device-based processing of AI algorithms is something that has been difficult until now because of the large processing needs, and the limitations on power consumption. Running NVIDIA’s Pascal GPU consumes hundreds of watts, which can be addressed by cooling mechanisms in a data center, but on a mobile or car that would be unthinkable. However, we are beginning to see several trends that suggest edge-based processing for AI algorithms is starting to happen. This is being pushed at one level by the hyperscalars themselves who are aware of privacy concerns, and want to enable real-time device-based AI training or inference. At the same time, startups are also coming up with innovative ideas, while hardware startups are developing custom solutions for embedded AI applications. Both software and hardware approaches are feeding into the edge-based processing for AI.
Data quality processes involve a range of costs, from the cost of data quality software to the resource needed to integrate systems. We recommend that every business carries out a review, prior to implementing new data quality measures. It needs to weigh up the points we looked at in the last section: negative effects of inaction, vs expense of throwing the entire budget at bad data. Additionally, the business needs to look at the way it’s using data, and figure out how to improve management internally. That might mean reducing manual touch points, so there’s less human error. Or retraining staff so they don’t type garbage into fields. Finally, let’s be realistic. The cost of the new data quality process needs to be factored into the business’ budget, like any other production cost.
"Many banks have increasingly leveraged and become dependent on third-party service providers to support key operations within their banks. Over time, consolidation among service providers has resulted in large numbers of banks (becoming) reliant on a small number of service providers," according to the regulator. It added that that can create "concentrated points of failure for certain lines of business or operational functions for a large segment of the banking industry." Banks also could run the risk of falling afoul of multiple new or amended regulations in lending and real estate, because their vendors are not aware of regulatory changes, the OCC said. Banks may rely on outside firms or software to process loan applications, underwrite or close loans, which could open them to challenges in complying with the new regulations.
The challenge for most small or tightly held businesses is that it can take 5-10 years to groom a successor to take over the business. Because of that you can't wait until you need to have a succession plan in place and you can't rely on just one person to be the potential successor. You need to constantly be thinking about, looking for, and grooming candidate successors. I can't tell you the number of businesses I've be involved in where the founder, and the business, just end up being stuck with no option but to liquidate or sell, and often for a huge loss in potential value. If I could give you one piece of advise here it would be to wake up every day asking, "If something happened and I could no longer run this business, how would it survive?" If you can't answer that I wouldn't worry much about waking up because I'd have too much trouble even getting to sleep!
Significantly, CopyCat steals credits earned by legitimate advertisers whenever one of their ads results in an application download. The malware accomplishes this by swapping out the ad company's real referrer ID with a fraudulent one. These credits are ultimately exchanged for revenue. According to Check Point researcher Daniel Padon, this technique has never been seen before, and is more lucrative than traditional ad fraud. "There are many efforts by ad networks to detect and stop fraud from happening and this is actually a... way to do it without being detected," said Padon, in an interview with SC Media. "You have to be on the device itself [and monitoring] device activity to understand that fraud has actually taken place." Otherwise, the ad transaction "will look like a legitimate one from end to end."
The name of the game is speed, and this platform provides real-time analytics for applications like fraud detection in real-time transactional data streams (banks) and real time personalised offers to customers in stores. In the interview, the Actian executive team ran through a number of case studies, where hybrid data management helped run and improve operations. ... The phenomenon here that we see is businesses trying to drive these applications – these pieces of data is now flattened across the organisation – no longer present in one single large repository deep in the enterprise, but they come from a number of different places really spread across the enterprise. It’s now incumbent on the company/companies that claim that want to profit from this information to be able to extract it, process it and analyse it from these multiple sources in multiple formats to really drive some of these insights.
Defining analytics as a “multi-disciplinary approach to deriving insights from data,” he said there are different degrees of analytics, starting with descriptive analytics in which you look at historical data to find out what is going on in an organization, what has happened, and what can be learned from that data. The next step, he noted, is predictive: what will happen? Can I forecast the future? “When we get into the predictive space, analytic techniques become more advanced,” he said. ... “However, AI and machine learning have become such buzzwords that many different things get lumped under them. At SAS we try to delineate these areas clearly and have a good understanding of what we mean by deep learning or AI. My calculator is better at arithmetic than I will ever be, but it’s not AI,” he said.
"There’s definitely a talent shortage of quality information security professionals who are capable of solving emerging problems," says Lee Kushner, president of cybersecurity recruiting firm LJ Kushner & Associates. "It’s not a shortage of general skill or average skill, it’s a shortage of skills that can help companies solve their problems." As the industry starts to look at the problem, it'd best start putting a finer point on the types of skills most in demand rather than fixating on one overarching security deficiency. "The problem is more granular than 'look at all the open jobs,'" says Mike Viscuso, CTO and co-founder of Carbon Black. According to the most recent research, the following specialties and skills are the ones that hiring managers are having the hardest time plugging into their teams.
Quote for the day:
"Leadership is like beauty; it's hard to define, but you know it when you see it." -- Warren Bennis