“As 5G continues to roll out globally, everything and everyone will become more connected than ever,” said Stan Lowe, global CISO at Zscaler. “IoT devices in the streets and in the home will all become connected with 5G. Our Alexa, our Google Home, our car and practically everything else will be constantly harvesting data and forwarding it to corporations for marketing purposes and to build your digital profile. ... “It is no secret that up-and-coming companies innovate at a faster rate than governments can introduce regulations, such as GDPR,” he said. “With laws and governmental bodies usually about five to six years behind the innovators, who are constantly innovating on ways to use the data that they harvest, the onus is on these companies to use the data they collect in a safe, fair and ethical way. “Ultimately, our data is a tradeable commodity, and corporations have a lot of power when it comes to how they use the data they collect. ...”
Requirements management is the process of gathering, validating, and tracking the requirements that end users have for a program. But if mismanaged, this process can cause software projects to go over budget, face delays, or fail entirely. Using AI, digital assistants can analyze requirements documents, find ambiguities and inconsistencies, and offer improvements. Powered by natural language processing, these tools can detect such issues as incomplete requirements, immeasurable quantification (missing units or tolerances), compound requirements, and escape clauses. Companies using such tools have reportedly been able to reduce their requirements review time by more than 50%, according to Deloitte. As developers type, AI-powered code completion tools can serve up recommendations for finishing lines of code. Some tools can even display a list of usable code snippets based on relevance. AI-powered code-review tools can understand the intent of the code and look for common mistakes, thereby detecting bugs and suggesting code changes. Video game company Ubisoft says the use of machine learning is helping it catch 70% of bugs prior to testing.
AutoML focuses on automating each part of the machine learning (ML) work process to increase effectiveness and democratize machine learning so that non-specialists can apply machine learning to their issues effortlessly. While AutoML includes the automation of a wide scope of problems related with ETL (extract, transform, load), model training, and model development, the issue of hyperparameter enhancement is a core focus of AutoML. This issue includes configuring the internal settings that govern the conduct of an ML model/algorithm so as to restore a top-notch prescient model. Creating neural network models frequently requires noteworthy architecture engineering. You can sometimes get by with transfer learning, yet if you truly need the most ideal performance it’s generally best to structure your very own network. This requires particular skills(read: costly from a business point of view) and is challenging in general; we may not know the cutoff points of the present cutting edge methods! It’s a ton of experimentation and the experimentation itself is tedious and costly.
The LoRaWAN protocol defines two layers of security: one at the network level and another at the application level, researchers described in the report. The network-level security ensures the authenticity of the device in the network, providing integrity between the device and the network server, they wrote. The application-layer security is responsible for confidentiality with end-to-end encryption between the device and the application server, preventing third parties from accessing the application data being transmitted. Each layer of protection depends on the security of two encryption keys–the Network Session Key (NwkSKey) and the Application Session Key (AppSKey), both of which are 128 bits long. These keys are “the source of the network’s only security mechanism, encryption,” and thus, once cracked, basically give hackers an open invitation to the devices and networks being protected by them, researchers noted.
"It's clear that cybercrime continues to grow as an issue for CEOs around the world, meaning that for many, the threat to their margins, their brands and even their continued existence from cyber attacks is no longer an abstract risk that can be ignored," said Richard Horne, cybersecurity chair at PwC. "Criminals are becoming more adept at monetizing their breaches, with a sharp rise in ransomware attacks this last year. They can have a devastating impact on the organisations they hit, as seen in many high-profile cases". The boardroom itself isn't immune to cyber crime as attackers will target executives - and the PwC report found that almost half of CEOs are taking action to make themselves less vulnerable to cyber attacks. It said 48 per cent CEOs surveyed said the risk of cyber attacks had caused them to alter their own personal digital behavior, such as deleting social media accounts or virtual assistant applications or requesting a company to delete their data Social media accounts could potentially be targeted by criminals as a means of gaining access to personal information about victims, while there have been privacy concerns about virtual assistants and their ability to enable unwanted eavesdropping.
Delegating that low-level work to a machine provides more time for the things that matter. “Machines will elevate the manager’s experience,” says Emily He, senior vice president of Human Capital Management at Oracle. “People see the difference between artificial intelligence and human intelligence, and they want from their managers things that machines can’t provide—things like empathy, personalized coaching, and career advice.” Imagine a workplace where managers, unencumbered by work that machines can do, can focus on people. “AI and machine learning are going to bring humanity back to the workplace,” says He. “[In] the last hundred years the advance of technology has made the workplace less human because the interface with technology has not been very natural. With AI, humans can go back to what is distinctly human and what they enjoy doing, which is to connect with each other, work on projects together, and generate new ideas.” Managers need to be prepared for this new world that will demand effective leadership.
One of the most difficult questions we must address is how to overcome bias, particularly the unintentional kind. Let’s consider one potential application for AI: criminal justice. By removing prejudices that contribute to racial and demographic disparities, we can create systems that produce more uniform sentencing standards. Yet, programming such a system still requires weighting countless factors to determine appropriate outcomes. It is a human who must program the AI, and a person’s worldview will shape how they program machines to learn. That’s just one reason why enterprises developing AI must consider workforce diversity and put in place best practices and control for both intentional and inherent bias. This leads back to transparency. A computer can make a highly complex decision in an instant, but will we have confidence that it’s making a just one? Whether a machine is determining a jail sentence, or approving a loan, or deciding who is admitted to a college, how do we explain how those choices were made? And how do we make sure the factors that went into that algorithm are understandable for the average person?
“With the ability to span across multiple industries, it ensures products can be traced, authenticated and verified on digital ledgers. In the pharmaceutical industry, organisations can apply blockchain technologies to ensure tailored drugs are delivered to the right person. By utilising a secure IoT platform to make sure medications are the right quality and don’t fail during the supply process, which can ultimately affect the efficacy when taken by the patient. “Through blockchain, these companies are able to verify where their product has travelled, and which components have been added at each transition point. In industries where each product can use components from tens or even hundreds of companies at one time, blockchain technologies ensure that the whole supply chain is more transparent, accountable and secure.” ... There are challenges, however, as Akber Datoo — CEO of D2 Legal Technology — highlights.
As advances and innovation around Facial Recognition technology continues to evolve even more, one of the latest trends come from CyberLink's FaceMe® AI Facial Recognition engine integrated into Vivotek's IP surveillance solutions of network cameras and back-end video management software. This integration enable security operators to receive accurate Facial Recognition alerts based on both blacklists and whitelists. According to Dr. Jau Huang, CyberLink's Founder and CEO, "the demand for Facial Recognition is booming, driven by the latest IoT and AIoT innovations, and are enabling a wide array of scenarios across industries such as security, home, public safety, retail, banking, and more." He says that each application is dependent on the performance of the cameras used to capture faces and by integrating FaceMe into Vivotek's surveillance devices it is possible to bring accurate and reliable new solutions into the market.
“Data has never been less secure,” said Privafy co-founder and CEO Guru Pai. “Solutions developed by the networking industry to protect data are rapidly becoming obsolete for today’s cloud-and mobile-based workloads. “Also, technologies such as SD-WAN and cloud-based point solutions focus more on cost reductions, but don’t address the underlying security vulnerabilities to sufficiently protect internet-reliant businesses. Privafy was purpose-built to secure data in today’s modern world. We have democratised internet security to protect data in a way that is easier to deploy and far more economical for any-sized enterprise, regardless of where or how it works.” Pai cited a Gartner research document, The future of network security is in the cloud, which noted that digital business transformation inverts network and security service design patterns, shifting the focus to the identity of the user and/or device, and not the datacentre. The report said the idea of the legacy datacentre as the hub of business network and network security architecture was obsolete and had become “an inhibitor to the needs of digital business”.
Quote for the day:
"Without growth, organizations struggle to add talented people. Without talented people, organizations struggle to grow." -- Ray Attiyah