With AI gaining momentum across multiple industries, its efficient use largely depends on businesses' necessity and opportunity to introduce innovation. When trying to understand whether it's a fit-for-purpose improvement, two criteria matter ― the nature of tasks and the cost of an error. First, automation is not always the best match for tasks that demand compromising, setting priorities or emotion-based decision-making. However, if your company collects, stores and processes big data, AI may become your first choice; handling huge data volumes at a high pace can streamline a business model. Second, human intelligence is the only way out (at least today) when it comes to making strategic choices like planning further guides for business development, as even a single mistake may lead to decreasing revenues or brand image deterioration. ... Data inside of a neural network is divided into three sectors. A training set ― the kit of input images of, for instance, printed circuit boards in which your neural network should identify defects, thus learning. A development set ― a new set for tuning your network depending on how well it performs on this set.
Manually provisioning or registering a certificate at the right time for the right purpose is an incredibly time-intensive task. Merely deploying an SSL certificate on just one server could take up to 2 hours! And that’s just the beginning – subtasks such as documenting each certificate’s location and purpose, configuring certificates according to a myriad of endpoint devices and varying operating systems, and then confirming that each performs correctly adds to the required time and effort. Today’s enterprises need to be quick-moving and agile to keep up with constant flux and rapid change. Beyond time saved, automated deployment means reduced human-error and increased reliability and consistency. ... Certificates include the requirements and policies that enterprises use to define trust within their organization, extending the security of using only highly trusted key architectures. To ensure a certificate is always in its best possible state, organizations need to be able to quickly and efficiently revoke and replace certificates on demand and without a hefty time-intensive process. Spending 2+ hours per certificate is unreasonable: it needs to happen seamlessly and at scale.
First, it’s important to understand what bot operators are attempting to accomplish. Are they trying to deplete inventory from your site? Scrape prices to better compete? Test stolen credentials to commit fraud? By understanding the full impact bots have on your business, you can make sure the solution puts an end to your specific problems. For example, many solutions are architected to require multiple requests before detecting a bot – if so, it’s not designed to effectively stop scraping and account takeover attempts that quickly move ‘in and out.’ History has shown that attackers will adapt to your defenses. A successful bot mitigation solution has to be effective immediately, stopping new bots and never seen before attack methods. It must also stand the test of time by stopping bots months and years later. You should ask what steps are being taken towards long-term efficacy, such as deterring reverse engineering and R&D to detect new automated threats. You should seek as little configuration, maintenance and support as possible. Does the solution make your life easier or not?
Most companies like to call a range of job roles data scientists when in reality, it could be machine learning engineer, big data developer, business intelligence analyst, data engineer, and so on. Recruiting for data scientists’ role but assigning tasks that do not sync with expectations is a big turn-off for candidates, which may lead to them quitting the job. Companies often put them in roles where, for example, only a data analyst is required. It quickly demotivates the data scientist and erodes their skill sets. The companies should be transparent on roles and responsibilities and the kind of project engagements the candidates will have. Setting the right expectation is crucial. Data science is quite popular, and many companies tend to create data science roles without knowing how data science can help the organisation. In such companies, the data scientists will have no clue as to what they are expected to do because the companies have not defined the roles in the first place. The confusion can lead to demotivation and, finally, result in the quitting or firing of the data scientist.
Private 5G? Has your company set aside funding for spectrum auctions? You’re bidding against big telcos who have spent billions, but think big. Anyway, you could use public spectrum or shared spectrum, right? Of course, you’d still need to build your own network, including towers and radios, wherever you expect to use that spectrum. Maybe the billions for spectrum wasn’t the big financial issue after all. Once you’ve deployed, everything will be great as long as nobody else on the spectrum plays dirty. Sell that to the CFO. There are companies that could justify private 5G, but it’s not a mainstream opportunity. Then there’s IoT. In pure marketing terms, it makes sense to turn devices into cellular customers when you start depleting the market among humans, but do we really think people or companies are going to pay for sensor connection via 5G on a large scale, when we already connect sensors in other ways (like Wi-Fi) for free? How many “things” have we managed to “internet” without 5G, and why wouldn’t those old ways continue to work, without adding 5G “thing-plans” to “family plans?”
Other industry experts agree on the need for energy companies to improve their cyber-security defenses as they begin to rely more heavily on digital technologies such as AI and cloud computing in their operations. “Industrial cyber has become the new risk frontier and in particular, the energy vertical is the most attacked infrastructure vertical,” said Leo Simonovich, global head of Industrial Cyber and Digital Security at Siemens. “The number of attacks is increasing and the sophistication is increasing.” He said an attack against a piece of critical infrastructure such as a power plant could lead to a temporary loss of power, total shutdown of operations or worse, a public safety incident. Siemens, he said, works with its customers to shore up their cyber defenses, “using next-generation built-for-purpose technologies powered by AI to stay ahead of attackers.” Many energy companies are beginning to adopt AI — which mimics human intelligence by analyzing data in order to make decisions — to stay ahead of the cybercriminals and foreign government-backed hackers.
At present, Khajetoorians and the team have discovered how to connect these cobalt atoms into tailored networks. This, it is believed, can mimic behavior of a model, which is like a human brain. These efforts are centered around the attempts to make artificial intelligence a possibility at a high level of existence. Thus, the researchers have thought of constructing on black phosphorous a network of cobalt atoms. Using this method, it has been claimed that one could design a material that stores and processes information like that of a human brain. One that adapts itself based on the input. The researchers plan to scale up the entire system and come up with a large network of atoms. They also aim to dive into this quantum material that can be of applicability. Eventually, the AI engineers can construct a real machine from the quantum material. Also, they can build a self-learning computing device that turns out to be more energy efficient and smaller in size than today’s technology. But it requires to understand how quantum brain works. This is still a mystery. However, the destination seems nearby.
The relationship between CIO and CISO can be fraught with friction, but Angelic Gibson and Christina Quaine, CIO and CISO of B2B payment service AvidXchange, represent a new emerging dynamic between IT and security, one that relies on open communication, a commitment to diversity, and a strong bond between IT leadership. It’s little secret that security has become a board-level priority for organizations across all industries, with organizations taking risk-adverse approaches to IT to stay ahead of security, compliance, and risk management issues. As security and its relationship to IT grows more complex, a solid relationship between CIOs and their CISO counterparts will be critical to delivering quality services, products, software, and hardware to customers.[ Looking to get ahead in tech? “It’s all about building trust with our business partners that we’re providing a service to, with our customers, and with one another,” says Quaine, who heads up the cybersecurity portion of the partnership. “The trust aspect is huge for me — it’s really around doing what you say you’ll do and committing to that. ..."
Industry clouds are of interest because of their potential to create value for both customers and public cloud providers. Established companies in industries feeling the sting of competition from cloud-native disrupters are especially good prospects for these types of solutions. For these companies, moving their core business applications to general-purpose public clouds can be challenging because they often rely on homegrown legacy applications or industry-specific software designed for on-premise data centers. These companies face a difficult choice. Simply “lifting and shifting” applications to the cloud could result in sub-optimal performance. Yet rewriting or optimizing them for the cloud would be time consuming and costly. Industry clouds have the potential to accelerate and take the risk out of their cloud migrations. An essential component of an industry cloud is that it must address the specific requirements of the industry it is designed to serve. For example, healthcare providers place a high priority on improving the patient experience but also require high levels of security, data protection, and privacy. These are necessary to demonstrate compliance with Health Insurance Portability and Accountability Act (HIPAA) regulations.
Algorithms can currently be tasked with making or informing decisions that are deemed "high-risk" by the TUC; for example, AI models can be used to determine which employees should be made redundant. Automated absence management systems were also flagged, based on examples of the technology wrongfully concluding that employees were absent from work, which incorrectly triggered performance processes. One of the most compelling examples is that of AI tools being used in the first stages of the hiring process for new jobs, where algorithms can be used to scrape CVs for key information and sometimes undertake background checks to analyze candidate data. In a telling illustration of how things might go wrong, Amazon's attempts to deploy this type of technology were scrapped after it was found that the model discriminated against women's CVs. According to the TUC, if left unchecked AI could, therefore, lead to greater discrimination in high-impact decisions, such as hiring and firing employees. The issue is not UK-specific. Across the Atlantic, Julia Stoyanovich, professor at NYU and founding director of the Center for Responsible AI, has been calling for more stringent oversight of AI models in hiring processes for many years.
Quote for the day:
"Every time you are tempted to react in the same old way, ask if you want to be a prisoner of the past or a pioneer of the future." -- Chopra