In most instances, AI pilot programs show promising results but then fail to scale. Accenture surveys point to 84 percent of C-suite executives acknowledging that scaling AI is important for future growth, but a whopping 76 percent also admit that they are struggling to do so. The only way to realize the full potential of AI is by scaling it across the enterprise. Unfortunately, some AI teams think only in terms of executing a workable prototype to establish proof-of-concept, or at best transform a department or function. Teams that think enterprise-scale at the design stage can go successfully from pilot to enterprise-scale production. They often build and work on ML-Ops platforms to standardize the ML lifecycle and build a factory line for data preparation, cataloguing, model management, AI assurance, and more. AI technologies demand huge compute and storage capacities, which often only large, sophisticated organizations can afford. Because resources are limited, AI access is privileged in most companies. This compromises performance because fewer minds mean fewer ideas, fewer identified problems, and fewer innovations.
The ultimate test of any model is to test it with every Scrum team and every organization. Since this is not practically feasible, scientists use advanced statistical techniques to draw conclusions about the population from a smaller sample of data from that population. Two things are important here. The first is that the sample must be big enough to reliably distinguish effects from the noise that always exists in data. The second is that the sample must be representative enough of the larger population in order to generalize findings to it. It is easy to understand why. Suppose that you’re tasked with testing the purity of the water in a lake. You can’t feasibly check every drop of water for contaminants. But you can sample some of the water and test it. This sample has to be big enough to detect contaminants and small enough to remain feasible. It's also possible that contaminants are not equally distributed across the lake. So it's a good idea to sample and test a bucket of water at various spots from the lake. This is effectively what happens here.
Today, the corporate world and biometric technology go hand in hand. Companies cannot operate seamlessly without biometrics. Regular security checks just don’t cut it in companies anymore. Since biometric technologies are designed specifically to offer the highest level of security, there is limited to no room when it comes to defrauding these systems. Thus, technologies like ID Document Capture, Selfie Capture, 3D Face Map Creation, etc., are becoming the best way to secure the workplace. Biometric technology allows for specific data collection. It doesn’t just reduce the risk of a data breach but also protects important data in offices. Whether it’s cards, passwords, documents, etc., biometric technology eliminates the need for such hackable security implementations at the workplace. All biometric data like fingerprints, facial mapping, and so on are extremely difficult to replicate. Certain biological characteristics don’t change with time, and that prevents authentication errors. Hence, there’s limited scope for identity replication or mimicry. Customized personal identity access control has become an employee’s right of sorts.
The ability to speed up processes and respond more quickly to a highly dynamic market is the key to survival in today’s competitive business environment. For many large businesses, the ERP system forms a crucial part of the digital core, which is supplemented by best-of-breed applications in areas such as customer experience, supply chain, and asset management. When it comes to digitalisation, organisations will often focus on these applications and the connections between them. However, we often see businesses forget to automate processes in the digital core itself — an oversight that can negatively impact other digitalisation efforts. For example, the ability to analyse demand trends on social media in the customer-focused application can offer valuable insights, but if it takes months for the product data needed to launch a new product variant to be accessed, customer trends are likely to have already moved on. If we look more closely at the process of launching a new product to market, this is a prime example of where digital transformation can be applied to help manufacturers remain agile and respond to market trends more quickly.
Kalay is a network protocol that helps devices easily connect to a software application. In most cases, the protocol is implemented in IoT devices through a software development kit that's typically installed by original equipment manufacturers. That makes tracking devices that use the protocol difficult, the FireEye researchers note. The Kalay protocol is used in a variety of enterprise IoT and connected devices, including security cameras, but also dozens of consumer devices, such as "smart" baby monitors and DVRs, the FireEye report states. "Because the Kalay platform is intended to be used transparently and is bundled as part of the OEM manufacturing process, [FireEye] Mandiant was not able to create a complete list of affected devices and geographic regions," says Dillon Franke, one of the three FireEye researcher who conducted the research on the vulnerability. FireEye's Mandiant Red Team first uncovered the vulnerability in 2020. If exploited, the flaw can allow an attacker to remotely control a vulnerable device, "resulting in the ability to listen to live audio, watch real-time video data and compromise device credentials for further attacks based on exposed device functionality," the security firm reports.
The distributed ledger created using blockchain technology is unlike a traditional network, because it does not have a central authority common in a traditional network structure. Decision-making power usually resides with a central authority, who decides in all aspects of the environment. Access to the network and data is subject to the individual responsible for the environment. The traditional database structure therefore is controlled by power. This is not to say that a traditional network structure is not effective. Certain business functions may best be managed by a central authority. However, such a network structure is not without its challenges. Transactions take time to process and cost money; they are not validated by all parties due to limited network participation, and they are prone to error and vulnerable to hacking. To process transactions in a traditional network structure also requires technical skills. In contrast, the distributed ledger is control by rules, not a central authority. The database is accessible to all the members of the network and installed on all the computers that use the database. Consensus between members is required to add transactions to the database.
Quote for the day:
"Nothing is less productive than to make more efficient what should not be done at all." -- Peter Drucker