There is a strong push for AI to reach into the realm of human-like understanding. Leaning on the paradigm defined by Daniel Kahneman in his book, Thinking, Fast and Slow, Yoshua Bengio equates the capabilities of contemporary DL to what he characterizes as “System 1” — intuitive, fast, unconscious, habitual, and largely resolved. In contrast, he stipulates that the next challenge for AI systems lies in implementing the capabilities of “System 2” — slow, logical, sequential, conscious, and algorithmic, such as the capabilities needed in planning and reasoning. In a similar fashion, Francois Chollet describes an emergent new phase in the progression of AI capabilities based on broad generalization (“Flexible AI”), capable of adaptation to unknown unknowns within a broad domain. Both these characterizations align with DARPA’s Third Wave of AI, characterized by contextual adaptation, abstraction, reasoning, and explainability, with systems constructing contextual explanatory models for classes of real-world phenomena. These competencies cannot be addressed just by playing back past experiences. One possible path to achieve these competencies is through the integration of DL with symbolic reasoning and deep knowledge.
Plans are also underway to enhance the Open Innovation Platform with new features to link up companies and government agencies with relevant technology providers to resolve their business challenges. A cloud-based digital bench, for instance, would help facilitate virtual prototyping and testing, Heng said. The Open Innovation Platform also offers co-funding support for prototyping and deployment, he added. The Building and Construction Authority, for example, was matched with three technology providers -- TraceSafe, TagBox, and Nervotec -- to develop tools to enable the safe reopening of worksites. These include real-time systems that have enabled construction site owners to conduct COVID-19 contact tracing and health monitoring of their employees. Enhancements would alsobe made for the Global Innovation Alliance, which was introduced in 2017 to facilitate cross-border partnerships between Singapore and global innovation hubs. Since its launch, more than 650 students and 780 Singapore businesses had participated in innovation launchpads overseas, of which 40% were in Southeast Asia, according to Heng.
Most machine learning algorithms require large sets of labeled data to train models. In many cases, instead of going through the effort of creating their own datasets, machine learning developers search and download datasets published on GitHub, Kaggle, or other web platforms. Eugene Neelou, co-founder and CTO of Adversa, warned about potential vulnerabilities in these datasets that can lead to data poisoning attacks. “Poisoning data with maliciously crafted data samples may make AI models learn those data entries during training, thus learning malicious triggers,” Neelou told The Daily Swig. “The model will behave as intended in normal conditions, but malicious actors may call those hidden triggers during attacks.” Neelou also warned about trojan attacks, where adversaries distribute contaminated models on web platforms. “Instead of poisoning data, attackers have control over the AI model internal parameters,” Neelou said. “They could train/customize and distribute their infected models via GitHub or model platforms/marketplaces.”
The very first step you should be taking is to embrace container technology. The biggest difference between a service-oriented architecture and a microservice-oriented architecture is that in the second one, the deployment is so complex, there are so many pieces with independent lifecycles, and each piece needs to have some custom configuration that it can no longer be managed manually. In a service-oriented architecture, with a handful of monolithic applications, the infrastructure team can still treat each of them as a separate application and manage them individually in terms of the release process, monitoring, health check, configuration, etc. With microservices, this is not possible with a reasonable cost. There will eventually be hundreds of different 'applications,' each of them with its own release cycle, health check, configuration, etc., so their lifecycle has to be managed automatically. There may be other technologies to do so, but microservices have become almost a synonym of containers. Not only Docker containers manually started, but you will also need an orchestrator. Kubernetes or Docker Swarm are the most popular ones.
Remember also that an additional “promise” you are paying for in many contemporary ransomware attacks is that the criminals will permanently and irrevocably delete any and all of the files they stole from your network while the attack was underway. You’re not only paying for a positive, namely that the crooks will restore your files, but also for a negative, namely that the crooks won’t leak them to anyone else. And unlike the “how much did you get back” figure, which can be measured objectively simply by running the decryption program offline and seeing which files get recovered, you have absolutely no way of measuring how properly your already-stolen data has been deleted, if indeed the criminals have deleted it at all. Indeed, many ransomware gangs handle the data stealing side of their attacks by running a series of upload scripts that copy your precious files to an online file-locker service, using an account that they created for the purpose. Even if they insist that they deleted the account after receiving your money, how can you ever tell who else acquired the password to that file locker account while your files were up there?
Proc is a special, pseudo-filesystem in Unix-like operating systems that is used for dynamically accessing process data held in the kernel. It presents information about processes and other system information in a hierarchical file-like structure. For instance, it contains /proc/[pid] subdirectories, each of which contains files and subdirectories exposing information about specific processes, readable by using the corresponding process ID. In the case of the “syscall” file, it’s a legitimate Linux operating system file that contains logs of system calls used by the kernel. An attacker could exploit the vulnerability by reading /proc/<pid>/syscall. “We can see the output on any given Linux system whose kernel was configured with CONFIG_HAVE_ARCH_TRACEHOOK,” according to Cisco’s bug report, publicly disclosed on Tuesday.. “This file exposes the system call number and argument registers for the system call currently being executed by the process, followed by the values of the stack pointer and program counter registers,” explained the firm. “The values of all six argument registers are exposed, although most system call use fewer registers.”
It is needless to emphasise that Data is the new Oil, as Data has shown us time on time that, without it, businesses cannot run now. We need to embrace not just the importance but sheer need of Data these days. Every business runs the onset of processes designed and defined to make everything function smoothly, which is achieved through – Business Processes Management. Each Business Process has three main pillars – Business Steps, Goals and Stakeholders, where series of Steps are performed by certain Stakeholders to achieve a concrete goal. And, as we move into the future where the entire businesses are driven by Data Value Chain which supports the Decision Systems, we cannot ignore the usefulness of Data Science combined with Business Process Management. And this new stream of data science is called Process Mining. As quoted by Celonis, a world-leading Process Mining Platform provider, that; “Process mining is an analytical discipline for discovering, monitoring, and improving processes as they actually are (not as you think they might be), by extracting knowledge from event logs readily available in today’s information systems.
Project Alexandria is a research project within Microsoft Research Cambridge dedicated to discovering entities, or topics of information, and their associated properties from unstructured documents. This research lab has studied knowledge mining research for over a decade, using the probabilistic programming framework Infer.NET. Project Alexandria was established seven years ago to build on Infer.NET and retrieve facts, schemas, and entities from unstructured data sources while adhering to Microsoft’s robust privacy standards. The goal of the project is to construct a full knowledge base from a set of documents, entirely automatically. The Alexandria research team is uniquely positioned to make direct contributions to new Microsoft products. Alexandria technology plays a central role in the recently announced Microsoft Viva Topics, an AI product that automatically organizes large amounts of content and expertise, making it easier for people to find information and act on it. Specifically, the Alexandria team is responsible for identifying topics and rich metadata, and combining other innovative Microsoft knowledge mining technologies to enhance the end user experience.
The company now has 80 Quarkus microservices running in production with another 50-60 Spring microservices remaining in maintenance mode and awaiting a business motive to update. Vodafone Greece’s success wasn’t just because of Sotiriou’s technology choices — he also cited organizational transitions the company made to encourage collaboration. “There is also a very human aspect in this. It was a risk, and we knew it was a risk. There was a lot of trust required for the team, and such a big amount of trust percolated into organizing a small team around the infrastructure that would later become the shared libraries or common libraries. When we decided to do the migration, the most important thing was not to break the business continuity. The second most important thing was that if we wanted to be efficient long term, we’d have to invest in development and research. We wouldn’t be able to do that if we didn’t follow a code to invest part of our time into expanding our server infrastructure,” said Sotiriou. That was extra important for a team that scaled from two to 40 in just under three years.
The confidential cloud employs these technologies to establish a secure and impenetrable cryptographic perimeter that seamlessly extends from a hardware root of trust to protect data in use, at rest, and in motion. Unlike the traditional layered security approaches that place barriers between data and bad actors or standalone encryption for storage or communication, the confidential cloud delivers strong data protection that is inseparable from the data itself. This in turn eliminates the need for traditional perimeter security layers, while putting data owners in exclusive control wherever their data is stored, transmitted, or used. The resulting confidential cloud is similar in concept to network micro-segmentation and resource virtualization. But instead of isolating and controlling only network communications, the confidential cloud extends data encryption and resource isolation across all of the fundamental elements of IT, compute, storage, and communications. The confidential cloud brings together everything needed to confidentially run any workload in a trusted environment isolated from CloudOps insiders, malicious software, or would-be attackers.
Quote for the day:
"Lead, follow, or get out of the way." -- Laurence J. Peter