The rise of IoT is shifting software and network requirements within organizations moving forward with efforts, the GAO report also states. The report's authors predict more emphasis on "analysis programs that can condense large volumes of IoT data into actionable information," as well as "'smart' programs that can augment or replace a human operator. Aggregated data gathered from IoT devices can undergo sophisticated data analysis techniques, or analytics, to find patterns and to extract information and knowledge, enhancing decision-making." There will be changes in business structures as a result as well. "IoT software developments permitting automation may reduce the need for human operators in certain capacities," particularly software that "relies on augmented intelligence and behavior to substitute for human judgement and actions, respectively. ..."
The new way brings the teams together, which makes problem resolution part of the process. Iterations are small, and we have tools to get real visibility into what's happening in production. If there is a problem, we can point to the data instead of each other. This focus on communication, collaboration, and the use of production data to drive decisions is the key to making security work in a DevOps world. The principle issue that security teams face when working with other organizations is how to effectively communicate risk, priority, and tradeoffs. Just because we think something is important doesn't mean our developers and ops guys do, too. Moreover, the old approach of enforcing process and exercising veto power over releases is no longer viable. With DevOps, one thing we can be certain of is that the release is going out the door. No more C-S-No. Today, we can't just say "no." We can't even say "no, but..." We need to find ways to say "Yes, here's how we can do it."
You can expect to see decentralized solutions like Storj, Sia, MaidSafe, and FileCoin start to gain some initial traction in the enterprise storage space. One enterprise pilot phase rollout indicates this decentralized approach could reduce the costs of storing data by 90 percent compared to AWS. As for blockchain-driven AI, you can expect to see a three phase roll-out. First, within the existing enterprise. Then, within the ecosystem. Finally, totally open systems. The entire industry might be termed blockchains for big data. Longer term, we will see an expansion of the concept of big data, as we move from proprietary data silos to blockchain-enabled shared data layers. In the first epoch of big data, power resided with those who owned the data. In the blockchain epoch of big data, power will reside with those who can access the most data and who can gain the most insights most rapidly.
Banks that collectively own SWIFT saw their profits vanish last year as the organization increased its investments in information security, even as the interbank messaging service handled record volumes of money-moving messages. The investment followed the $81 million heist from the central bank of Bangladesh in February last year, accomplished by attackers who issued fraudulent SWIFT money-moving messages from a compromised Bangladesh Bank system. News of the attack sparked a public relations disaster for the Brussels-based cooperative, formally known as the Society for Worldwide Interbank Financial Telecommunication, calling into question the integrity of its messaging service and whether the organization was doing enough to police members' information security practices.
Within the realm of IoT, blockchain has huge potential with home automation systems, connected thermostats, autonomous vehicles, etc. Blockchain helps reduce security threats at the edge. The long-term value is with interactive appliances such as refrigerators or washing machines that can intuitively restock, order, pay for and have items shipped without user interaction. Industries like aviation, financial services, healthcare, mining, public sector and supply chain/logistics companies have all begun transforming to support blockchain. For example, aviation and manufacturing are using blockchain to track, move and locate replacement parts across multiple companies and suppliers. The financial services industry is investigating blockchain to ensure transactional integrity, faster clearing and settlement with lowered costs, especially at scale.
The number one trend around artificial intelligence is underscored by the fact that bankers believe artificial intelligence (AI) will ‘revolutionize the way banks gather information and interact with customers.’ This is in line with the findings from other industries, where the application of big data and machine learning is expected to provide a better understanding of customer beliefs and intentions, enabling enhanced customer experiences and better competitive positioning. ... “With advances in artificial intelligence, the Internet of Things and big data analytics, humans can now design technology that’s capable of learning to think more like people and to constantly align to and help advance their wants and needs. This human-centered technology approach pays off for businesses, as leading companies will transform relationships from provider to partner — simultaneously transforming internally.”
Machine learning uses algorithms that iteratively learn from data, allowing technology to glean more actionable insights from the available data. Examples of machine learning outside of the hotel sector include credit scoring and the targeting of marketing advertisements. In hotel revenue technology, machine learning is often used in conjunction with statistical methods to produce cutting-edge forecasting and decision optimisation. High-performance technology can use machine-learning processes to better understand the relationship between price and demand, and generate room rates that adapt and anticipate market fluctuations.In the age of big data, machine learning systems are critical. Any revenue manager working without the support of an analytical revenue management solution will find themselves overwhelmed by the sheer volume and complexity of data.
Smart cities are built on citywide fiber networks, which can eventually (as with the case of ZenFi's network) connect 5G wireless antennas on every street corner and every floor of every office building back to the core network. This densification of the wireless networks is the true hero of the smart cities revolution, enabling not only smart-city kiosks, but capacity for high-speed wireless applications on smartphones and tablets, widespread IoT deployments, mobile augmented reality applications, self-driving cars and more. It's also amazing that New York is leading the smart city charge. Because if the concept can make it there, it can make it anywhere. Dark-fiber deployments in New York typically cost far more than in just about any other city because of heavy unionization and the scale of any disruption when streets have to be closed for fiber installation.
Mesh networks are resilient, self-configuring, and efficient. You don’t need to mess with them after often minimal work required to set them up, and they provide arguably the best and highest throughput you can achieve in your home. These advantages have led to several startups and existing companies introducing mesh systems contending for the home and small business Wi-Fi networking dollar. Mesh networks solve a particular problem: covering a relatively large area, more than about 1,000 square feet on a single floor, or a multi-floor dwelling or office, especially where there’s no ethernet already present to allow easier wired connections of non-mesh Wi-Fi routers and wireless access points. All the current mesh ecosystems also offer simplicity. You might pull out great tufts of hair working with the web-based administration control panels on even the most popular conventional Wi-Fi routers.
Proponents of Rust, the language engineered by Mozilla to give developers both speed and memory safety, are stumping for the language as a long-term replacement for C and C++. But replacing software written in these languages can be a difficult, long-term project. One place where Rust could supplant C in the short term is in the traditionally C libraries used in other languages. Much of the Python ecosystem for statistics and machine learning is written in C, via modules that could be replaced or rewritten incrementally. It isn’t difficult to expose Rust code to Python. A Rust library can expose a C ABI (application binary interface) to Python without too much work. Some Rust crates (as Rust packages are called) already expose Python bindings to make them useful in Python. But there is always opportunity for closer integrations between both languages.
Quote for the day:
"Beware that the detours in your life don't turn into destinations.: -- Tim Fargo