With the emphasis on cybersecurity, I expect to see open source projects and commercial offerings squarely focused on cloud native security. Two areas will get the attention — software supply chain and eBPF. The software supply chain closely mimics the supply chain of real-world commerce where resources are consumed, then transformed, through a series of steps and processes, and finally supplied to the customer. Modern software development is about assembling and integrating various components available in the public domain as open source projects. In the complex supply chain of software, a compromised piece of software can cause severe damage to multiple deployments. Recent incidents involving CodeCov, Solarwinds, Kaseya, and the ua-parser-js NPM package highlight the need to secure the software supply chain. In 2022, there will be new initiatives, projects, and even new startups focusing on secure software supply chain management. The other exciting trend is eBPF that enables cloud native developers to build secure networking, service mesh, and observability components.
Many chronic diseases move along a dynamic trajectory that creates a challenge of unpredictable progression. This is often disregarded by first-generation AI as it requires constant adaptation of therapeutic regimens. Also, many therapies do not show loss of response until even a few months. The second-generation AI systems are designed to improve response to therapies and facilitate analysing inter-subject and intra-subject variabilities in response to therapies over time. Most first-generation AI systems extract data from large databases and artificially impose a rigid “one for all” algorithm on all subjects. Attempts to constantly amend treatment regimens based on big data analysis might be irrelevant for an individual patient. Imposing a “close to optimal” fit on all subjects does not resolve difficulties associated with dynamicity and the inherent variability of biological systems. The second-generation AI systems focus on a single patient as the epicentre of an algorithm and to adapt their output in a timely manner.
A public blockchain network — one that anyone can join without asking for permission — allows unlimited viewing of information stored on it, eliminates intermediaries, and operates independently of any governing party. It is well-suited for digital consumer offerings (like NFT’s), cryptocurrencies, and certifying information such as individuals’ degrees or certificates. But private networks — those that require a party to be granted permission to join it — are often far better suited for businesses because access is restricted to verified members and only parties directly working together can see the specific information they exchange. This better satisfies industrial-grade security requirements. For these reasons, Walmart decided to go with a private network built on Hyperledger Fabric, an open-source platform. ... For Walmart and its carriers, this meant working with each carrier’s unique data (vendor name, payment terms, contract duration, and general terms and conditions), which is combined with governing master tables of information such as fuel rates and tax rates. The parties should then jointly agree to the formulas that the blockchain will use to calculate each invoice.
A property graph uses nodes, relationships, labels, and “properties.” Both the relationships and their connecting nodes of data are named, and capable of storing properties. Nodes can be labeled in support of being part of a group. Property graphs use “directed edges” and each relationship has a start node and an end node. Relationships can also be assigned properties. This feature is useful in presenting additional metadata to the relationships between the nodes. ... Knowledge graphs are very useful in working with data fabric. The semantics feature (and the use of graphs) supports discovery layers and data orchestration in a data fabric. Combining the two makes the data fabric easier to build out incrementally and more flexible, which lowers risk and speeds up deployment. The process allows an organization to develop the fabric in stages. It can be started with a single domain, or a high value use case, and gradually expanded incrementally with more data, users, and use cases. A data fabric architecture, combined with a knowledge graph, supports useful capabilities in many key areas.
The world of ten years ago was dominated by structured data. After 2012, though, as sensors became cheaper, cell phones gradually became smartphones, and cameras were installed to make shooting easier. With this, a large amount of unstructured data was generated, and enterprises entered uncharted territory, making progress slow. Some of the inhibitors to progress in this area include: Complexity: Unlike structured data which can be analyzed intuitively, unstructured data needs to be further processed and then analyzed, usually best done through artificial intelligence. Machine learning algorithms classify and label content from it. However, it is not easy to identify high-quality data from the data set due to the large amount and complexity of unstructured data -- this has been painful for developer teams and a key challenge to data architectures that are already complex. Cost: Although the enterprise recognizes the value of unstructured data, the cost can be a potential obstacle to making use of it. The cost of enterprise infrastructure, human resources, and time can hinder the implementation and development of AI and the data it analyzes.
“It’s a digital representation of the physical supply chain,” said Hans Thalbauer, the managing director for supply chain and logistics for Google Cloud. “You model all the different locations of your enterprise. Then you model all your suppliers, not just the tier one but tier two, three, and four. You bring in the logistic service providers. You bring in manufacturing partners. You bring in customers and consumers so that you have really the full view.” Once a network of supply chain players has been built out, the customer then starts loading data into their digital twin. The customer starts with their private enterprise data, which typically includes past orders, pricing, costs, and supply and demand forecasts, Thalbauer said. “Then you also want to get information from your business partners,” Thalbauer told Datanami last year. “You share your demands with your suppliers. And they actually loop back to you what is the supply situation. You share the information with the logistics service providers. You share sustainability information with the service provider.”
Private blockchain platforms are particularly suited to supply chain management because they provide traceability, transparency, real-time logistics tracking, electronic funds transfer and smart contract management. Processes including negotiations support and procurement can also be connected via blockchain to build trust and confidence with new suppliers, partners and colleagues. While private blockchains adhere to the original principles of blockchain and offer all the distributed benefits, they also retain some of the characteristics of more centralised, controlled networks. This improves greater privacy and eliminates many of the illicit activities often associated with public blockchains and cryptocurrencies. No one can enter this type of ‘permissioned’ network without proper authentication, making it ideal where it does not suit an enterprise to allow every participant full access to the entire contents of the database.
Blockchain technology is effective in remedying these problems. As a decentralized, immutable ledger where data can be inputted and shared at every point of action, blockchain works by storing information in interconnected blocks and provides a value-add for insuring carbon offsets. This creates a chain of information that cannot be hacked and can be transmitted between all relevant parties throughout the supply chain. Key players can enter, view, and analyze the same data points securely and with the assurance of the data’s accuracy. In addition, the technology can identify patterns of error, giving actionable insights into where systems or humans may be contributing to the problem. Data needs to move with products throughout the supply chain to create an overall number for carbon emissions. Blockchain’s decentralization offers value to organizations and their respective industries so that more, reliable data can be shared between all parties to shine a light on the areas they need to work on, such as manufacturing operations and the offsets of buildings.
Institutional Incremental Learning is one of the promising ways of addressing data-sharing concerns. Using this approach, organizations can train the model in a secure environment and can share the model without having to share precious data. Institutional Incremental Learning differs from federated learning. In federated learning, all the participants do the training simultaneously. This is challenging, as the centralized server needs to update and maintain models. This results in complex technology and communication requirements. ... After training a model locally, the model, along with the metrics, is shared with participating entities. In this way, the decision to use a particular model lies with the organization, which is going to use them and not be forced by anyone. This truly enables decentralized machine learning, where a model is not only trained but also used at the user's discretion. Incremental institutional learning helps to address catastrophic forgetting.
To discern where AI can improve business processes and where it cannot, it’s important to take into account its legal and ethical considerations, its biases and its transparency. Asking critical questions about certain AI applications is critical to setting a project up for success and avoiding risk down the line. From a legal perspective, we must decipher who carries responsibility for a bad judgment call (i.e. a self-driving car hits a pedestrian). We also must recognize that there is bias when working with cognitive-based technology. AI learns from the data it gets; however, it doesn’t have the means to question this data, which means that data sets can easily skew in one direction and leave AI to adopt bias. This can lead to things like discrimination in recruiting processes or racial bias in healthcare management. Businesses that work with AI will also find themselves walking a fine line between trust and transparency. While the intention of advanced AI is to make more independent decisions over time, engineers can run into a “black box” scenario where it’s unclear how the application came to its decision.
Quote for the day:
"To be the improver of improvements you must challenge assumptions, starting with your own." -- Vala Afshar