“Confidential computing is a technique for securing data while in use by creating secure spaces that users rather than administrators’ control,” says Martin O’Reilly, director of research engineering at the Alan Turing Institute. “The idea is to create a trusted execution environment (TEE) or secure enclave, where the data is only accessible by a specific application or user, and only as the data is being processed.” ... Confidential computing’s ‘360-degree protection’ enables data to be processed within a limited part of the computing environment, giving organisations the ability to reduce exposure to sensitive data while also providing greater control and transparency, even allowing businesses to share data for joint processing securely. This represents a significant change, says O’Reilly, pointing out that the ability to create secure spaces where the user controls who has access to the data effectively replicates the trust companies might have in their own IT departments. He notes, however, that the advantages should be weighed against the complexities involved in setting up and managing these technologies.
The warning signs that have emerged should make the leadership team within all the financial institutions sit up and take serious notice. With enough success achieved on customer acquisition, it's now time to focus energies on creating systems that can serve the financial institutions' best interests and use AI as an effective tool for customer engagement rather than coercive intrusions. AI can indeed be moulded into a great tool for enhanced customer success, although the possibilities of this have not been fully explored. Ethical AI is a practice of evaluating the ethical quality of the impact of its prediction on human life. Debt collection is a good place to develop this practice because human debt collectors with performance targets typically find it hard to navigate the moral quagmire involved in dealing with financial stress. India, the land of spirituality, is well-positioned to become a leader in the practice of Ethical AI. The day is not far when the practice of Ethical AI will be a key differentiator for AI Platforms. On the policy side, countries across the globe are also working on Data Protection laws in the model of EU's GDPR, which would give customers the legal Right to Explanation.
While some of their activities might overlap, data engineers are primarily about moving and transforming data into pipelines for the data science team. Put it simply, data engineers have three critical tasks to perform — design, build and arrange data pipelines. In contrast, data scientists analyse, test, aggregate and optimise data. ... Data engineers essentially collect, generate, store, enrich and process data in real-time or in batches. Data engineering involves building data infrastructure and data architecture. Data engineers require experience in software engineering, programming languages, and a firm grip on core technical skills. Understanding ETL, SQL, and programming languages such as Java, Scala, C++, and Python are desired. ... The data science strategy in an organisation deals with data infrastructure, data warehousing, data mining, data modelling, data crunching, and metadata management, most of which are carried out by data engineers. Studies suggest most data science projects fall through as data engineers and data scientists find themselves at cross purposes. Many companies fail to recognise the importance of hiring data engineers. While most companies are starting to realise the importance of data engineers, the talent shortage is all too real.
With machine learning, we can reduce maintenance efforts and improve the quality of products. It can be used in various stages of the software testing life-cycle, including bug management, which is an important part of the chain. We can analyze large amounts of data for classifying, triaging, and prioritizing bugs in a more efficient way by means of machine learning algorithms. Mesut Durukal, a test automation engineer at Rapyuta Robotics, spoke at Aginext 2021 about using machine learning in testing. Durukal uses machine learning to classify and cluster bugs. Bugs can be classified according to severity levels or responsible team or person. Severity assignment is called triage and important in terms of prioritization, where the assignment of bugs to the correct team or person prevents a waste of time. Clustering bugs helps to see whether they heap together on specific features. Exploring the available data on bugs with machine learning algorithms gave him more insight into the health of their products and the effectiveness of the processes that were used.
Today’s data governance and data management practices must be redefined to support the organization’s business needs and ultimately underpin the organization’s data monetization strategy. A Data Monetization practice must: Evangelize a compelling vision regarding the economic potential of data and analytic assets to power an organization’s digital transformation; Educate senior executives, business stakeholders and strategic customers on how to “Think Like a Data Scientist” in identifying where and how data and analytics can deliver material business value; Apply Design Thinking and Value Engineering concepts in collaborating with business stakeholders to identify, validate, value and prioritize the organization’s high-value use cases that will drive the organization’s data and analytics development roadmap; Champion a Data Science team to “engineer” reusable, continuously-learning and adapting analytic assets that support the organization’s high priority use cases; Develop an analytics culture that synergizes the AI / ML model-to-human collaboration that empowers teams at the point of customer engagement and operational execution.
The execution of modern technologies such as Artificial Intelligence and IoT has been changing the entire business world. A recent survey stated that almost 500 IT professionals claiming AI and IoT as most emerging technologies do remodeling business operations and compelling companies to invest more to gain a competitive advantage. And the reasons are simple. The amalgamation of IoT and AI can create smart strategies that can read human preferences and help management make informed decisions with zero error. Not convinced? Let’s check it out with a real-life example. One of the world’s renowned car manufacturers, BMW, has started using AI and IoT in their manufacturing process. It uses censored robots in their premises which help workers while producing innovative cars. They are also leveraging AI for driverless cars for the future. In fact, AI and IoT are affecting the entire transportation industry. Interactive maps and smart route optimization is making it easy for drivers to reach the destination early. It saves fuel cost and reduces journey time. This is why you might have heard why entrepreneurs embrace AI solutions in their taxi app clone development because it plans routes based on peak hours and road construction.
Edge use cases are expanding across industries as companies move compute and analytics capabilities to the edge. Some companies want to reduce latency. Others want to gain greater insights into what's happening in the field whether people, crops, or oil rigs. "Edge computing enables companies and other types of organizations to analyze large amounts of data on site or on devices in real time," said Shamik Mishra, CTO for Connectivity in the Engineering and R&D Business at global consulting firm Capgemini. "This can enable several new opportunities in terms of new sources of revenue, improved productivity, and decreased costs." In fact, there's an entire world of the Internet of Things (IoT) innovation happening that makes edge use cases even more compelling including smart homes, wearables, AR video games and increasingly intelligent vehicles. Gartner expects the IoT platform market to grow to $7.6 billion by 2024, which represents both on-premises and cloud deployments. The company considers PaaS a key enabler of digital scenarios. Allied Market Research sees the broader opportunity worth $16.5 billion by 2025, driven by the desire to avoid network latency problems and restrictions on bandwidth usage for storing data in the cloud...."
To determine what proportion of your sprint to allocate to tech debt, simply find the overlap between the parts of your codebase you'll modify with your feature work and the parts of your codebase where your worse tech debt lives. You can then scope out the tech debt work and allocate resources accordingly. Some teams even increase the scope of their feature work to include the relevant tech debt clean-up. More in this article 'How to stop wasting time on tech debt.' For this to work, individual contributors need to track medium-sized debt whenever they come across it. It is then the Team Lead's responsibility to prioritize this list of tech debt, and to discuss it with the Product Manager prior to sprint planning so that engineering resources can be allocated effectively. Every once in a while, your team will realize that some of the medium-sized debt they came across is actually due to a much larger piece of debt. For example, they may realize that the reason the front-end code is under-performing is that they should be using a different framework for the job. Left unattended, these large pieces of debt can cause huge problems, and — like all tech debt — get much worse as time goes by.
Quote for the day:
"No man is good enough to govern another man without that other's consent." -- Abraham Lincoln