“The vast majority of the affected companies fall into the 1000+ employees category, which most likely reflects the higher prevalence of internal library usage within larger organizations,” Birsan noted. The researcher received more than $130,000 in both bug bounties and pre-approved financial arrangements with targeted organizations, who all agreed to be tested. The hack’s original target PayPal, as well as Apple and Canada’s Shopify, each contributed $30,000 to that amount. Birsan said he came up with an idea to explore the trust that developers put in a “simple command,” “pip install package_name,” which they commonly use with programming languages such as Python, Node, Ruby and others to install dependencies, or blocks of code shared between projects,. These installers—such as Python Package Index for Python or npm and the npm registry for Node–are usually tied to public code repositories where anyone can freely upload code packages for others to use, Birsan noted. However, using these packages comes with a level of trust that the code is authentic and not malicious, he observed.
In the Fourth Industrial Revolution, the urgency to future-proof and transition careers has required nothing short of a reskilling revolution. According to global Salesforce research, since the onset of the pandemic 40% of the workforce have considered a career change. As the digital economy continues to evolve, businesses don’t just have a responsibility to provide employees opportunities to retrain and transition to the jobs of the future. It’s increasingly within their interest to do so. Now more than ever, people need access to the technologies and skills necessary to land the jobs of the future. This why at Salesforce we launched Trailhead in 2014, our free online learning platform, to democratise education and provide an equal pathway into the tech industry. Since the onset of the pandemic we’ve seen a 37% increase in registrations to courses – joining over 2.2 million learners gaining technical, business, partner, and soft skills. Delivering in-demand skills and resume-worthy credentials, we’re addressing the skills imperative and equipping people with the tools they need to succeed. As a society, we need to continually ask ourselves whether we are doing enough to provide everyone with the opportunity to participate.
With respect to accountability – human directors’ decision-making should not be replaced or influenced by unaccountable artificial intelligence’s decision-making. I warn that using artificial intelligence to make decisions in boardrooms could lead to a void of accountability. The use of artificial intelligence in boardrooms could raise other issues as well. ... Human directors, who have consciousness and a conscience, would be accountable; whereas I do not know how AI-directors could effectively be held accountable. This would be an instance in which the risk that directors lose their independent judgment intertwines with the accountability issues possibly arising from the use of artificial intelligence in corporate boardrooms. ... Philosophers warn us that if artificial intelligence developed a conscience and consciousness, it could also possibly experience suffering. Uber-intelligence could lead to uber-suffering. As I wrote in my article, “no potential benefits resulting from the use of AI in the boardrooms, in corporate governance, or in other settings could be worth the risk that artificial agents could suffer; even more drastically, no potential benefit resulting from the use of AI is worth the risk that relations between natural beings and artificial beings could evolve into exploitative relations.”
The conclusion falls in line with the findings of a recent study by the Linux Foundation, which found that hiring managers are 70% more likely to hire a professional with knowledge of open cloud technologies. At the same time, the same report showed that 93% of respondents were struggling to find sufficient talent with open-source skills. Mastering open-source tools and programming libraries can add a lot of value to a developers' CV, therefore. Among the most important tools to add to developers' skillset, Linux featured prominently, with an overwhelming 95% of developers saying they considered the technology to be important to their career; but the understanding of containers and databases also ranked high. IBM's latest research comes in the midst of increasing interest in open-source software, and a desire to tap the technology to create value. Not-for-profit think tank the OpenForum Europe recently found that the open-source ecosystem was contributing up to €95 billion ($113.7 billion) per year to the EU's GDP; and that even a marginal increase of activity could boost the continent's wealth by hundreds of billions of euros.
Erin Kenneally, director of cyber risk analytics at Guidewire, and previously a staffer in the US Department of Homeland Security’s cyber division, says dialogue is needed to disincentivise both the supply-side and the demand-side for ransomware payments – banning insurance payments would evidently fall under the former approach. She also highlights that current light touch interventions for ransomware have been shown to be ineffective. “The US, for example, has issued an Office of Foreign Assets Control [OFAC] advisory on the sanction risks of paying ransoms and a FINCEN Advisory on reporting ransomware red flag indicators. To date, there have been no civil penalties levied against victim companies, insurers or response firms for paying or facilitating the payment of cyber extortion,” she says. “In a nutshell, since the ransom is often lower than the cost of recovery, business interruption and lost business – the convergence of which can spell financial death – many victims and insurers simply pay the ransom and risk sanctions. “As a result, insurers have taken a rational economics approach to ransomware payments, leading to a growing sentiment that the industry is worsening the problem by paying extortions.”
The most extreme form of automation is an autonomous system that operates without human intervention. That's not to say that autonomous systems don't need oversight, however."Automation is a necessary, functional component of an autonomous system. 'Autonomous' implies a degree of artificial intelligence, decision making that is not necessarily rule or workflow based, rather taking actions based on new patterns that are not hard coded into the system," said Robert Greene, senior director, Oracle Autonomous Database product management. "Automation…still requires a human to make the decision to invoke [an] action, so a human is still in the loop." Organizations are automating more tasks using robotics process automation (RPA) and in some cases, they're inheriting autonomous capabilities from the enterprise products they use such as the Oracle Autonomous Database. "You start out by automating smaller steps with smaller stakes, so your organization builds its internal capacity to do automation well and learn how to make it work in hybrid situations that involve people," said Chris Nicholson, founder CEO of deep reinforcement learning solution provider Pathmind.
Digital tools, used appropriately and effectively, can contribute to planning and monitoring internal processes, increasing transparency and accountability across all levels of management, and building customers’ trust. Digital tools are not only helping leaders solve complex issues related to personnel and minimizing operational costs, but also improving decision making. However, leaders will have to verify the suitability of tech tools being implemented in relation to organizational needs and objectives. These are not top-down decisions. Leaders promoting open ways of working in their organization could make this a more inclusive and participatory process by adopting and implementing an approach such as the Open Decision-Making Framework. One key factor to remember: While digital technologies have much potential to improve organizational processes, leaders must take proactive actions and measured steps to help employees internalize and integrate these processes. The easier that leaders make it for employees to adapt to and use new technology in their daily routines, the faster the integration. The hardest part is often the change management: Leaders need to facilitate this in a way that instills a positive attitude in employees.
First, not all companies have embraced an SRE model. A recent study by Blameless found “… 50% of respondents employ an SRE model with dedicated engineers focused on infrastructure and tooling, or an embedded model where full-time SREs are assigned to a service.” The SRE model is gaining momentum, but there is still room for greater adoption. There is also room for internal growth. Ostrowski sees a single SRE team as a single point of failure. “It needs to be a whole department,” he said. In addition, SREs are gaining a more prominent voice at the table, influencing feature rollout. “With proper and mature SRE involvement, teams can’t willy-nilly deploy,” he said. Ostrowski views these teams as maintaining a critical balance between business risk and introducing new technology. Many companies are experiencing rising user demands, and thus must rapidly scale their application networks. Simultaneously, there has been a Cambrian explosion of deployment types — systems could be using any assortment of legacy infrastructure, mainframe, microservices, cloud environments and multiple cloud vendors. “The complexity and topology of the IT space has grown substantially, with many interdependencies,” Ostrowski said.
You will recognize business intelligence by its charts, dashboards, database diagrams, and data integration projects. It is expensive and frustrating -- but indispensable. BI has a permanent advantage over DS because it has concrete data points; few, simple assumptions; self-explanatory metrics; and automated processes. Furthermore, BI will never go away. It will always be a work in progress because you will never stop changing your business or upgrading and replacing the source systems. ... Looking in the rearview mirror of data is important and helpful, but it's limited and will never get you where you want to go. At some point you need to look ahead. BI needs to be accompanied by data science. DS is a complicated, sophisticated form of planning and optimization. Examples include: Predicting in real time which product a customer is most likely to buy; Forming a weighted network between business micro events and micro responses so that decisions can be made without human intervention, then updating that network with every outcome so that it learns as it acts; Forecasting at the SKU level, by day, with every sale; Identifying and predicting rare events, such as credit card fraud, and sending automatic notifications to customers and/or staff;
When we talk about observability, there are two sets of tools: specific observability tools, such as Zipkin and Jaeger, as well as broader application performance monitoring (APM) tools such as DataDog and AppDynamics. When monitoring systems, we need information from all levels, from method and operating system level tracing to database, server, API call, thread, and lock data tracing. Asking developers to add instrumentation to get these statistics is costly and time consuming and should be avoided whenever possible. Instead, developers should be able to use plugins, interception, and code injection to collect data as much as possible. APM tools have done a pretty good job of this. Typically they have instrumentation (e.g. Java agents) built into program languages to collect method-level tracing data, and they have added custom filter logic to detect database, server, API call, thread, and lock data tracing by looking at the method traces. Furthermore, they have added plugins to commonly used middleware tools to collect and send instrumented data. One downside of this approach is that the instrumentation needs will change as programming languages and middleware evolve.
Quote for the day:
"Coaching isn't an addition to a leader's job, it's an integral part of it." -- George S. Odiorne