dAPIs are on-chain data feeds that are comprised of aggregated responses from first-party (API provider-operated) oracles. This allows for the removal of many vulnerabilities, unnecessary redundancies, and middleman taxes created by existing third-party oracle solutions. Further, using first-party oracles leverages the off-chain reputation of the API provider (compare this to the nonexistent reputation of anonymous third-party oracles). See our article “First-Party vs Third-Party Oracles” for a more extended treatise on these issues. Further, dAPIs are data feeds built with transparency. What we mean by this is: you know exactly where the data comes from — this ensures things like data quality as well as independence of data sources to mitigate skewness in aggregated results. Rather than having oracle-level staking — which is impractical and arguably infeasible for reasons alluded to in this article — API3 has a staking pool. API3 holders can provide stake to the protocol. This stake backs insurance services that protect users from potential damages caused by dAPI malfunctions. The collateral utility has the participants share API3’s operational risk and incentivizes them to minimize it. Staking in the protocol also grants stakers inflationary rewards and shares in profits.
Data has been on the front lines in recent culture wars due to accusations of racial, gender, and other forms of socioeconomic bias perpetrated in whole or in part through algorithms. Algorithmic biases have become a hot-button issue in global society, a trend that has spurred many jurisdictions and organizations to institute a greater degree of algorithmic accountability in AI practices. Data scientists who’ve long been trained to eliminate biases from their work now find their practices under growing scrutiny from government, legal, regulatory, and other circles. Eliminating bias in the data and algorithms that drive AI requires constant vigilance on the part of not only data scientists but up and down the corporate ranks. As Black Lives Matter and similar protests have pointed out, data-driven algorithms can embed serious biases that harm demographic groups (racial, gender, age, religious, ethnic, or national origin) in various real-world contexts. Much of the recent controversy surrounding algorithmic biases has focused on AI-driven facial recognition software. Biases in facial recognition applications are especially worrisome if used to direct predictive policing programs or potential abuse by law enforcement in urban areas with many disadvantaged minority groups.
In essence, if you can create a digital clone of a person, you can much better predict his or her online behavior. That’s a core part of the monetization model of social media companies, but it could become a capability of adversarial states who acquire the same data through third parties. That would enable much more effective disinformation. A new paper from the Center For European Analysis, or CEPA, also out on Wednesday, observes that while there has been progress against some tactics that adversaries used in 2016, policy responses to the broader threat of micro-targeted disinformation “lag.” “Social media companies have concentrated on takedowns of inauthentic content,” wrote authors Alina Polyakova and Daniel Fried. “That is a good (and publicly visible) step but does not address deeper issues of content distribution (e.g., micro-targeting), algorithmic bias toward extremes, and lack of transparency. The EU’s own evaluation of the first year of implementation of its Code of Practice concludes that social media companies have not provided independent researchers with data sufficient for them to make independent evaluations of progress against disinformation.” Polyakova and Fried suggest the U.S. government make several organizational changes to counter foreign disinformation.
We’re talking about smart, multi-tasking robots that are increasingly being trusted catalysts at the core of digital work transformation strategies. This is because they effortlessly perform joined up, data-driven work across multiple operating environments of complex, disjointed, difficult to modify legacy systems and manual workflows. And unlike any other robot, they deliver work without interruption, automatically making adjustments according to obstacles – different screens, layouts or fonts, application versions, system settings, permissions, and even language. These robots also uniquely solve the age old problem of system interoperability by reading and understanding applications’ screens in the same way humans do. They’re re-purposing the human interface as a machine-usable API – crucially without touching underlying system programming logic. This ‘universal connectivity’ also means that all current and future technologies can be used by robots – without the need of APIs, or any form of system integration. ... This capability breathes new life into any age of technology and enables these robots to be continually augmented with the latest cloud, artificial intelligence, machine learning, and cognitive capabilities that are simply ‘dragged and dropped’ into newly designed work process flows.
All-pairs testing greatly reduces testing time, which in turn controls testing costs. The QA team only checks a subset of input/output values -- not all -- to generate effective test coverage. This technique proves useful when there are simply too many possible configuration options and combinations to run through. Pairwise testing tools make this task even easier. Numerous open source and free tools exist to generate pairwise value sets. The tester must inform the tool about how the application functions for these value sets to be effective. With or without a pairwise testing tool, it's crucial for QA professionals to analyze the software and understand its function to create the most effective set of values. Pairwise testing is not a no-brainer in a testing suite. Beware these factors that could limit the effectiveness of all-pairs testing: unknown interdependencies of variables within the software being tested; unrealistic value combinations, or ones that don't reflect the end user; defects that the tester can't see, such as ones that don't reflect in a UI view but trigger error messages into a log or other tracker; and tests that don't find defects in the back-end processing engines or systems.
Even before remote work was ubiquitous, accidental and malicious insider threats posed a serious risk to data security. As trusted team members, employees have unprecedented access to company and customer data, which, when left unchecked, can undermine company, customer, and employee privacy. These risks are magnified by remote work. Not only has the pandemic’s impact on the job market made malicious insiders more likely to capture or compromise data to gain leverage with new employment prospects or to generate extra income, but accidental insiders are especially prone to errors when working remotely. For example, many employees are blurring the lines between personal and professional technology, sharing or accessing sensitive data in ways that could undermine its integrity. In response, companies need to be proactive about establishing and enforcing clear data management guidelines. In this regard, communication is key, and accountability through monitoring initiatives or other efforts will help keep data protected during the transition.
Employees need to feel connected and trusted. Yet leaders who find it tough to trust their workforce might opt for micro-management; they'll continue to check-up on their workers rather than checking-in to see how they're getting on. Peterson says leaders should look to develop a management style that cultivates wellbeing. In uncertain times, employees need a sense of certainty from their leaders. Executives should ensure their staff feel engaged, not micro-managed. "It's more important than ever for managers to ask whether people are getting their ABCs: their autonomy, belonging and competence. Leaders who don't get that from their own boss will tend to overcompensate with the people they're managing; they'll micro-manage, and that's not helpful," he says. Lily Haake, head of the CIO Practice at recruiter Harvey Nash, agrees that leaders who micro-manage will struggle in the new normal. They won't get the best from the workers and their effectiveness will suffer. Haake says managers who want to cultivate wellbeing need to pick up on subtle signs that all isn't well. Executives should adopt a considered approach, using a technique like active listening, to pick up on potential issues before they become major problems.
Stakeholders in blockchain solutions will need to ensure that their products comply with a legal and regulatory framework that was not conceived with this technology in mind. From a commercial law standpoint, smart contracts must be contemplated for negotiation, execution and administration on a blockchain, and in a legal and compliant fashion. Liability needs to be addressed. What if the contract has been miscoded? What if it does not achieve the parties' intent? The parties must also agree on applicable law, jurisdiction, proper governance, dispute resolution, privacy and more. There are public policy concerns that should be taken into account in shaping new laws, rules and regulations. For example, permissionless blockchains can be used for illegal purposes such as money laundering or circumventing competition laws. Also, participants may be exposed to irresponsible actions on the part of the "miners" who create new blocks. Unfortunately, there aren't any current legal remedies for addressing corrupt miners. As lawyers and technologists ponder these issues, several solutions are being bandied about. One possible remedy involves a hybrid of permissioned and permissionless blockchains.
PyTorch is seeing particularly strong adoption in the automotive industry—where it can be applied to pilot autonomous driving systems from the likes of Tesla and Lyft Level 5. The framework also is being used for content classification and recommendation in media companies and to help support robots in industrial applications. Joe Spisak, product lead for artificial intelligence at Facebook AI, told InfoWorld that although he has been pleased by the increase in enterprise adoption of PyTorch, there’s still much work to be done to gain wider industry adoption. “The next wave of adoption will come with enabling lifecycle management, MLOps, and Kubeflow pipelines and the community around that,” he said. “For those early in the journey, the tools are pretty good, using managed services and some open source with something like SageMaker at AWS or Azure ML to get started.” ... “The TensorFlow object detector brought memory issues in production and was difficult to update, whereas PyTorch had the same object detector and Faster-RCNN, so we started using PyTorch for everything,” Alfaro said. That switch from one framework to another was surprisingly simple for the engineering team too.
Techno-nationalism is fueled by a complex web of justified economic, political and national security concerns. Countries engaging in “protectionist” practices essentially ban or embargo specific technologies, companies, or digital platforms under the banner of national security, but we are seeing it used more often to send geopolitical messages, punish adversary countries, and/or prop up domestic industries. Blanket bans give us a false sense of security. At the same time, when any hardware or software supplier is embedded within critical infrastructure – or on almost every citizen’s phone – we absolutely need to recognize the risk. We need to take seriously the concern that their kit could contain backdoors that could allow that supplier to be privy to sensitive data or facilitate a broader cyberattack. Or, as is the lingering case with TikTok, the concern is whether the collection of data on U.S. citizens via an entertainment app could be forcibly seized under Chinese law and enable state-backed cyber actors to then target and track federal employees or conduct corporate espionage.
Quote for the day:
"Stand up for what you believe, let your team see your values and they will trust you more easily." -- Gordon Tredgold