Businesses today are under constant pressure to develop high-quality software and applications rapidly, while keeping costs under control. However, per a recent column in Forbes, 33% of developer time is spent on tech debt-related tasks versus working on new products and features. These tasks are usually mundane and time-consuming for the developers assigned to them, and may cause low motivation and add to high churn rates. 

This is where technologies like Generative AI (Gen AI) can make a difference, opening up new possibilities for automating tedious coding tasks and adding capacity to development teams.

Gen AI coding led to a seismic shift in software development. It has propelled the emergence of coding assistance tools, some enhancing developer productivity with intelligent assistance, while others pioneer autonomous coding technologies. This points to a future where AI handles the bulk of coding, transitioning developers from coding tasks to strategic project oversight.

To capitalise on these advancements, Stride has introduced Stride Conductor, a novel solution that uses natural language to direct a team of LLM agents. These agents collaborate to develop, enhance, and test software in the user’s native development environment, essentially augmenting their team. Stride opted for a multi-agent approach because it allows for self-improvement through critique, better decomposition of large tasks, and the creation of inspectable, readable, and explainable code.

With Stride Conductor’s multi-agent approach, technical debt no longer becomes an expensive inevitability in most IT projects

Technical debt mitigation

With Stride Conductor’s multi-agent approach, technical debt no longer becomes an expensive inevitability in most IT projects, nor does it impede progress. Stride Conductor tackles tech debt in the background by taking over tasks such as:

Static Analysis: Stride Conductor facilitates the high volume, low complexity changes related to linting and validation, security audits, accessibility checks, and web performance optimisation.

Automated Code Testing: Stride Conductor enhances test coverage, fixes failing tests, and adds tests to pull/merge requests to ensure that new or changed code performs as expected.

Replatforming: Stride Conductor can also manage much larger tasks like upgrading language versions, migrating to new stacks, or repatterning existing code.

Case study

Stride Conductor completely changes the calculus around technical debt remediation – enabling projects delayed because of high costs and/or high risk to be delivered with confidence. In a recent project involving a public e-commerce company, Stride was tasked with fixing tens of thousands of static analysis errors in test code, which were preventing the team from upgrading their CI/CD test infrastructure.

These types of errors are generally simple to fix, but the sheer volume was overwhelming, and the project had been deprioritised accordingly.  This kind of “dirty job” is perfect for semi-autonomous AI, which can be taught about codebase rules and patterns and then apply them to novel situations.  Best of all, it’s straightforward to tell when the work is done – when the code patterns look good and the failing tests pass, businesses can move forward with confidence.   

Using Stride Conductor, the company was able to create positive ROI for fixing these static analysis errors, bringing a task estimated at two person-years down to a matter of weeks, including human approvals and oversight. Conductor also offers these advantages over other tools which claim to fix technical debt:

Force Multiplication: Unlike tools that require active operation, Stride Conductor functions like an auxiliary development team, executing tasks semi-autonomously. Human developers need only provide high-level guidance and oversight.

Seamless Interoperability: Stride Conductor integrates into a company’s existing environment, minimising process disruption and maintaining compatibility with existing platforms, stacks and best practices.

Traceability and Transparency: Stride Conductor generates code and a comprehensive chain of thought that can be inspected and reviewed. This end-to-end traceability ensures organisations maintain control and accountability in their AI-augmented development processes.

The post Stride Conductor: AI Enables Companies to Tackle Tech Debt appeared first on AthisNews.

The investigation into the Cambridge Analytica scandal was a high point for awareness of privacy breaches in the social media community, but it certainly was not the first. In February 2018, Guillaume Chaslot, a former YouTube employee, went public with his study on YouTube’s algorithms, which found extreme bias in relation to the 2016 election. The study found that 84% of videos recommended by the algorithm were pro-Trump, with only 16% pro-Clinton. Meanwhile, Twitter came under attack as a documentary by Project Veritas purportedly proved political bias in its regulation of its users.

The push for better regulation with regard to how algorithms work and how to protect user privacy has already advanced, with the European Union’s General Data Protection Regulation (GDPR) governing online data privacy and use of user data having gone into effect in May 2018. However, we contend that while these efforts have been aimed at regulating user data, efforts must be made to regulate algorithms themselves.

Algorithms Are More Than Just Social Media

The truth is, algorithms pervade our lives. They have existed in the systems that run and regulate our lives for decades, performing tasks from a national security early warning system to traffic control systems. More recently, algorithms have found their way into our cars, our homes, and now have tasks as varied as deciding how suitable we are as job candidates or helping to identify health issues.

As with social media, while these algorithms have delivered convenience and usability, they have also failed us. In March and April 2017, Tesla was hit by two lawsuits by citizens claiming that Tesla’s autopilot function was “dangerously defective.” Risk-assessment algorithms regularly used to predict likelihood of reoffending in criminal offenders in six states throughout the United States have been found to be significantly racially biased, resulting in black offenders receiving longer and heavier sentences than their white counterparts. Tay, Microsoft’s machine-learning chatbot, was taken offline within 24 hours of its launch on Twitter after its conversation and language patterns became disturbingly racist.

The risk to users of technology reliant on algorithms is about more than just privacy concerns. There is risk associated with the algorithms themselves — the purpose for which they are built and their error rates in fulfilling these purposes.

We contend that there is a mismatch between the purpose of the algorithms and the rigor with which the algorithms are tested for efficacy, and the impact of a failure in the algorithm. For example, a minor failure in Tesla’s autopilot function to properly operate in certain conditions could result in numerous injuries and deaths if it fails just a fraction of the time. An element of racial bias in algorithms involved in sentencing criminal offenders would reinforce racial discrimination and serve to prevent minor offenders from rehabilitation.

What Do We Do Now?

It’s clear that we have come to the point where the risks of relying on algorithms are becoming too large to ignore. Politicians and journalists have already begun calling for regulation of social media algorithms, even as the platforms continue to mine user data and defend their algorithms. As of May 2018, 33 states in the U.S. had already introduced regulations relating to self-driving cars, but not their algorithms.

But how do we even begin to approach the regulation of a technology that is so widespread and widely used? Are all algorithms equally dangerous? Adding to the complexity of regulation is the fact that algorithms are typically considered proprietary technology, and different algorithms with different uses are governed by different agencies. For example, self-driving cars are governed by transportation authorities, while medical algorithms for disease detection or risk assessment are governed by the Food & Drug Administration. Some tech organizations don’t believe it’s possible to fully regulate algorithms and have argued that this kind of regulation would be a goal that is beyond the capabilities of a government.

An Approach for Assessing and Managing Algorithmic Risks

We propose that a combination of government regulation and self-regulation be introduced as a balanced approach that both protects proprietary assets and helps manage the impact of algorithms on our lives. This approach will also allow for some level of transparency and building an element of trust with the global community.

Government regulation. While algorithms remain valuable proprietary assets for technology companies, there is an inherent need for greater transparency and understanding of how those algorithms work. In his book The Black Box Society, Frank Pasquale recommends allowing a greater role for regulators such as the Federal Trade Commission (FTC) to test algorithms for ethical values such as fairness, social bias, and anti-competitiveness. This should be supported by the requisite funding and would require the ability and willingness to prosecute for ethical violations in the same way that the financial system is regulated. In fact, starting with social media, the FTC has shown a willingness to ensure that algorithms reflect accurate and fair information. For instance, in October 2009, the FTC revised its Endorsement Guides to encompass blogging, and, more recently, the FTC has further flexed its regulatory muscles in the social media sphere, now ensuring that social media influencers comply with these same kinds of regulations.

Regulations to ensure consumer protection from the use of algorithms in technology are on the horizon and cannot come soon enough. In January 2017, the Consumer Product Safety Commission produced reports on the safety of emergent and future technologies, which highlights the state of the art on the use of algorithms to draw insights from consumers or control the behavior of robots and digital assistants. In 2018, the FTC invited public comment and began conducting a series of public hearings on issues arising from the use of digital technologies and algorithms that are likely to help in the development of policies. For example, the FTC received comments from the Information Technology & Innovation Foundation (ITIF) on “the consumer welfare implications associated with the use of algorithmic decision tools, artificial intelligence, and predictive analytics,” in which the ITIF highlights the inadequacy of existing regulations and the need for regulators to protect individuals from companies using algorithms.

Self-regulation via an industry body and an ethical framework. We recommend that technology companies construct an industry-wide ethical framework that applies to algorithms to address fairness, social bias, and anti-competitiveness. This can be constructed by an industry body tasked with constructing the framework and best practices to be implemented, as well as light enforcement in the form of identifying companies that are aligned with their standards. Examples of successful implementations of a similar approach in other industries are numerous, including the palm oil industry with RSPO certification, the fair trade model in agriculture, and the Made in USA standard.

An industry can use this approach to introduce a method to self-regulate its own proprietary algorithms. This also aligns with the previously proposed approach to government regulation, which requires both self-assessment on the part of the technology firm and enforcement by government regulators.

Self-regulation via a risk assessment and prevention framework. In discussing the risks of technology, in his 2010 book The Technology Trap, L.J. Dumas highlights the need to assess the maximum credible risk of technology used on a high-volume basis. The implication is that, when a technology is used at high volumes, even rare events become more likely to occur.

We propose that technology companies use a similar approach to construct a risk assessment framework to apply to algorithms. The aim is to identify critical risks that take the volume of the algorithm’s users into consideration and assess the likelihood of critical events — that is, events that can be considered disastrous. Companies should use the results of such a framework to construct a comprehensive risk prevention framework to minimize the likelihood and impact of critical events.

As news continues to surface of algorithms exploiting public opinion and the impact of algorithmic failures, the need for regulation becomes clear. Nevertheless, consumer awareness, responsible business practices, and governmental protection are still no match for the threat algorithms present to our privacy and social equality.

Important steps toward upholding the legal and ethical principles of democratic societies in the digital age include organizations’ efforts to incorporate values and transparency into their algorithms, and government regulations that encourage innovation and accountability. Finally, it’s important to delineate clear penalties to those who use algorithms with disregard for or intent to harm.

The post An Approach for Assessing and Managing Algorithmic Risks appeared first on AthisNews.

Metamaterials are artificially engineered materials. Scientists create them by combining multiple elements from composite materials such as a metal and an electrical insulator. The result is an entirely new material with properties not found in nature. Engineers can then use these materials to create new devices or improve existing ones.

“With this algorithm, we can design new metamaterial properties on demand,” said Liu, an assistant professor of mechanical and industrial engineering. Credit: Adam Glanzman/Northeastern University

Let’s say you want to build a real-life invisibility cloak. To achieve invisibility, a metamaterial needs to possess certain optical properties. Specifically, scientists would have to design the material so that they could control how light moves around an object without being reflected or absorbed. This design is possible, but it would take just the right material with just the right structure.

There are hundreds of thousands of potential material structures with optical responses that fall somewhere along the optical spectrum. Sifting through them to find a new material design has traditionally taken hours or even days.

Now, Northeastern professor Yongmin Liu has developed a new method for quickly discovering materials that have desirable qualities. In a paper published recently in ACS Nano, Liu and his co-authors describe a machine learning algorithm they developed and trained to identify new metamaterial structures. The new method is much faster and more accurate than previous approaches, paving the way for engineers to design next-generation materials.

The algorithm Liu and his team built was trained with a data set of 30,000 different samples, each representing a specific relationship between a metamaterial structure and corresponding optical property. Once the algorithm learned those relationships, it was able to predict new ones.

“Searching through all possible parameter combinations for materials is nearly impossible. By introducing artificial intelligence to the metamaterial design, I believe the potential of metamaterials will be fully realized,” said Shuang Zhang, a professor of physics at the University of Birmingham. “Prof. Liu’s research points to a new research direction which will be followed by many groups in this field.”

Engineers can now use the algorithm to discover new materials with specific useful characteristics. For example, current solar panels can only convert 20 to 30 percent of sunlight to energy. Liu is interested in finding a material capable of 100 percent light absorption to create more efficient solar panels.

“With this algorithm, we can design new metamaterial properties on demand,” said Liu, an assistant professor of mechanical and industrial engineering. “These novel optical materials will serve as the foundation for a variety of functional devices.”

So, how far off is that invisibility cloak? Liu said he’s confident the algorithm would be able to identify the right material. But current technology could only assemble the material on a nano-scale. Fabricating a cloak large enough for someone to wear is a significant challenge that Liu believes scientists are still 10 to 15 years away from overcoming.

“We have seen tremendous progress in advanced manufacturing, such as 3-D printing,” Liu said. “I hope that people who work in this area come up with some creative ideas to solve the fabrication challenge for a wearable cloak.”

Source: https://www.northeastern.edu/

The post New algorithm can discover materials with unusual characteristics – including invisibility appeared first on AthisNews.