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The key to smarter robot collaborators may be more simplicity



Maybe if self-driving cars did not have to process all the details of every move they made, they might be able to more around more nimbly.

When we are driving on the road, our brains are processing so much information sub consciously. We are scanning the surrounding vehicles, anticipating their next move, and contemplating how we might respond. We even wonder how our moves might influence other drivers to act.

Robots have to do the same things that we do to operate in our world. Researchers have come up with new ways of enabling robots to better model our behavior. The researchers from Virginia Tech and Stanford University will be presenting their innovation to the annual International Conference on Robot Learning which takes place next week.

They plan to make robots more efficient by designing them to only analyze the major moves of other drivers on the road instead of analyzing each detail. This will enable them to predict their next actions faster and to respond swiftly.

A Theory of Mind

Robot makers are usually guided from Psychology’s theory of mind which posits that people are guided by their understanding of other people’s beliefs in their effort to empathize with and interact with them.

According to the Theory of Mind, young children master this skill and use it throughout their lives.

If robots can come up with a system for predicting the next moves of other actors, they can operate more efficiently on the road.

According to Stanford Professor Dorsa Sadigh, when two people are doing something simple like moving a table, they go by simple things like their discernment of the force from collaborators as they push or pull to make their next move.

A robot store could use this to remember basic descriptions of the actions of the agents surrounding. For example a robot playing a team sport could take note of the movements of its opponents and classify them as right, center, and left.

This data is enough to come up with two algorithms: one for predicting an opponent’s next move, and another for determining its own response.

The second algorithm will also track the opponent’s responses to the robot’s own responses, in order to allow the robot to learn how to influence its opponents.

The key feature of this innovation is that the robot handles minimal data and is therefore able to train itself as it goes along.

Usually, a robot in such a situation would remember not only the exact coordinates of every one of its opponent’s steps but also the direction in which they were moving.

This might seem like an approach that will yield greater accuracy, but that is not how the human mind operates. The human mind goes by simple clues and does not process too much information on the fly.

Future Uses

There are still a lot of questions that will only be answered in the long run. At the moment for example, researchers are working with the assumption that robots only interact in finite interactions.

Researchers assumed that robot cars involved in simulating self-driving were only experiencing single interactions with other cars in every training session. But that is not how driving works.

Cars interact with each other continuously and a self-driving car needs to keep learning and adapting its actions with every interaction.

Sadigh explains that the approach assumes that robot designers know how best to describe the behavior of other actors on the road. They thought of using ‘left’ ‘center’ and ‘right’ to describe the actions of opponents in an air hockey game.

These labels will become much less obvious when the interactions are not as simple. Researchers are still optimistic about their innovation being a game changer. By bridging the gap that separates human-AI interaction and multi-agent learning, scientist never stop learning because it is an important new field of research.

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The Transformative Role of Automation Technology in Banking: Navigating the Future with Efficiency and Compliance




In an age where technological progression sets the tempo for industries across the board, the banking sector finds itself at a crossroads between tradition and innovation. Automation technology, a force majeure in this digital symphony, promises a future where efficiency, accuracy, and customer satisfaction are not just goals but realities. PaymentsJournal’s recent exploration into this dynamic arena highlights how financial institutions are increasingly turning to automation to not only streamline operations but also to navigate the choppy waters of regulatory compliance with agility and foresight.

Embracing Change Amidst Regulatory Tides

The banking landscape is no stranger to regulation; if anything, it’s a domain where regulatory frameworks dictate the rhythm of progression. The impending modernization of the Community Reinvestment Act (CRA) and the introduction of Dodd-Frank 1071 underscore a regulatory environment in flux, one that demands adaptability and forward-thinking strategies from financial institutions. The CRA, a bedrock of equitable lending practices, is poised for an overhaul to align with the digital age, ensuring that banks continue to meet the evolving needs of their communities. On the other hand, Dodd-Frank 1071 aims to shine a light on small business lending practices, with a particular focus on transparency for businesses owned by women and minorities.

The essence of these regulatory updates transcends mere compliance; they represent a shift towards a more inclusive, transparent, and efficient banking ecosystem. However, the road to adherence is fraught with challenges, primarily due to the manual and labor-intensive processes entrenched in the sector. This is where automation technology, with its promise of precision and efficiency, steps in as a pivotal ally for banks navigating the compliance maze.

Automation: A Lever for Efficiency and Compliance

The role of automation in banking transcends operational efficiency. It’s a strategic imperative that addresses a spectrum of challenges, from reducing manual errors to enhancing customer experiences. Tools and systems like Robotic Process Automation (RPA), Artificial Intelligence (AI), and machine learning (ML) are not just buzzwords but integral components of a bank’s arsenal to streamline complex processes and improve decision-making. The Federal Reserve’s insights into banking automation underscore the transformative potential of these technologies in risk management and compliance, highlighting how they can offer real-time monitoring and analysis of transactions to detect anomalies and mitigate fraud.

The efficiency brought about by automation is particularly relevant in the context of compliance. Financial institutions are well-acquainted with the resource-intensive nature of manual compliance processes. Automation offers a reprieve by streamlining data collection and reporting, thereby ensuring accuracy and reducing the likelihood of errors that can lead to regulatory penalties. Moreover, the dynamic nature of regulatory frameworks necessitates a degree of agility that only automated systems can provide. These systems offer continuous monitoring and adaptability to regulatory changes, ensuring that banks remain on the right side of compliance.

Looking Ahead: The Future of Banking with Automation

The journey towards fully automated banking processes is not without its hurdles. Questions about data security, privacy, and the digital divide persist. Yet, the trajectory is clear: automation is not just an option but a necessity for banks aiming to thrive in an increasingly digital and regulated world. Industry leaders and regulatory bodies emphasize the importance of collaboration between banks and technology providers to navigate these challenges effectively.

The transition towards automation in banking, prompted by regulatory changes and operational efficiencies, is more than a technological upgrade. It’s a reimagining of what banking can be in the 21st century: more accessible, efficient, and inclusive. As financial institutions continue to harness the power of automation technology, they pave the way for a future where banking is not just about transactions but about fostering growth, inclusivity, and innovation in the communities they serve.

In essence, the transformative role of automation in banking is a narrative of progress. It’s a story of how technology, when aligned with strategic vision and regulatory compliance, can redefine the banking experience for institutions and customers alike. As the sector stands on the brink of this digital revolution, the promise of a more efficient, compliant, and customer-centric banking future seems not just plausible but inevitable.

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Navigating the Automation Wave: Overcoming Challenges to Reap Benefits in 2024




As we delve into the intricate world of automation, a landscape filled with boundless potential and unforeseen challenges unfolds before us. The dynamic interplay between advancing technology and the evolving needs of businesses has positioned automation at the forefront of digital transformation strategies. Yet, the journey towards fully leveraging the capabilities of automation is fraught with obstacles that require strategic navigation. Drawing insights from Jakob Freund, CEO of Camunda, and enriched with authoritative sources, this article delves into the primary challenges facing automation in 2024 and outlines effective strategies for their mitigation.

The Surge in Automation Investment

Despite the tumultuous nature of recent years, the commitment to automation among businesses has remained remarkably resilient. A report from Camunda reveals a compelling narrative: 90% of IT decision-makers are poised to amplify their investment in automation within the next two years. This statistic, however, belies a puzzling contradiction—the number of automated processes within these companies has not seen a corresponding uptick. The query that arises is straightforward yet complex: What impedes the translation of investment into actionable automation?

The Conundrum of Stakeholder Misalignment

A critical barrier to the successful deployment of automation initiatives is the misalignment between the technical and business factions within an organization. An astonishing 68% of entities report that miscommunication between these teams often leads to the development and implementation of solutions that miss the mark, directly impacting customer satisfaction and loyalty. This challenge is further exacerbated by a disconnect in understanding and prioritizing processes, with over 50% of businesses acknowledging a significant gap.

To bridge this chasm, the adoption of a decentralized center of excellence model has proven beneficial. This approach fosters a culture of collaboration, where a dedicated team disseminates best practices and technologies across departments, ensuring a unified vision. Furthermore, the utilization of open standards like BPMN and DMN can facilitate a mutual understanding of process flows, enabling both technical and business stakeholders to engage in constructive dialogue from the get-go.

Integrating Disparate Technologies

The aftermath of the pandemic’s digital acceleration has left organizations grappling with the integration of a myriad of point solutions. The statistic is telling: 42% of IT leaders cite integration challenges as a significant roadblock to their digital transformation endeavors. The complexity of modern processes, often involving upwards of 26 systems, necessitates a cohesive orchestration layer that transcends traditional point solutions, as emphasized by research from Deloitte.

The Scaling Dilemma

Another pivotal challenge is the scalability of automation efforts. The ambition to augment the degree of automation within an organization often clashes with the reality of maintaining and visualizing existing processes. As automation proliferates, the oversight of mission-critical processes becomes increasingly cumbersome, a sentiment echoed by 68% of surveyed organizations.

Achieving scalable automation necessitates a robust framework for continuous improvement, underpinned by precise metrics and goals. For instance, a mortgage lender aiming to streamline application processes must focus on key performance indicators such as processing times and error rates, which directly influence overarching business metrics like customer satisfaction and conversion rates.

Charting the Course Forward

As we navigate the challenges of automation in 2024, it becomes clear that a strategic, holistic approach is paramount. The transition from isolated point solutions to a unified, strategic automation framework is crucial for realizing the full spectrum of benefits automation offers. In this context, the insights provided by Jakob Freund, alongside McKinsey’s research, serve as a guiding beacon for organizations striving to harness the transformative power of automation.

Embracing automation is not merely about adopting new technologies but about reimagining operational paradigms and fostering a culture of innovation and collaboration. As we move forward, the lessons learned from the challenges of today will undoubtedly illuminate the path to a more automated, efficient, and resilient tomorrow.

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Harnessing AI and Automation in Retail: A Strategic Imperative for Growth and Innovation




In the dynamic world of retail, the adoption of Artificial Intelligence (AI) and automation technologies is not just a trend but a strategic imperative for businesses aiming to thrive amidst fierce competition. The retail industry, characterized by its rapid pace and ever-evolving consumer preferences, demands innovative solutions to stay ahead. A recent Forrester report sheds light on the pivotal role of AI and automation in transforming retail operations, offering a nuanced analysis of over a dozen solutions through its Tech Tide™ framework.

AI and automation have long been integral to retail success, optimizing everything from inventory management to customer engagement. Machine learning algorithms, a cornerstone of AI, have significantly improved inventory allocation, pricing strategies, store labor optimization, and marketing promotions. These are not novel concepts but established practices that have enabled retailers to streamline processes and adapt to market changes effectively. The message is clear: the most impactful AI use cases in retail are those that are tried, true, and established.

However, the landscape of AI and automation in retail is not static. Emerging tools, such as anomaly detection systems, offer retailers real-time insights into potential issues, acting as an MRI for retail operations. These tools promise enhanced customer experiences and operational efficiency, yet investment in such innovations remains tepid. Similarly, autonomous stores, epitomized by Amazon’s Just Walk Out technology, offer a glimpse into a frictionless shopping future. Despite their potential, the perception of high costs has limited their adoption in larger retail formats.

The advent of generative AI introduces new possibilities, from improved customer service chatbots to highly personalized marketing campaigns. Nevertheless, these technologies are not silver bullets for retail challenges. They require substantial data and content to function effectively, and their impact must be carefully evaluated against traditional methods like segmentation. The Forrester report suggests a pragmatic approach: while generative AI offers enticing opportunities for innovation, retailers should balance enthusiasm with evidence-based strategies.

Forrester’s comprehensive Tech Tide™: Retail AI And Automation, Q1 2024 study provides a roadmap for retailers navigating this complex landscape. It emphasizes the need for a strategic alignment of AI and automation investments with business goals, budgets, and capabilities. Cutting-edge technologies, such as in-store drones and autonomous vehicles, continue to capture the industry’s imagination. Yet, the effectiveness of AI solutions ultimately depends on their relevance to the brand’s objectives and the practicality of their implementation.

In conclusion, the Forrester report underscores a critical message for the retail sector: embracing AI and automation is essential for innovation and growth. However, the journey toward digital transformation is nuanced, requiring retailers to judiciously select technologies that align with their strategic vision and operational realities. As the retail industry continues to evolve, guided by AI and automation, businesses must remain adaptable, informed, and strategic in their technology investments.

For retailers seeking to navigate the complexities of AI and automation, Forrester offers extensive research and guidance. In this rapidly changing landscape, understanding the nuances of technology adoption is key to unlocking growth and staying competitive. The future of retail lies in leveraging AI and automation not just as technological advancements, but as strategic assets for enduring success.

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