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Decentralized science can be the next big thing for blockchain utilization – Cointelegraph

April 30, 2024 | Author: admin

The digital transformation has marked a stepping stone for making scientific research accessible to more researchers worldwide. However, two decades of digitization have shown that preparing and distributing content in a digital format is not enough to lower the barriers to scholarly information.

Peer review, a vital process to ensure the quality and accuracy of published academic work, is still notoriously inefficient. The slow and opaque processes lead to delays in publishing new research. On top of that, most researchers from smaller institutions or developing countries have to face an uphill battle in gaining visibility and accessing mainstream scientific platforms.

Due to the heavily centralized nature of such platforms, maintaining control and rights over ones scientific data and findings becomes another challenge for most researchers. All in all, these challenges lead to a slower pace of scientific advancement and collaboration, often discouraging researchers and limiting the diversity of contributions in the scientific community.

Blockchain technology can potentially improve various processes of scientific publishing, offering a solution to key challenges across the board. The immutability, transparency and global availability of blockchain present an opportunity for science circles to take a major step toward decentralization while giving power and governance to users and researchers.

NobleBlocks is a blockchain-based decentralized science (DeSci) platform that promises to address critical pain points in scientific publishing. Built on the Internet Computer Protocol, a blockchain infrastructure known for its speed and scalability, NobleBlocks offers high-speed and transparent transactions to minimize the time and complexity typically associated with publishing scientific research.

Key to NobleBlocks mission of incentivizing scientific contribution is NOBL, the platforms native token, which is built on the Ethereum blockchain. NobleBlocks uses its custom ckNOBL solution to integrate the Ethereum-based NOBL with the Internet Computer blockchain, enhancing the decentralization aspect of the DeSci platform and ensuring a seamless and secure connection between two robust blockchain ecosystems.

The tokenization of scientific efforts enables DeSci to acknowledge and reward every contributor from editor to reviewer in a fair model. As a globally accessible portal, NobleBlocks allows researchers to expand their projects horizons through partnerships with international researchers.

NobleBlocks provides a streamlined system on blockchain that significantly reduces the time from research submission to publication, fixing inefficient peer review processes.

The platform democratizes access to essential research material globally by offering all researchers an open and easy-to-access platform. NobleBlocks also establishes data sovereignty and the integrity of scientific work by allowing researchers to retain control over their intellectual property a crucial factor in encouraging original and diverse scientific contributions.

Publications and contributions can be seen from the dashboard. Source: NobleBlocks

Integrating the decentralized nature of blockchain with conventional scientific publishing practices is no easy task. To achieve it, NobleBlocks harmonized the rigors of academic peer review with the efficiency and transparency of blockchain. The resulting solution has enabled the preservation of the integrity of the scientific process while opening doors to faster, more equitable access to research.

As a fully on-chain platform, NobleBlocks ensures complete transparency and immutability of scientific data and publications. Being fully on-chain allows every transaction, submission and review process to be recorded on the blockchain, offering enhanced security and trust in the hosted academic content.

Aside from providing a platform for publishing and reviewing scientific work, NobleBlocks also builds a network to connect researchers, academicians and thought leaders from diverse disciplines and geographies. It seeks to contribute to a richer and more inclusive scientific dialogue by encouraging collaboration, idea exchange and collective problem-solving.

The platform offers detailed information about a researcher through quick profiles. Source: NobleBlocks

The concept of making scientific research globally accessible through blockchain has raised interest from both the Web3 ecosystem and the scientific communities. Aside from receiving grants from the Dfinity Foundation, a major contributor to the Internet Computer blockchain, NobleBlocks also enjoyed a rapid sell-out for NOBL in under one minute.

NobleBlocks envisions an open, transparent and efficient scientific publishing landscape enhanced by blockchain technology. It anticipates a shift toward more accessible and participatory scientific processes that reduce barriers and improve efficiency.

By merging scientific publishing with blockchain, the platform aims to contribute to this evolution. NobleBlocks promises to reshape how research is shared and accessed, enhancing the integrity and value of scientific work.

For the broader crypto and Web3 ecosystem, NobleBlocks exemplifies the practical application of blockchain in non-financial sectors through the utilization of DeSci. The platform offers a model for integrating blockchain into academia, encouraging similar adaptations in other areas and advancing the entire Web3 ecosystem.

Disclaimer. Cointelegraph does not endorse any content or product on this page. While we aim at providing you with all important information that we could obtain in this sponsored article, readers should do their own research before taking any actions related to the company and carry full responsibility for their decisions, nor can this article be considered as investment advice.

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UVA’s new School of Data Science has grand opening – – CBS19 News

April 30, 2024 | Author: admin

Inside there is also a space to bring in researchers and guest speakers, as well as host annual events, including the School's annual Datapalooza and Women in Data Science events.

The building also features a giant data sculpture. It is an interactive art piece that displays different data.

"The genome related to Alzheimer's disease is loaded up on there, we've got the number of hours it took to build this building by day is loaded up on there. The idea is you select a data set, and you are rewarded by seeing the data fill the sculpture with light," said Burgess.

Burgess says the growth of the School of Data Science shows how much the university believes data science will change the world.

"The fact that we have university leadership here saying, 'This is a place that brings us together.' We have the governor here saying, 'This is important for the Commonwealth, this is important for higher education.' We really believe that and we think that what we've seen is that other people believe that as well," she said.

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Deloitte Broadens Biosecurity, Public Health Capabilities With Gryphon Scientific Acquisition; Beth Meagher Quoted – GovCon Wire

April 30, 2024 | Author: admin

Deloitte has acquired Gryphon Scientific for an undisclosed sum as part of efforts to broaden its biosafety, biosecurity, public health and emergency preparedness and response capabilities.

Through the transaction, Deloitte said Monday it will absorb Gryphons scientists, programmers, data analysts, public health specialists and planning and policy professionals with experience in scientific communications, modeling, data science and risk assessment.

The two companies will also develop artificial intelligence applications through Deloittes Federal Health AI Accelerator to improve public health and safety and help customers prepare for chemical threats, biothreats and other biological emergencies.

Beth Meagher, U.S. federal health sector leader and principal at Deloitte Consulting, said the addition of Gryphons professionals and leadership reflects an enhancement to the consulting firms data analytics and advanced tech capabilities.

Our federal health practice is excited to lead the way for U.S. government and public services (GPS) to push the boundaries and bring our clients to the forefront of AI-enabled, mission-driven work, Meagher noted. This enhances the types of data-driven technology and scientific experience that we can offer to federal agencies and strengthens our ability to support government leaders in their efforts to safeguard the security of our nation and the health and safety of our people.

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Gilbane completes University of Virginia’s School of Data Science building – World Construction Network

April 30, 2024 | Author: admin

Gilbane Building Company has completed the construction of the University of Virginias (UVA) School of Data Science building in the US.

The project was supported via a $120m donation from the Quantitative Foundation.

The design process, which commenced in January 2020, was a collaborative effort involving Hopkins Architects, VMDO, and the universitys Office of the Architect.

Ground-breaking on the project took place in October 2021, with construction led by Gilbane.

Gilbane Richmond Virginia business leader Maggie Reed said: We are delighted to continue our partnership with UVA on this cutting-edge facility that creates opportunity for collaborative, open, and responsible data science research and education as the schools goals state.

While we helped build a physical building to house the programme, we are excited about what this school without walls will create in the future.

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The UVAs School of Data Science now features a new four-storey, 61,000ft facility.

The building, situated at the east entrance of the Emmet-Ivy Corridor, is part of a larger expansion project by the university.

It has been designed with open hallways, an atrium, stairs, adaptive classrooms, and research and meeting areas, aiming to create an interactive and collaborative learning environment.

UVA president Jim Ryan said: This beautiful and unique new space gives students, faculty, and staff a home base for research and teaching that intersects with schools and disciplines across grounds and beyond.

The School of Data Science building has achieved Leadership in Energy and Environmental Design (LEED) Gold certification for its green design.

It incorporates solar panels, daylighting strategies, and shading systems to minimise the need for artificial lighting.

The building is expected to derive 15% of its power from solar energy, using four arrays of solar photovoltaic panels on its roof.

This aligns with the UVAs Sustainability Plan, which targets carbon neutrality by 2030 and a fossil fuel-free status by 2050.

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Is Data Science a Bubble Waiting to Burst? – KDnuggets

April 30, 2024 | Author: admin

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I once spoke with a guy who bragged that, armed only with some free LinkedIn courses and an outdated college Intro to SQL course, hed managed to bag a six-figure job in data science. Nowadays, most people struggling to get a good data science job will agree thats unlikely to happen. Does that mean the data science job category is a popped bubble or worse, that it hasnt yet burst, but is about to?

In short, no. Whats happened is that data science used to be an undersaturated field, easy to get into if you used the right keywords on your resume. Nowadays, employers are a little more discerning and often have specific skill sets in mind that theyre looking for.

The bootcamps, free courses, and Hello World projects dont cut it anymore. You need to prove specific expertise and nail your data science interview, not just drop buzzwords. Not only that, but the shine of data scientist has worn off a little. For a long time, it was the sexiest job out there. Now? Other fields, like AI and machine learning, are just a bit sexier.

That all being said, there are still more openings in data science than there are applicants, and reliable indicators say the field is growing, not shrinking.

Not convinced? Lets look at the data.

Over the course of this article, Ill drill down into multiple graphs, charts, figures, and percentages. But lets start with just one percentage from one outstandingly reputable source: The Bureau of Labor Statistics.

The BLS predicts that there will be a 35 percent change in employment from 2022 to 2032 for data scientists. In short, in 2032, there will be about a third more jobs in data science than there were in 2022. For comparison, the average growth rate for all jobs is 3 percent. Keep that number in mind as you go through the rest of this article.

The BLS does not think that data science is a bubble waiting to burst.

Now we can start getting into a bit of the nitty gritty. The first signs people point to as signs of a popped or impending bubble pop are the mass layoffs in data science.

Its true that the numbers dont look good. Starting in 2022 and continuing through 2024, the tech sector in general experienced 430k layoffs. Its difficult to tease out data science-specific data from those numbers, but the best guesses are that around 30 percent of those were in data science and engineering.

Source: https://techcrunch.com/2024/04/05/tech-layoffs-2023-list/

However, thats not a burst bubble of data science. Its a little smaller in scope than that its a pandemic bubble popping. In 2020, as more people stayed home, profits rose, and money was cheap, FAANG and FAANG-adjacent companies scooped up record numbers of tech workers, only to lay many of them off just a few years later.

If you zoom out and look at the broader picture of hirings and layoffs, youll be able to see that the post-pandemic slump is a dip in an overall rising line, which is even now beginning to recover:

Source: https://www.statista.com/

You can clearly see the huge dip in tech layoffs during 2020 as the market tightened, and then the huge spike starting in Q1 of 2022 as layoffs began. Now, in 2024, the number of layoffs is smaller than in 2023.

Another scary stat often touted is that FAANG companies shuttered their job openings by 90% or more. Again, this is most in reaction to a widely high number of job openings during the pandemic.

That being said, job openings in the tech sector are still lower than they were pre-pandemic. Below, you can see an adjusted chart showing demand for tech jobs relative to February 2020. Its clear to see that the tech sector took a blow its not recovering from any time soon.

Source: https://www.hiringlab.org/2024/02/20/labor-market-update-tech-jobs-below-pre-pandemic-levels/

However, lets look a little closer at some real numbers. Looking at the chart below, while job openings are indubitably down from their 2022 peak, the overall number of openings is actually increasing up 32.4% from the lowest point.

Source: https://www.trueup.io/job-trend

If you look at any labor and news reports online, youll see theres a bit of an anti-remote, anti-tech backlash happening at the moment. Meta, Google, and other FAANG companies, spooked by the bargaining power that employees enjoyed during the pandemic heights, are now pushing for return-to-office mandates (data science jobs and other tech jobs are often remote) and laying off large quantities of employees somewhat unnecessarily, judging by their revenue and profit reports.

Just to give one example, Googles parent company Alphabet laid off over 12,000 employees over the course of 2023 despite growth across its ad, cloud, and services divisions.

This is just one facet with which to examine the data, but part of the reason companies are doing these layoffs is more to do with making the board happy rather than any decreased need for data scientists.

I find that people believing were in a data science bubble are most often those who dont really know what data scientists do. Think of that BLS stat and ask yourself: why does this well-informed government agency believe that theres strong growth in this sector?

Its because the need for data scientists cannot go away. While the names might be changed AI expert or ML Cloud Specialist rather than Data Scientist the skills and tasks that data scientists perform cant be outsourced, dropped, decreased, or automated.

For example, predictive models are essential for businesses to forecast sales, predict customer behavior, manage inventory, and anticipate market trends. This enables companies to make informed decisions, plan strategically for the future, and maintain competitive advantages.

In the financial sector, data science plays a crucial role in identifying suspicious activities, preventing fraud, and mitigating risks. Advanced algorithms analyze transaction patterns to detect anomalies that may indicate fraud, helping protect businesses and consumers alike.

NLP enables machines to understand and interpret human language, powering applications like chatbots, sentiment analysis, and language translation services. This is critical for improving customer service, analyzing social media sentiment, and facilitating global communication.

I could list dozens more examples demonstrating that data science is not a fad, and data scientists will always be in demand.

Revisiting my anecdote from earlier, part of the reason it feels like were in a bubble that is either popping or about to pop is the perception of data science as a career.

Back in 2011, Harvard Business Review famously called it the sexiest job of the decade. In the intervening years, companies hired more data scientists than they knew what to do with, often unsure about what data scientists actually did.

Now, a decade and a half later, the field is a little wiser. Employers understand that data science is a broad field, and are more interested in hiring machine learning specialists, data pipeline engineers, cloud engineers, statisticians, and other specialties that broadly fall under the data science hat but are more specialized.

This also helps explain why this idea of walking into six figure job straight out of a bachelor's degree used to be the case - since employers didn't know better - but now is impossible to do. The lack of easy data science jobs makes it feel like the market is tighter. It's not; data shows job openings are still high and demand is still greater than the graduates coming out with appropriate degrees. But employers are more discerning and unwilling to take a chance on untried college grads with no demonstrated experience.

Finally, you can take a look at the tasks that data scientists do and ask yourself what companies would do without those tasks getting done.

If you don't know much about data science, you might guess that companies can simply automate this work, or even go without. But if you know anything about the actual tasks data scientists do, you understand that the job is, currently, irreplaceable.

Think of how things were in the 2010s: that guy I talked about, with just a basic understanding of data tools, catapulted himself into a lucrative career. Things arent like that anymore, but this recalibration isnt a sign of a bursting bubble as some believe. Instead, its the field of data science maturing. The entry-level data science field may be oversaturated, but for those with specialized skills, deep knowledge, and practical experience, the field is wide open.

Furthermore, this narrative of a bubble is fueled by a misunderstanding of what a bubble actually represents. A bubble occurs when the value of something (in this case, a career sector) is driven by speculation rather than actual intrinsic worth. However, as we covered, the value proposition of data science is tangible and measurable. Companies need data scientists, plain and simple. Theres no speculation there.

Theres also a lot of media sensationalism surrounding the layoffs in big tech. While these layoffs are significant, they reflect broader market forces rather than a fundamental flaw in the data science discipline. Dont get caught up in the headlines.

Finally, its also worth noting that the perception of a bubble may stem from how data science itself is changing. As the field matures, the differentiation between roles becomes more pronounced. Job titles like data engineering, data analysis, business intelligence, machine learning engineering, and data science are more specific, and require a more niche skill set. This evolution can make the data science job market appear more volatile than it is, but in reality, companies just have a better understanding of their data science needs and can recruit for their specialities.

If you want a job in data science, go for it. Theres very little chance were actually in a bubble. The best thing you can do is, as Ive indicated, pick your specialty and develop your skills in that area. Data science is a broad field, spilling over into different industries, languages, job titles, responsibilities, and seniorities. Select a specialty, train the skills, prep for the interview, and secure the job.

Nate Rosidi is a data scientist and in product strategy. He's also an adjunct professor teaching analytics, and is the founder of StrataScratch, a platform helping data scientists prepare for their interviews with real interview questions from top companies. Nate writes on the latest trends in the career market, gives interview advice, shares data science projects, and covers everything SQL.

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Gilbane Building Company successfully completes construction of the University of Virginia’s cutting-edge School of … – PR Web

April 30, 2024 | Author: admin

"This beautiful and unique new space gives students, faculty, and staff a home base for research and teaching that intersects with schools and disciplines across Grounds and beyond," said University President Jim Ryan.

This four-story, 61,000-square-foot facility for the School of Data Science embodies the core principles of openness and collaboration that drive the school's mission. Designed with open hallways, a spacious atrium, monumental stairs, adaptive classrooms, and research and meeting areas, the building promotes a culture of innovation and teamwork.

"We are delighted to continue our partnership with UVA on this cutting-edge facility that creates opportunity for collaborative, open, and responsible data science research and education as the school's goals state," said Maggie Reed, Gilbane's Richmond, Virginia, Business Leader. "While we helped build a physical building to house the program, we are excited about what this school without walls will create in the future."

An interactive data sculpture, designed by SoSo Limited and built by Hypersonic, will be a unique feature of the building, allowing visitors to engage with data sets and explore various data stories. The sculpture, hanging from a skylight at the building's core, will be visible from the street and every floor.

Inclusivity is a priority, and the facility features a wellness space, a lactation space, and non-gendered restrooms, making it a welcoming environment for all.

Sustainability is a crucial focus, evident in the LEED Gold-certified green building design. Solar panels, daylighting strategies, and shading systems reduce the need for artificial lighting. The School of Data Science will receive 15% of its power from solar energy provided by four arrays of solar photovoltaic panels installed on the roof of its soon-to-open new home. This aligns with UVA's Sustainability Plan, aiming for carbon neutrality by 2030 and fossil fuel-free status by 2050.

About the University of Virginia UVA is an iconic public institution of higher education, boasting nationally ranked schools and programs, diverse and distinguished faculty, a major academic medical center, and a proud history as a renowned research university. The community and culture of the University are enriched by active student self-governance, sustained commitment to the arts, and a robust NCAA Division I Athletics program. In its third century, the University of Virginia offers an affordable, world-class education consistently ranked among the nation's best. As one of the nation's leading public institutions, UVA pushes the boundaries of what's possible always in the name of the greater good. One of the things that makes this possible is an unswerving commitment to initiatives that grow, strengthen, and shape our institution for the future. For more information visit: https://www.virginia.edu.

About Gilbane Building Company Gilbane provides a full slate of construction and facilities-related services from preconstruction services planning and integrated consulting capabilities to comprehensive construction management, general contracting, design-build, and facility management services for clients across various markets. Founded in 1870 and still a privately held, family-owned company, Gilbane has more than 45 office locations worldwide. For more information, visit http://www.gilbaneco.com.

About Gilbane Richmond Serving the Richmond community for over 50 years as a construction industry leader, Gilbane has the breadth of experience and local knowledge to partner with corporations, institutions, schools and universities, government agencies, and attractions in Virginia. Gilbane has constructed many of Richmond's local iconic landmarks and facilities, including the Virginia State Capitol Building Restoration and Extension, the Altria Corporate Headquarters Campus, the Virginia Commonwealth University Institute of Contemporary Art, CenterStage and Landmark Theatre, the Altria Theater Renovations as well as several major renovation projects for Capitol One on their West Creek campus.

Media Contact

Heidi K. Bodine, Gilbane Building Company, 407-204-4023, [emailprotected], http://www.gilbaneco.com

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Data Scientist Breakdown: Skills, Certifications, and Salary – KDnuggets

April 30, 2024 | Author: admin

A lot of us are worried about the demand for data scientists since the use of platforms such as ChatGPT. Over the past few years, companies have been laying off employees in the tech sector, and the big question everybody is asking is whether AI is the reason behind it.

In today's article, we will be speaking specifically about data science, and although there are challenges, those with data science skills have a more promising career longevity.

A study by 365datascience shows that data scientists made up 3% of those laid off by major tech companies. Other tech professionals such as software engineers, were affected more, at around 22%.

This statistic alone presents to us the crucial role data scientists play in advancing the tech industry.

A data scientist's job role focuses on statistics, machine learning, and artificial intelligence. Their business objective is to be able to use different data strategies and turn raw data into business insights that can be used in the decision-making process.

This can go from simple data analysis to building machine learning models.

A data scientist is skilled in mathematics, statistics, and computer science with expertise in a programming language such as Python or R.

As stated above, to become a data scientist, you will need to have a good foundational understanding of mathematics, and statistics along with a programming language.

What about computer science? Do I not need a degree for this?

In some cases yes, depending on where you are in the world. For example, in the UK a lot of companies desire a university degree. However, as the demand for data scientists continues to grow, organisations understand the low supply and are more than happy to take on people with the correct certifications and skills.

So what kind of certifications are these?

So whats the money like?

According to Glassdoor, updated on the 12th of April 2024 - the average salary for a data scientist in the US is $157,000, ranging from $132,00 to $190,000.

Please note that in this figure, only 37.8% of job postings announced their salary. Working in the tech industry, I have come across US data scientists with a salary between $160,000$200,000 annually.

However, salary is highly dependent on a range of factors:

The demand for data scientists will continue to grow and if you are somebody who is looking to transition into the tech industry with a career that has a higher chance of job security - data science is for you.

Dont worry about not having the right qualifications from University, you can gain the same experience, skills, and land a job with the certifications mentioned above!

Nisha Arya is a data scientist, freelance technical writer, and an editor and community manager for KDnuggets. She is particularly interested in providing data science career advice or tutorials and theory-based knowledge around data science. Nisha covers a wide range of topics and wishes to explore the different ways artificial intelligence can benefit the longevity of human life. A keen learner, Nisha seeks to broaden her tech knowledge and writing skills, while helping guide others.

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Invafresh names Andrew Cron Chief Data Officer – Supermarket News

April 30, 2024 | Author: admin

Invafresh today announced Andrew Cron, Ph.D., has joined the company as chief data officer.

Cron is an accomplished executive-level data scientist and technology thought leader with a proven track record in driving transformational business results by bridging the gap between cutting-edge technology innovation and real-world application.

He joins Invafresh from 84.51, Krogers retail data science, insights, and media company, where he was SVP, chief scientist. In this role, directing a 100-member cross functional team comprising of researchers, data scientists, and engineers, Cronbuilt and led the R&D vision and strategy for Krogers data analytics and data monetization subsidiary, enhancing Krogers in-store and supply chain operations, as well as CPG partner product performance. Prior to 84.51, Cronheld senior data scientist roles with Citadel LLC, Weinrub Analytics and MaxPoint.

I am excited to join the Invafresh team as this is a pivotal time to be in grocery retail technology given the applicability AI has in addressing industry-wide challenges, said Cron. I look forward to partnering with our customers around the world to transform AI technology into tangible business solutions that will help optimize their fresh food operations.

AI technologies are revolutionizing the way grocery retailers operate, enabling them to streamline supply chains, enhance customer experiences, drive labor efficiencies, reduce food waste, and use technology to guide and accelerate decision making, said Tim Spencer, chief executive officer at Invafresh. Invafresh is at the forefront of accelerating the AI transformation of fresh food retail operations worldwide and I look forward to Andrews leadership in strengthening our capabilities in this area to deliver meaningful value to our customers.

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The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A – Towards Data Science

April 30, 2024 | Author: admin

Since the launch of ChatGPT, the initial wave of generative AI applications has largely revolved around chatbots that utilize the Retrieval Augmented Generation (RAG) pattern to respond to user prompts. While there is ongoing work to enhance the robustness of these RAG-based systems, the research community is now exploring the next generation of AI applications a common theme being the development of autonomous AI agents.

Agentic systems incorporate advanced capabilities like planning, iteration, and reflection, which leverage the models inherent reasoning abilities to accomplish tasks end-to-end. Paired with the ability to use tools, plugins, and function calls agents are empowered to tackle a wider range of general-purpose work.

Reasoning is a foundational building block of the human mind. Without reasoning one would not be able to make decisions, solve problems, or refine plans when new information is learned essentially misunderstanding the world around us. If agents dont have strong reasoning skills then they might misunderstand their task, generate nonsensical answers, or fail to consider multi-step implications.

We find that most agent implementations contain a planning phase which invokes one of the following techniques to create a plan: task decomposition, multi-plan selection, external module-aided planning, reflection and refinement and memory-augmented planning [1].

Another benefit of utilizing an agent implementation over just a base language model is the agents ability to solve complex problems by calling tools. Tools can enable an agent to execute actions such as interacting with APIs, writing to third party applications, and more. Reasoning and tool calling are closely intertwined and effective tool calling has a dependency on adequate reasoning. Put simply, you cant expect an agent with poor reasoning abilities to understand when is the appropriate time to call its tools.

Our findings emphasize that both single-agent and multi-agent architectures can be used to solve challenging tasks by employing reasoning and tool calling steps.

For single agent implementations, we find that successful goal execution is contingent upon proper planning and self-correction [1, 2, 3, 4]. Without the ability to self-evaluate and create effective plans, single agents may get stuck in an endless execution loop and never accomplish a given task or return a result that does not meet user expectations [2]. We find that single agent architectures are especially useful when the task requires straightforward function calling and does not need feedback from another agent.

However, we note that single agent patterns often struggle to complete a long sequence of sub tasks or tool calls [5, 6]. Multi-agent patterns can address the issues of parallel tasks and robustness since multiple agents within the architecture can work on individual subproblems. Many multi-agent patterns start by taking a complex problem and breaking it down into several smaller tasks. Then, each agent works independently on solving each task using their own independent set of tools.

Architectures involving multiple agents present an opportunity for intelligent labor division based on capabilities as well as valuable feedback from diverse agent personas. Numerous multi-agent architectures operate in stages where teams of agents are dynamically formed and reorganized for each planning, execution, and evaluation phase [7, 8, 9]. This reorganization yields superior outcomes because specialized agents are utilized for specific tasks and removed when no longer required. By matching agent roles and skills to the task at hand, agent teams can achieve greater accuracy and reduce the time needed to accomplish the goal. Crucial features of effective multi-agent architectures include clear leadership within agent teams, dynamic team construction, and efficient information sharing among team members to prevent important information from getting lost amidst superfluous communication.

Our research highlights notable single agent methods such as ReAct, RAISE, Reflexion, AutoGPT + P, LATS, and multi agent implementations such as DyLAN, AgentVerse, and MetaGPT, which are explained more in depth in the full text.

Single Agent Patterns:

Single agent patterns are generally best suited for tasks with a narrowly defined list of tools and where processes are well-defined. They dont face poor feedback from other agents or distracting and unrelated chatter from other team members. However, single agents may get stuck in an execution loop and fail to make progress towards their goal if their reasoning and refinement capabilities arent robust.

Multi Agent Patterns:

Multi agent patterns are well-suited for tasks where feedback from multiple personas is beneficial in accomplishing the task. They are useful when parallelization across distinct tasks or workflows is required, allowing individual agents to proceed with their next steps without being hindered by the state of tasks handled by others.

Feedback and Human in the Loop

Language models tend to commit to an answer earlier in their response, which can cause a snowball effect of increasing diversion from their goal state [10]. By implementing feedback, agents are much more likely to correct their course and reach their goal. Human oversight improves the immediate outcome by aligning the agents responses more closely with human expectations, yielding more reliable and trustworthy results [11, 8]. Agents can be susceptible to feedback from other agents, even if the feedback is not sound. This can lead the agent team to generate a faulty plan which diverts them from their objective [12].

Information Sharing and Communication

Multi-agent patterns have a greater tendency to get caught up in niceties and ask one another things like how are you, while single agent patterns tend to stay focused on the task at hand since there is no team dynamic to manage. This can be mitigated by robust prompting. In vertical architectures, agents can fail to send critical information to their supporting agents not realizing the other agents arent privy to necessary information to complete their task. This failure can lead to confusion in the team or hallucination in the results. One approach to address this issue is to explicitly include information about access rights in the system prompt so that the agents have contextually appropriate interactions.

Impact of Role Definition and Dynamic Teams

Clear role definition is critical for both single and multi-agent architectures. Role definition ensures that the agents understands their assigned role, stay focused on the provided task, execute the proper tools, and minimizes hallucination of other capabilities. Establishing a clear group leader improves the overall performance of multi-agent teams by streamlining task assignment. Dynamic teams where agents are brought in and out of the system based on need have also been shown to be effective. This ensures that all agents participating in the tasks are strong contributors.

Summary of Key Insights

The key insights discussed suggest that the best agent architecture varies based on use case. Regardless of the architecture selected, the best performing agent systems tend to incorporate at least one of the following approaches: well defined system prompts, clear leadership and task division, dedicated reasoning / planning- execution evaluation phases, dynamic team structures, human or agentic feedback, and intelligent message filtering. Architectures that leverage these techniques are more effective across a variety of benchmarks and problem types.

Our meta-analysis aims to provide a holistic understanding of the current AI agent landscape and offer insight for those building with existing agent architectures or developing custom agent architectures. There are notable limitations and areas for future improvement in the design and development of autonomous AI agents such as a lack of comprehensive agent benchmarks, real world applicability, and the mitigation of harmful language model biases. These areas will need to be addressed in the near-term to enable reliable agents.

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The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A - Towards Data Science

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Computer science alumni judge senior capstones, provide insight to students – University of Colorado Boulder

April 30, 2024 | Author: admin

On Friday, April 26th, 29 computer science alumni judged current students' senior capstone projects at the Engineering Projects Expo.

Spending half an hour with three different groups, the judges provided insight as professionals into the students' presentations, their demos and their underlying code architecture.

Ryan Dowell (CompSci'11), who works for industrial textile machine company Melco, said he was impressed by the scale and variety of projects created by the students in partnership with their capstone clients.

"When I took the capstone course," Dowell said, "the biggest benefit was understanding the difference between something academic and something commercially-focused. The capstone meant a lot to me and helped me understand a lot. I hope it helps them understand where things go from here."

For Erika Bailon (CompSci'20) the event brought nostalgia.

"I remember the days I was working on this," she said. "It's amazing to see how everything is evolving and how the students are working together in a really good way."

Bailon, who works at NASA's Jet Propulsion Laboratory, said she hoped that by being a judge, she could help show that women have a voice in computer science.

"I remember so many times when I wanted to give up on this, and I didn't do it because I knew that there were people like me who made it, so I could do it too," she said.

Derek Rieger (CompSci'99), who works for Accelerate Learning, said it was an enjoyable experience on several levels. He said he was inspired by the students and their passion.

"It's easy to say, 'oh, I don't have time to judge' but it's worthwhile to be able to do stuff like this. I know not everyone can, but if you can create the margin for it, I would absolutely encourage you to do so," he said.

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Computer science alumni judge senior capstones, provide insight to students - University of Colorado Boulder

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