#problem solving agents in artificial intelligence | Explore Tumblr posts and blogs

furyblaze76tm · 6 months ago

Text

🪐🌿NATURäLaW

©️ TM @lawerystuff @waynefamilyreactions @waynestate @dukeupress @detroitlib

🪐QTNATURäL

✊🏿 Famous Step Shows

Step shows are a dynamic form of performance that showcase synchronized dance routines, often rooted in African-American traditions.

Step shows are a vibrant expression of culture, community, and artistry. They play a significant role in celebrating African-American heritage and fostering unity among participants.

Whether through competitive events or community gatherings, step shows continue to be a powerful platform for expression and activism.

If you have more questions or want to explore specific aspects of step shows, feel free to ask!

#### 1. **The Divine Nine Step Shows**

The **Divine Nine** refers to the nine historically Black Greek-letter organizations.

#### 2. **The Step Show at Howard University**

#### 3. **The National Step Show**

The **National Step Show** is an annual event that brings together step teams from across the United States.

✊🏿4. **Step Shows in Popular Media**

🗝️Campaign: Disability & Fabulous Public School

Step shows have also made their way into popular media, such as the documentary **"Step"**, which follows a high school step team in Baltimore as they prepare for a major competition. This film highlights the challenges and triumphs of the team, showcasing the role of stepping in fostering community and resilience.

#### 5. **Local and Regional Competitions**

Many colleges and universities host their own step shows, which can become quite famous within their regions. Events like the **Southern University Step Show** and the **Florida A&M University Step Show** are known for their high-energy performances and strong community support. These shows often serve as a platform for local talent and help to promote cultural pride.

#### Conclusion

Step shows are a vibrant expression of culture, community, and artistry. They play a significant role in celebrating African-American heritage and fostering unity among participants. Whether through competitive events or community gatherings, step shows continue to be a powerful platform for expression and activism. If you have more questions or want to explore specific aspects of step shows, feel free to ask!

🗝️Fabulous:Universal Housing: Affordable

🪶🐆Activists Who Have Used Kwanzaa as a Platform

Kwanzaa, created by activist Maulana Karenga in 1966, has been utilized by various activists and community leaders as a platform for promoting cultural pride, social justice, and community empowerment. Here are some notable figures and their contributions:

👩🏽‍🦱Activists : Dr. Jessica B. Harris

@jessicablackedbunny

**Dr. Jessica B. Harris**, a noted culinary historian and author, has also engaged with Kwanzaa as a platform for cultural expression. Through her writings and cookbooks, she highlights the significance of food in African-American culture and its role in Kwanzaa celebrations. By focusing on the culinary traditions associated with Kwanzaa, Harris promotes cultural heritage and community bonding, reinforcing the holiday's themes of unity and collective celebration.

👥 Community Leaders and Activists

Various community leaders and activists have embraced Kwanzaa to address social issues within their neighborhoods. Many local organizations host Kwanzaa events that include discussions on social justice, economic empowerment, and education. These gatherings often serve as platforms for activists to mobilize community members around pressing issues, such as police reform, economic inequality, and educational access. By integrating Kwanzaa celebrations with activism, these leaders reinforce the holiday's principles and encourage collective action.

🗝️FABULOUS:Universal Health Care

#### Kwanzaa in the Context of Broader Movements

Kwanzaa has also been referenced by activists involved in broader movements, such as the Black Lives Matter movement. The principles of Kwanzaa resonate with the movement's focus on self-determination, community empowerment, and the fight against systemic racism. Activists use Kwanzaa as a time to reflect on the ongoing struggles for civil rights and to inspire action within their communities.

#### Conclusion

In summary, Kwanzaa serves as a significant platform for various activists, including its founder Maulana Karenga and community leaders who promote cultural pride and social justice. By linking the holiday's principles to contemporary issues, these activists continue to use Kwanzaa as a means of fostering empowerment and collective action within African-American communities and beyond. If you have more specific questions or topics related to Kwanzaa and activism, feel free to ask!

2 notes · View notes

darkmaga-returns · 6 months ago

Text

Google has a “vision of a universal assistant,” but Mariner falls short. AI Agents are reputed to be the future of AI which autonomously “takes actions, adapts in real-time, and, solves multi-step problems based on context and objectives.” This is the technology that will destroy massive numbers of jobs in the future. ⁃ Patrick Wood, Editor.

Today, chatbots can answer questions, write poems and generate images. In the future, they could also autonomously perform tasks like online shopping and work with tools like spreadsheets.

Google on Wednesday unveiled a prototype of this technology, which artificial intelligence researchers call an A.I. agent.

Google is among the many tech companies building A.I. agents. Various A.I. start-ups, including OpenAI and Anthropic, have unveiled similar prototypes that can use software apps, websites and other online tools.

Google’s new prototype, called Mariner, is based on Gemini 2.0, which the company also unveiled on Wednesday. Gemini is the core technology that underpins many of the company’s A.I. products and research experiments. Versions of the system will power the company’s chatbot of the same name and A.I. Overviews, a Google search tool that directly answers user questions.

“We’re basically allowing users to type requests into their web browser and have Mariner take actions on their behalf,” Jaclyn Konzelmann, a Google project manager, said in an interview with The New York Times.

Gemini is what A.I researchers call a neural network — a mathematical system that can learn skills by analyzing enormous amounts of data. By recognizing patterns in articles and books culled from across the internet, for instance, a neural network can learn to generate text on its own.

The latest version of Gemini learns from a wide range of data, from text to images to sounds. That might include images showing how people use spreadsheets, shopping sites and other online services. Drawing on what Gemini has learned, Mariner can use similar services on behalf of computer users.

“It can understand that it needs to press a button to make something happen,” Demis Hassabis, who oversees Google’s core A.I. lab, said in an interview with The Times. “It can take action in the world.”

Mariner is designed to be used “with a human in the loop,” Ms. Konzelmann said. For instance, it can fill a virtual shopping cart with groceries if a user is in an active browser tab, but it will not actually buy the groceries. The user must make the purchase.

2 notes · View notes

religion-is-a-mental-illness · 2 years ago

Text

Religion has continuously declined worldwide in the 21st century, including in the United States. There are different ideas as to the factors that contribute to this decline. However, a recent paper suggests that automation in the form of artificial intelligence and robotics is a primary driver in the current downward trend of global religiosity.

In a recent paper, researchers argued that automation in the form of robotics and AI is the real driver of the recent trend. A series of experiments showed that exposure to automation at the national and local levels is linked to a reduction in religiosity.https://t.co/LklYwP0AyU — FFRF (@FFRF) August 31, 2023

An international team of researchers recently published a paper titled “Exposure to Automation Explains Religious Declines” in the Proceedings of the National Academy of Sciences (PNAS). The report sought to explain the decline of religion amidst the technological advancements in the 21st century.

“When people can use technology to predict the weather, diagnose and treat illness, and manufacture resources, they may rely less on religious beliefs and practices,” the paper said.

Although technological advancement has been considered a primary factor in religion’s decline globally, religiosity hasn’t seen a massive drop during historical periods where technology significantly developed, notably the Industrial Revolution, the Space Race during the Cold War, and the rise of Personal Computers.

The researchers presented a hypothesis as to why religiosity only dropped massively as recently as the 21st century. It’s not technology itself that reduces religiosity, but automation, mainly in AI and robotics, two technological advancements that only became prominent in the 21st century.

“This claim is based on recent research on lay perceptions of automation. Such studies show that people ascribe automation technology with abilities that border on supernatural,” the researchers wrote.

“Historically, people have deferred to supernatural agents and religious professionals to solve instrumental problems beyond the scope of human ability. These problems may seem more solvable for people working and living in highly automated spaces,” they added.

The researchers conducted four experiments to test their hypothesis. The first tracked religious decline from 2006 to 2019 across 68 countries through a survey question with more than two million respondents, which said, “Is religion an important part of your daily life?”

They reported that exposure to robotics “was robustly and negatively associated with religiosity across the globe.” The correlation remains valid even after factors such as GDP per capita, telecom development, and energy development have been considered.

The second experiment examined the decline in religious belief in the United States, comparing religiosity rates and robotics growth in metropolitan areas from 2008 to 2016.

“Metropolitan areas with higher levels of robotics growth (+1 standard deviation) experienced an approximately 3% yearly decline in religion each decade,” the paper said.

For the third experiment, the researchers followed 46,680 individuals in a specific community from 2009 to 2020, measuring their self-reported belief in God and their job exposure to automation. They discovered that the religiosity of individuals who worked at jobs with higher exposure to AI and robotics significantly dropped over time.

“People with jobs that were one standard deviation higher than the mean on occupational exposure to AI were 45% less likely to believe in God compared to people in occupations that had a mean level of exposure to AI,” the paper said.

The last experiment was conducted at the most local level. The researchers followed 238 employees in an organization, directly measuring their exposure to AI and religious beliefs. They found that AI exposure was connected to a drop in religious belief.

While the study's results were correlative and do not necessarily imply causation, they strongly support the researchers’ hypothesis that automation leads to a drop in religiosity.

“Our studies demonstrate that automation is linked to religious decline across multiple religious traditions (e.g., Christian, Muslim, and Buddhist), world regions (e.g., North America, South Asia, and Oceania), and levels of analysis,” they noted.

Any sufficiently advanced technology is indistinguishable from gods.

#AI #artificial intelligence #Atheist Republic #rise of the nones #leaving religion #empty the pews #decline of religion #religion #FFRF #Freedom from Religion Foundation #religion is a mental illness

18 notes · View notes

dhanasrivista · 8 months ago

Text

Generative AI’s Role in IT Service Management: A Game-Changer for Efficiency and Innovation

In the rapidly evolving landscape of IT Service Management (ITSM), emerging technologies continually reshape the way organizations deliver, manage, and optimize IT services. One of the most disruptive innovations today is Generative AI, which is transforming how IT professionals approach their tasks. By harnessing the capabilities of machine learning and artificial intelligence, Generative AI is enhancing service efficiency, improving user experience, and paving the way for more predictive and proactive IT operations.

Generative AI, which refers to AI models capable of producing new content, data, or solutions based on learned patterns from vast datasets, has significant implications for IT Service Management. With the rise of Generative AI certification, professionals can gain the skills needed to harness this transformative technology. It goes beyond traditional automation, enabling ITSM teams to move from reactive problem-solving to proactive service enhancement. This technology offers more than just automated responses; it introduces intelligent, data-driven insights that can optimize IT service delivery and innovation.

1. Enhancing Service Desk Operations

One of the most prominent roles of Generative AI in ITSM is its impact on service desk operations. The service desk is the frontline of IT support, managing a multitude of tickets, incidents, and requests daily. Traditionally, managing these operations required significant human effort, with support teams spending time on repetitive, low-value tasks such as ticket classification, incident management, and basic troubleshooting.

Generative AI, particularly through AI-powered chatbots and virtual agents, is revolutionizing these operations. These intelligent tools can process vast amounts of data from historical tickets and documentation, enabling them to resolve common issues, provide step-by-step guidance, and offer tailored responses to users. For example, instead of waiting for human intervention, a virtual agent can quickly resolve a password reset request or troubleshoot a network connectivity issue. By automating these tasks, IT service teams can focus on more complex issues, ultimately improving productivity and reducing response times. Enrolling in a Generative AI Course can provide deeper insights into how these technologies work and how to leverage them for improved IT service management.

Moreover, generative AI models can continuously learn from interactions, becoming more effective and accurate over time. As a result, the service desk can provide more consistent, 24/7 support to users, ensuring that even complex queries are addressed swiftly without the need for manual escalation.

2. Improving Incident Management and Resolution

Incident management is one of the core processes of ITSM, requiring prompt and efficient handling of issues to minimize downtime and service disruption. Generative AI is playing a crucial role in optimizing this process by providing predictive insights and automating parts of incident resolution.

AI models can analyze past incidents, detect patterns, and predict potential future issues before they escalate into major problems. This predictive capability allows IT teams to proactively address vulnerabilities and risks in the IT infrastructure, thus preventing costly downtime. Additionally, when incidents do occur, Generative AI can quickly suggest solutions or provide troubleshooting guides to service desk staff based on historical data and contextual analysis.

Generative AI also enhances collaboration by providing real-time insights and recommendations to various teams across the organization. For example, if an incident is reported, AI can instantly identify similar cases, suggest resolutions, or alert relevant teams about recurring patterns, significantly speeding up the resolution process.

3. Streamlining Change and Release Management

Change management in ITSM involves controlling and overseeing modifications to IT systems, services, or applications. It’s a delicate balance between innovation and maintaining system stability. Generative AI can assist by providing detailed risk assessments, forecasting potential impacts of proposed changes, and recommending the best timing or methods for implementation.

By analyzing past changes and their outcomes, AI models can identify the most effective strategies for rolling out new services or updates. This capability is particularly useful for release management, where AI can simulate the impact of changes across different environments before they are implemented in production. Generative AI models can also automate routine aspects of the release process, such as code testing or deployment verification, ensuring faster and more reliable updates.

4. Optimizing Knowledge Management

Effective knowledge management is vital for ITSM teams to resolve incidents swiftly and maintain high service levels. Generative AI plays a transformative role by not only indexing and searching knowledge repositories but also creating new knowledge artifacts based on the data it processes.

For instance, AI can analyze IT service logs, historical ticket data, and other internal documents to automatically generate new troubleshooting guides or best practices. This ensures that the knowledge base remains up to date, reducing the time IT professionals spend searching for solutions. Furthermore, AI-driven knowledge management can enhance training and onboarding by providing real-time, contextual learning experiences for new employees, helping them adapt to complex IT environments more quickly.

5. Facilitating IT Asset and Configuration Management

IT asset management and configuration management are critical for ensuring that IT services are delivered efficiently and securely. Generative AI can support these processes by automating the tracking and auditing of IT assets, enabling real-time updates to configuration management databases (CMDBs), and generating recommendations for optimizing resource utilization.

AI models can also provide insights into the lifecycle of IT assets, predicting when equipment or software may need maintenance or replacement. This proactive approach reduces the likelihood of service disruptions due to outdated or malfunctioning assets, ensuring smoother and more reliable service delivery.

6. Driving Continuous Service Improvement

Continuous service improvement (CSI) is a key principle in ITSM, focusing on the ongoing enhancement of IT services. Generative AI plays a vital role in this area by offering real-time analytics and insights that inform decision-making.

With access to vast amounts of data, Generative AI can identify trends, predict future service demands, and recommend ways to optimize performance. For example, it can analyze service response times, user feedback, and system performance metrics to highlight areas for improvement. This data-driven approach helps IT teams make informed decisions and implement strategies that align with business goals and user expectations.

Conclusion: The Future of IT Service Management with Generative AI

Generative AI is not just another tool in the ITSM toolkit; it represents a paradigm shift in how IT services are delivered and managed. By automating routine tasks, providing predictive insights, and enabling more proactive service management, Generative AI empowers IT teams to focus on innovation and continuous improvement. As AI technology continues to evolve, its role in ITSM will only grow, offering new opportunities for enhancing efficiency, reducing operational costs, and delivering superior user experiences.

Incorporating Generative AI into ITSM strategies is no longer optional but essential for organizations aiming to stay competitive in the digital age. As this technology becomes more integrated into IT operations, businesses will experience a new era of service management, characterized by increased automation, smarter decision-making, and a relentless focus on innovation.

#Generative AI Certification #Generative AI Course #Generative AI Training #Artificial Intelligence #Generative AI Technology #Generative AI Benefits #Generative AI in ITSM #Generative AI Importance

2 notes · View notes

riverandpeakventures · 2 years ago

Text

How AI Is Transforming Digital Marketing: A Deep Dive

In today's fast-paced digital landscape, staying ahead of the curve is not just an option; it's a necessity. For digital marketers, this means constantly adapting to new technologies and trends to ensure that they can connect with their audience effectively. One such game-changer that's reshaping the digital marketing landscape is Artificial Intelligence (AI). In this blog, we'll take a deep dive into how AI is transforming digital marketing.

Understanding Artificial Intelligence

Before we delve into its impact, let's briefly understand what AI is. AI refers to the development of computer systems that can perform tasks that typically require human intelligence. These tasks include problem-solving, learning, understanding natural language, and recognizing patterns.

Personalization at Scale

One of the most significant ways AI is transforming digital marketing is through hyper-personalization. Traditional marketing approaches often treated all customers similarly, but AI allows marketers to create personalized experiences at scale. AI analyzes vast amounts of data to understand consumer behavior, preferences, and habits. With this information, marketers can tailor content, recommendations, and offers to each individual, increasing engagement and conversion rates.

Enhanced Customer Insights

AI-powered analytics tools provide marketers with unprecedented insights into customer behavior. By tracking and analyzing user interactions with websites, emails, and social media, AI can uncover hidden patterns and trends. This valuable data enables marketers to make data-driven decisions, refine their strategies, and create content that resonates with their target audience.

Chatbots and Customer Support

Chatbots are becoming an integral part of customer support in the digital age. Powered by AI, chatbots can handle routine customer inquiries, provide product recommendations, and even complete transactions. They are available 24/7, improving customer service and freeing up human agents to focus on more complex issues.

Content Creation and Curation

AI is revolutionizing content creation. From generating product descriptions to writing news articles and blog posts, AI-driven algorithms can produce high-quality content quickly and efficiently. Additionally, AI helps in content curation by identifying trending topics and suggesting relevant articles for sharing on social media or inclusion in newsletters.

Predictive Analytics

AI can forecast future trends and customer behaviors through predictive analytics. By analyzing historical data, AI algorithms can make predictions about which marketing strategies are likely to succeed. This enables marketers to allocate resources more effectively and anticipate customer needs.

Ad Campaign Optimization

In the realm of digital advertising, AI-powered algorithms are optimizing ad campaigns like never before. They analyze user data in real-time to adjust ad placements, bids, and targeting to maximize ROI. This level of optimization ensures that marketing budgets are spent efficiently.

Final Thoughts

Artificial Intelligence is not just a buzzword; it's a transformative force in digital marketing. By leveraging AI, marketers can enhance personalization, gain deeper insights, improve customer support, streamline content creation, make data-driven decisions, and optimize ad campaigns. As the digital marketing landscape continues to evolve, embracing AI is not just an option—it's the path to staying competitive and relevant in the digital age. So, if you're a digital marketer, now is the time to explore and integrate AI into your strategies and campaigns. It's the future, and it's here to stay.

#digital marketing #artificial intelligence #ai #digital media #digital illustration #business growth #marketing

2 notes · View notes

snapdougles · 6 months ago

Text

okay so I'm gonna try keep this brief so I'm not here forever, but here's the thing, this isn't new, actually people have tried using computers for therapy since the 60s:

Meet ELIZA

ELIZA is a simple computer program that responds to questions it is asked in a way meant to emulate a therapist, it did this so well, even it's creator was concerned at how quickly people started to empathize with it and consider it to not be a machine but a sentient being.

Humans are really good at recognising human like traits, and latching on to them, we decide that things with traits like us, are humanlike like us, similiarly, things that lack those human traits, will have their humanlikeness diminished.

This is great at finding likeminded tribes, terrible at identifying any sort of truth though.

To start my next point, I'm just gonna preface it with yeah therapy is good, therapists are good, it's not an easy job, and therapy with a person probably better than with an 'AI', but saying 'bots' (i don't think AI is the right description here,) can't help is just not correct, as measurably, they can, and do.

One of the advantages of therapy is having someone to prompt you to think in ways that you aren't used to, therapists can't peer magically into peoples minds, they are prompting the patient to express their experience, and try to help them to realize things they might not be aware of

"And how does that make you feel?"

Immediate follow up, obviously therapists do way more than this, this isn't gonna help everyone, and it isn't gonna automatically solve problems, but a lot of the time people just need help reframing issues into terms that they can understand and deal with, and bots can help with that.

Whether you think this is good/bad, right/wrong or useful at all is up to you, but ruling it out as being non-functional because "ai bad" is bad praxis.

sources:

guys. please

#i hate how buzzwordy everything is nowaways jeez #“oh it uses AI? Must be bad don't use it”#ELIZA #and again let's reiterate #a bot won't have the experience or competence to deal with everyone's problems #but a lot of peoples problems are simpler than they realize and having an external agent to interact with is enough to figure it out

79K notes · View notes

jcmarchi · 5 months ago

Text

DeepMind’s Mind Evolution: Empowering Large Language Models for Real-World Problem Solving

New Post has been published on https://thedigitalinsider.com/deepminds-mind-evolution-empowering-large-language-models-for-real-world-problem-solving/

DeepMind’s Mind Evolution: Empowering Large Language Models for Real-World Problem Solving

In recent years, artificial intelligence (AI) has emerged as a practical tool for driving innovation across industries. At the forefront of this progress are large language models (LLMs) known for their ability to understand and generate human language. While LLMs perform well at tasks like conversational AI and content creation, they often struggle with complex real-world challenges requiring structured reasoning and planning.

For instance, if you ask LLMs to plan a multi-city business trip that involves coordinating flight schedules, meeting times, budget constraints, and adequate rest, they can provide suggestions for individual aspects. However, they often face challenges in integrating these aspects to effectively balance competing priorities. This limitation becomes even more apparent as LLMs are increasingly used to build AI agents capable of solving real-world problems autonomously.

Google DeepMind has recently developed a solution to address this problem. Inspired by natural selection, this approach, known as Mind Evolution, refines problem-solving strategies through iterative adaptation. By guiding LLMs in real-time, it allows them to tackle complex real-world tasks effectively and adapt to dynamic scenarios. In this article, we’ll explore how this innovative method works, its potential applications, and what it means for the future of AI-driven problem-solving.

Why LLMs Struggle With Complex Reasoning and Planning

LLMs are trained to predict the next word in a sentence by analyzing patterns in large text datasets, such as books, articles, and online content. This allows them to generate responses that appear logical and contextually appropriate. However, this training is based on recognizing patterns rather than understanding meaning. As a result, LLMs can produce text that appears logical but struggle with tasks that require deeper reasoning or structured planning.

The core limitation lies in how LLMs process information. They focus on probabilities or patterns rather than logic, which means they can handle isolated tasks—like suggesting flight options or hotel recommendations—but fail when these tasks need to be integrated into a cohesive plan. This also makes it difficult for them to maintain context over time. Complex tasks often require keeping track of previous decisions and adapting as new information arises. LLMs, however, tend to lose focus in extended interactions, leading to fragmented or inconsistent outputs.

How Mind Evolution Works

DeepMind’s Mind Evolution addresses these shortcomings by adopting principles from natural evolution. Instead of producing a single response to a complex query, this approach generates multiple potential solutions, iteratively refines them, and selects the best outcome through a structured evaluation process. For instance, consider team brainstorming ideas for a project. Some ideas are great, others less so. The team evaluates all ideas, keeping the best and discarding the rest. They then improve the best ideas, introduce new variations, and repeat the process until they arrive at the best solution. Mind Evolution applies this principle to LLMs.

Here’s a breakdown of how it works:

Generation: The process begins with the LLM creating multiple responses to a given problem. For example, in a travel-planning task, the model may draft various itineraries based on budget, time, and user preferences.

Evaluation: Each solution is assessed against a fitness function, a measure of how well it satisfies the tasks’ requirements. Low-quality responses are discarded, while the most promising candidates advance to the next stage.

Refinement: A unique innovation of Mind Evolution is the dialogue between two personas within the LLM: the Author and the Critic. The Author proposes solutions, while the Critic identifies flaws and offers feedback. This structured dialogue mirrors how humans refine ideas through critique and revision. For example, if the Author suggests a travel plan that includes a restaurant visit exceeding the budget, the Critic points this out. The Author then revises the plan to address the Critic’s concerns. This process enables LLMs to perform deep analysis which it could not perform previously using other prompting techniques.

Iterative Optimization: The refined solutions undergo further evaluation and recombination to produce refined solutions.

By repeating this cycle, Mind Evolution iteratively improves the quality of solutions, enabling LLMs to address complex challenges more effectively.

Mind Evolution in Action

DeepMind tested this approach on benchmarks like TravelPlanner and Natural Plan. Using this approach, Google’s Gemini achieved a success rate of 95.2% on TravelPlanner which is an outstanding improvement from a baseline of 5.6%. With the more advanced Gemini Pro, success rates increased to nearly 99.9%. This transformative performance shows the effectiveness of mind evolution in addressing practical challenges.

Interestingly, the model’s effectiveness grows with task complexity. For instance, while single-pass methods struggled with multi-day itineraries involving multiple cities, Mind Evolution consistently outperformed, maintaining high success rates even as the number of constraints increased.

Challenges and Future Directions

Despite its success, Mind Evolution is not without limitations. The approach requires significant computational resources due to the iterative evaluation and refinement processes. For example, solving a TravelPlanner task with Mind Evolution consumed three million tokens and 167 API calls—substantially more than conventional methods. However, the approach remains more efficient than brute-force strategies like exhaustive search.

Additionally, designing effective fitness functions for certain tasks could be a challenging task. Future research may focus on optimizing computational efficiency and expanding the technique’s applicability to a broader range of problems, such as creative writing or complex decision-making.

Another interesting area for exploration is the integration of domain-specific evaluators. For instance, in medical diagnosis, incorporating expert knowledge into the fitness function could further enhance the model’s accuracy and reliability.

Applications Beyond Planning

Although Mind Evolution is mainly evaluated on planning tasks, it could be applied to various domains, including creative writing, scientific discovery, and even code generation. For instance, researchers have introduced a benchmark called StegPoet, which challenges the model to encode hidden messages within poems. Although this task remains difficult, Mind Evolution exceeds traditional methods by achieving success rates of up to 79.2%.

The ability to adapt and evolve solutions in natural language opens new possibilities for tackling problems that are difficult to formalize, such as improving workflows or generating innovative product designs. By employing the power of evolutionary algorithms, Mind Evolution provides a flexible and scalable framework for enhancing the problem-solving capabilities of LLMs.

The Bottom Line

DeepMind’s Mind Evolution introduces a practical and effective way to overcome key limitations in LLMs. By using iterative refinement inspired by natural selection, it enhances the ability of these models to handle complex, multi-step tasks that require structured reasoning and planning. The approach has already shown significant success in challenging scenarios like travel planning and demonstrates promise across diverse domains, including creative writing, scientific research, and code generation. While challenges like high computational costs and the need for well-designed fitness functions remain, the approach provides a scalable framework for improving AI capabilities. Mind Evolution sets the stage for more powerful AI systems capable of reasoning and planning to solve real-world challenges.

0 notes

mariacallous · 2 months ago

Text

A young entrepreneur who was among the earliest known recruiters for Elon Musk’s so-called Department of Government Efficiency (DOGE) has a new, related gig—and he’s hiring. Anthony Jancso, cofounder of AccelerateX, a government tech startup, is looking for technologists to work on a project that aims to have artificial intelligence perform tasks that are currently the responsibility of tens of thousands of federal workers.

Jancso, a former Palantir employee, wrote in a Slack with about 2000 Palantir alumni in it that he’s hiring for a “DOGE orthogonal project to design benchmarks and deploy AI agents across live workflows in federal agencies,” according to an April 21 post reviewed by WIRED. Agents are programs that can perform work autonomously.

“We’ve identified over 300 roles with almost full-process standardization, freeing up at least 70k FTEs for higher-impact work over the next year,” he continued, essentially claiming that tens of thousands of federal employees could see many aspects of their job automated and replaced by these AI agents. Workers for the project, he wrote, would be based on site in Washington, DC, and would not require a security clearance; it isn’t clear for whom they would work. Palantir did not respond to requests for comment.

The post was not well received. Eight people reacted with clown face emojis, three reacted with a custom emoji of a man licking a boot, two reacted with custom emoji of Joaquin Phoenix giving a thumbs down in the movie Gladiator, and three reacted with a custom emoji with the word “Fascist.” Three responded with a heart emoji.

“DOGE does not seem interested in finding ‘higher impact work’ for federal employees,” one person said in a comment that received 11 heart reactions. “You’re complicit in firing 70k federal employees and replacing them with shitty autocorrect.”

“Tbf we’re all going to be replaced with shitty autocorrect (written by chatgpt),” another person commented, which received one “+1” reaction.

“How ‘DOGE orthogonal’ is it? Like, does it still require Kremlin oversight?” another person said in a comment that received five reactions with a fire emoji. “Or do they just use your credentials to log in later?”

Got a Tip?Are you a current or former government employee who wants to talk about what's happening? We'd like to hear from you. Using a nonwork phone or computer, contact the reporter securely on Signal at carolinehaskins.61 and vittoria89.82.

AccelerateX was originally called AccelerateSF, which VentureBeat reported in 2023 had received support from OpenAI and Anthropic. In its earliest incarnation, AccelerateSF hosted a hackathon for AI developers aimed at using the technology to solve San Francisco’s social problems. According to a 2023 Mission Local story, for instance, Jancso proposed that using large language models to help businesses fill out permit forms to streamline the construction paperwork process might help drive down housing prices. (OpenAI did not respond to a request for comment. Anthropic spokesperson Danielle Ghiglieri tells WIRED that the company "never invested in AccelerateX/SF,” but did sponsor a hackathon AccelerateSF hosted in 2023 by providing free access to its API usage at a time when its Claude API “was still in beta.”)

In 2024, the mission pivoted, with the venture becoming known as AccelerateX. In a post on X announcing the change, the company posted, “Outdated tech is dragging down the US Government. Legacy vendors sell broken systems at increasingly steep prices. This hurts every American citizen.” AccelerateX did not respond to a request for comment.

According to sources with direct knowledge, Jancso disclosed that AccelerateX had signed a partnership agreement with Palantir in 2024. According to the LinkedIn of someone described as one of AccelerateX’s cofounders, Rachel Yee, the company looks to have received funding from OpenAI’s Converge 2 Accelerator. Another of AccelerateSF’s cofounders, Kay Sorin, now works for OpenAI, having joined the company several months after that hackathon. Sorin and Yee did not respond to requests for comment.

Jancso’s cofounder, Jordan Wick, a former Waymo engineer, has been an active member of DOGE, appearing at several agencies over the past few months, including the Consumer Financial Protection Bureau, National Labor Relations Board, the Department of Labor, and the Department of Education. In 2023, Jancso attended a hackathon hosted by ScaleAI; WIRED found that another DOGE member, Ethan Shaotran, also attended the same hackathon.

Since its creation in the first days of the second Trump administration, DOGE has pushed the use of AI across agencies, even as it has sought to cut tens of thousands of federal jobs. At the Department of Veterans Affairs, a DOGE associate suggested using AI to write code for the agency’s website; at the General Services Administration, DOGE has rolled out the GSAi chatbot; the group has sought to automate the process of firing government employees with a tool called AutoRIF; and a DOGE operative at the Department of Housing and Urban Development is using AI tools to examine and propose changes to regulations. But experts say that deploying AI agents to do the work of 70,000 people would be tricky if not impossible.

A federal employee with knowledge of government contracting, who spoke to WIRED on the condition of anonymity because they were not authorized to speak to the press, says, “A lot of agencies have procedures that can differ widely based on their own rules and regulations, and so deploying AI agents across agencies at scale would likely be very difficult.”

Oren Etzioni, cofounder of the AI startup Vercept, says that while AI agents can be good at doing some things—like using an internet browser to conduct research—their outputs can still vary widely and be highly unreliable. For instance, customer service AI agents have invented nonexistent policies when trying to address user concerns. Even research, he says, requires a human to actually make sure what the AI is spitting out is correct.

“We want our government to be something that we can rely on, as opposed to something that is on the absolute bleeding edge,” says Etzioni. “We don't need it to be bureaucratic and slow, but if corporations haven't adopted this yet, is the government really where we want to be experimenting with the cutting edge AI?”

Etzioni says that AI agents are also not great 1-1 fits for job replacements. Rather, AI is able to do certain tasks or make others more efficient, but the idea that the technology could do the jobs of 70,000 employees would not be possible. “Unless you're using funny math,” he says, “no way.”

Jancso, first identified by WIRED in February, was one of the earliest recruiters for DOGE in the months before Donald Trump was inaugurated. In December, Jancso, who sources told WIRED said he had been recruited by Steve Davis, president of the Musk-founded Boring Company and a current member of DOGE, used the Palantir alumni group to recruit DOGE members. On December 2nd, 2024, he wrote, “I’m helping Elon’s team find tech talent for the Department of Government Efficiency (DOGE) in the new admin. This is a historic opportunity to build an efficient government, and to cut the federal budget by 1/3. If you’re interested in playing a role in this mission, please reach out in the next few days.”

According to one source at SpaceX, who asked to remain anonymous as they are not authorized to speak to the press, Jancso appeared to be one of the DOGE members who worked out of the company’s DC office in the days before inauguration along with several other people who would constitute some of DOGE’s earliest members. SpaceX did not respond to a request for comment.

Palantir was cofounded by Peter Thiel, a billionaire and longtime Trump supporter with close ties to Musk. Palantir, which provides data analytics tools to several government agencies including the Department of Defense and the Department of Homeland Security, has received billions of dollars in government contracts. During the second Trump administration, the company has been involved in helping to build a “mega API” to connect data from the Internal Revenue Service to other government agencies, and is working with Immigration and Customs Enforcement to create a massive surveillance platform to identify immigrants to target for deportation.

10 notes · View notes

coursebykevin123 · 2 days ago

Text

Jason Zhou – AI Builder Club March 2025: Build, Automate, and Scale with Next-Gen AI Systems

Artificial Intelligence isn’t the future anymore—it’s the present. And those who master AI tools today are the ones shaping tomorrow’s businesses, products, and customer experiences. The Jason Zhou – AI Builder Club March 2025 course is your gateway to becoming one of those pioneers.

Whether you're a developer, solopreneur, startup founder, or digital creator, the Jason Zhou – AI Builder Club March 2025 Online Program teaches you how to build, deploy, and monetize powerful AI-driven tools, agents, and workflows—all using cutting-edge no-code and low-code technologies.

What is the Jason Zhou – AI Builder Club March 2025 Program?

The Jason Zhou – AI Builder Club March 2025 Program is an advanced online learning experience created to help ambitious individuals and teams build custom AI tools for real-world application. It covers everything from AI automations to building custom GPTs and launching agent-based products.

With a focus on practical builds, the course guides you in creating market-ready AI projects. These could be:

Automated customer service agents

Content generation tools

AI-powered data dashboards

Lead generation bots

SaaS MVPs using GPT, Claude, or Gemini

Whether you’re a tech-savvy entrepreneur or someone looking to break into AI development with little coding knowledge, this course gives you a step-by-step blueprint.

Meet the Creator: Jason Zhou

Jason Zhou is a rising name in the AI builder space, known for his actionable and technical insights shared across platforms like Twitter, YouTube, and his AI community. He’s built dozens of AI agents, automated systems, and monetized tools using both open-source and commercial models like ChatGPT, Claude, Mistral, and LLM APIs.

What sets Jason apart is that he doesn’t just teach AI theory. He builds real tools, ships products, and shows you exactly how to do the same—inside the AI Builder Club March 2025 Online Course By Jason Zhou.

What Will You Learn Inside the Course?

The Jason Zhou – AI Builder Club March 2025 Online Course is designed to be hands-on and high-impact. It’s less about lectures and more about building real tools you can use or sell.

Here’s what’s covered:

🔹 Module 1: Understanding the AI Tool Stack

Overview of the current AI landscape

Choosing the right LLM: OpenAI, Claude, Gemini, Mistral

Prompt engineering and chaining logic

What makes an AI tool actually useful to users

🔹 Module 2: No-Code & Low-Code Development

Using tools like Make, Zapier, Retool, and Bubble

Creating UI/UX for AI-powered SaaS tools

Building backend logic with APIs and scripting

Hosting and scaling tools using affordable stacks

🔹 Module 3: Building Your First AI Product

Project-based learning: real GPT-powered app builds

Templates for newsletter generators, copywriting tools, and outreach bots

Integrating Google Sheets, Notion, Slack, and other apps

How to deploy your MVP in under a week

🔹 Module 4: AI Agents and Automations

Creating memory-based agents for long-term conversations

Setting up multi-step decision workflows

Building business process agents for clients

Using embeddings and vector search for smarter output

🔹 Module 5: Monetization & Launch Strategies

How to package and sell your AI tools

Finding profitable problems to solve

Jason’s launch playbook: Gumroad, Product Hunt, Twitter

Pricing models, freemium vs. paid, and building an audience

🔹 Bonuses and Extras

Live recorded sessions with community Q&A

Code snippets and project repositories

Pre-built templates to kickstart your own projects

Discord access to the AI Builder Club community

Who Should Take This Course?

The AI Builder Club March 2025 Online Program By Jason Zhou is perfect for:

✅ Indie hackers and solopreneurs who want to build and sell AI tools

✅ Developers and engineers ready to learn no-code/low-code workflows

✅ Agencies and consultants who want to offer AI services

✅ Content creators and marketers who want to automate tasks

✅ Anyone interested in launching their first AI project in weeks, not months

You don’t need deep technical skills. If you understand how to use basic tools and APIs, you can follow along and build powerful systems.

Why AI Builder Club March 2025 Is a Game-Changer

The Jason Zhou – AI Builder Club March 2025 Online Program isn’t your average “AI 101” course. It’s an execution-based masterclass for building real-world tools that deliver value and create income.

What sets it apart?

🛠 Project-Based Learning: You’ll finish the course with actual AI tools, not just notes.

🔁 Updated for March 2025: Covers the latest changes in GPT-4o, Claude 3.5, open-source models, and tool integrations.

📦 Monetization Focus: Learn not just to build—but to launch and earn.

⚙️ Template Driven: Pre-built frameworks accelerate your learning and implementation.

🤝 Community Access: Get direct feedback and support from builders just like you.

You’ll walk away with both the knowledge and the tools to launch your own AI business or automate your company’s internal operations.

Student Reviews and Results

“Before this course, I had no idea how to build with GPT. After just a few weeks, I launched a content repurposing AI tool that’s now making passive income.” “Jason makes complex workflows simple and fun. The AI Builder Club March 2025 Online Course By Jason Zhou changed the way I think about automation.” “The best course I’ve taken on practical AI applications. No fluff. Just build, ship, and launch.”

Where to Buy the Course

The Jason Zhou – AI Builder Club March 2025 Online Course is available now from trusted platforms.

👉 We recommend buying directly from ECOMKEVIN COURSE

This platform ensures secure checkout, immediate access, and all bonus material included.

Final Thoughts

AI isn’t a buzzword anymore — it’s a core skill for entrepreneurs and digital professionals. The

What is the Jason Zhou – AI Builder Club March 2025 Program?

With a focus on practical builds, the course guides you in creating market-ready AI projects. These could be:

Automated customer service agents

Content generation tools

AI-powered data dashboards

Lead generation bots

SaaS MVPs using GPT, Claude, or Gemini

Whether you’re a tech-savvy entrepreneur or someone looking to break into AI development with little coding knowledge, this course gives you a step-by-step blueprint.

Meet the Creator: Jason Zhou

What Will You Learn Inside the Course?

The Jason Zhou – AI Builder Club March 2025 Online Course is designed to be hands-on and high-impact. It’s less about lectures and more about building real tools you can use or sell.

Here’s what’s covered:

🔹 Module 1: Understanding the AI Tool Stack

Overview of the current AI landscape

Choosing the right LLM: OpenAI, Claude, Gemini, Mistral

Prompt engineering and chaining logic

What makes an AI tool actually useful to users

🔹 Module 2: No-Code & Low-Code Development

Using tools like Make, Zapier, Retool, and Bubble

Creating UI/UX for AI-powered SaaS tools

Building backend logic with APIs and scripting

Hosting and scaling tools using affordable stacks

🔹 Module 3: Building Your First AI Product

Project-based learning: real GPT-powered app builds

Templates for newsletter generators, copywriting tools, and outreach bots

Integrating Google Sheets, Notion, Slack, and other apps

How to deploy your MVP in under a week

🔹 Module 4: AI Agents and Automations

Creating memory-based agents for long-term conversations

Setting up multi-step decision workflows

Building business process agents for clients

Using embeddings and vector search for smarter output

🔹 Module 5: Monetization & Launch Strategies

How to package and sell your AI tools

Finding profitable problems to solve

Jason’s launch playbook: Gumroad, Product Hunt, Twitter

Pricing models, freemium vs. paid, and building an audience

🔹 Bonuses and Extras

Live recorded sessions with community Q&A

Code snippets and project repositories

Pre-built templates to kickstart your own projects

Discord access to the AI Builder Club community

Who Should Take This Course?

The AI Builder Club March 2025 Online Program By Jason Zhou is perfect for:

✅ Indie hackers and solopreneurs who want to build and sell AI tools

✅ Developers and engineers ready to learn no-code/low-code workflows

✅ Agencies and consultants who want to offer AI services

✅ Content creators and marketers who want to automate tasks

✅ Anyone interested in launching their first AI project in weeks, not months

You don’t need deep technical skills. If you understand how to use basic tools and APIs, you can follow along and build powerful systems.

Why AI Builder Club March 2025 Is a Game-Changer

What sets it apart?

🛠 Project-Based Learning: You’ll finish the course with actual AI tools, not just notes.

🔁 Updated for March 2025: Covers the latest changes in GPT-4o, Claude 3.5, open-source models, and tool integrations.

📦 Monetization Focus: Learn not just to build—but to launch and earn.

⚙️ Template Driven: Pre-built frameworks accelerate your learning and implementation.

🤝 Community Access: Get direct feedback and support from builders just like you.

You’ll walk away with both the knowledge and the tools to launch your own AI business or automate your company’s internal operations.

Student Reviews and Results

Where to Buy the Course

The Jason Zhou – AI Builder Club March 2025 Online Course is available now from trusted platforms.

👉 We recommend buying directly from ECOMKEVIN COURSE

This platform ensures secure checkout, immediate access, and all bonus material included.

Final Thoughts

AI isn’t a buzzword anymore — it’s a core skill for entrepreneurs and digital professionals. The

What is the Jason Zhou – AI Builder Club March 2025 Program?

With a focus on practical builds, the course guides you in creating market-ready AI projects. These could be:

Automated customer service agents

Content generation tools

AI-powered data dashboards

Lead generation bots

SaaS MVPs using GPT, Claude, or Gemini

Whether you’re a tech-savvy entrepreneur or someone looking to break into AI development with little coding knowledge, this course gives you a step-by-step blueprint.

Meet the Creator: Jason Zhou

What Will You Learn Inside the Course?

The Jason Zhou – AI Builder Club March 2025 Online Course is designed to be hands-on and high-impact. It’s less about lectures and more about building real tools you can use or sell.

Here’s what’s covered:

🔹 Module 1: Understanding the AI Tool Stack

Overview of the current AI landscape

Choosing the right LLM: OpenAI, Claude, Gemini, Mistral

Prompt engineering and chaining logic

What makes an AI tool actually useful to users

🔹 Module 2: No-Code & Low-Code Development

Using tools like Make, Zapier, Retool, and Bubble

Creating UI/UX for AI-powered SaaS tools

Building backend logic with APIs and scripting

Hosting and scaling tools using affordable stacks

🔹 Module 3: Building Your First AI Product

Project-based learning: real GPT-powered app builds

Templates for newsletter generators, copywriting tools, and outreach bots

Integrating Google Sheets, Notion, Slack, and other apps

How to deploy your MVP in under a week

🔹 Module 4: AI Agents and Automations

Creating memory-based agents for long-term conversations

Setting up multi-step decision workflows

Building business process agents for clients

Using embeddings and vector search for smarter output

🔹 Module 5: Monetization & Launch Strategies

How to package and sell your AI tools

Finding profitable problems to solve

Jason’s launch playbook: Gumroad, Product Hunt, Twitter

Pricing models, freemium vs. paid, and building an audience

🔹 Bonuses and Extras

Live recorded sessions with community Q&A

Code snippets and project repositories

Pre-built templates to kickstart your own projects

Discord access to the AI Builder Club community

Who Should Take This Course?

The AI Builder Club March 2025 Online Program By Jason Zhou is perfect for:

✅ Indie hackers and solopreneurs who want to build and sell AI tools

✅ Developers and engineers ready to learn no-code/low-code workflows

✅ Agencies and consultants who want to offer AI services

✅ Content creators and marketers who want to automate tasks

✅ Anyone interested in launching their first AI project in weeks, not months

You don’t need deep technical skills. If you understand how to use basic tools and APIs, you can follow along and build powerful systems.

Why AI Builder Club March 2025 Is a Game-Changer

What sets it apart?

🛠 Project-Based Learning: You’ll finish the course with actual AI tools, not just notes.

🔁 Updated for March 2025: Covers the latest changes in GPT-4o, Claude 3.5, open-source models, and tool integrations.

📦 Monetization Focus: Learn not just to build—but to launch and earn.

⚙️ Template Driven: Pre-built frameworks accelerate your learning and implementation.

🤝 Community Access: Get direct feedback and support from builders just like you.

You’ll walk away with both the knowledge and the tools to launch your own AI business or automate your company’s internal operations.

Student Reviews and Results

Where to Buy the Course

The Jason Zhou – AI Builder Club March 2025 Online Course is available now from trusted platforms.

👉 We recommend buying directly from ECOMKEVIN COURSE

This platform ensures secure checkout, immediate access, and all bonus material included.

Final Thoughts

AI isn’t a buzzword anymore — it’s a core skill for entrepreneurs and digital professionals. The Jason Zhou – AI Builder Club March 2025 Program gives you the tools, strategies, and step-by-step projects to turn ideas into fully functional AI products.

Whether you want to automate workflows, build your first AI SaaS, or generate income by solving niche problems with smart tools—this course is your complete blueprint.

If you’re serious about AI, now is the time to act. Get started with Jason Zhou’s most practical and powerful course yet — and start building the future with your own hands.

gives you the tools, strategies, and step-by-step projects to turn ideas into fully functional AI products.

Whether you want to automate workflows, build your first AI SaaS, or generate income by solving niche problems with smart tools—this course is your complete blueprint.

If you’re serious about AI, now is the time to act. Get started with Jason Zhou’s most practical and powerful course yet — and start building the future with your own hands.

gives you the tools, strategies, and step-by-step projects to turn ideas into fully functional AI products.

Whether you want to automate workflows, build your first AI SaaS, or generate income by solving niche problems with smart tools—this course is your complete blueprint.

If you’re serious about AI, now is the time to act. Get started with Jason Zhou’s most practical and powerful course yet — and start building the future with your own hands.

0 notes

educationalmafia · 3 days ago

Text

Unlocking the Power of Autonomous AI: Why Agentic AI Certification Is Your Career Catalyst

We’re entering a new era in artificial intelligence, one where AI doesn’t just respond but acts. Welcome to the world of Agentic AI, where machines can make decisions, adapt in real time, and operate autonomously without human prompts. 🤖⚡

This next-gen AI model is already being adopted by top organizations across India, Southeast Asia, and the United States, reshaping how industries manage data, workflows, and customer experiences. If you want to stay competitive and future-proof your career, becoming a certified agentic ai professional is the move you need to make. 💼🌟

💡 What Makes Agentic AI Different?

While traditional AI depends on fixed models and static input, Agentic AI is about initiative and adaptability. These systems can:

Set goals

Solve problems without instruction

Continuously learn and improve

Make decisions based on context

Imagine automating a business process not just with rules but with thinking agents that optimize outcomes in real time. That’s what’s drawing so many companies in India’s tech cities, Southeast Asia’s startups, and U.S.-based enterprises to invest in agentic AI professionals. 🌐🔥

🎓 About the Certification

The GSDC’s Agentic AI Certification for Professionals is a global program designed to make you job-ready with:

✅ A deep understanding of autonomous systems ✅ Exposure to real-world agentic tools ✅ Practical project-based learning ✅ Flexibility through agentic ai certification online access ✅ A wallet-friendly agentic ai certification cost

This agentic ai certificate course is 100% online and tailored for working professionals—learn at your pace, from anywhere! 📚💻

🚀 Why This Certification Is a Career Game-Changer

✔️ Industry Recognition – Stand out with global credentials ✔️ Career Growth – Access in-demand roles like Agent Developer, AI Strategist, and Automation Consultant ✔️ Global Relevance – Skills aligned with opportunities in India, Singapore, U.S., and beyond ✔️ Practical Mastery – Learn how to build and deploy real agentic AI solutions ✔️ Network Access – Join a thriving community of innovators and thought leaders

If you're a tech professional looking to reskill or a student entering the AI field, this course gives you the momentum to thrive.

👥 Who Should Enroll?

This certification is ideal for:

AI & ML Engineers

Software Developers

Product Managers

Data Analysts

Business Consultants

Fresh Graduates

Becoming a certified agentic ai professional means you’re ready to lead automation in the world’s most innovative companies.

✨ The Future Belongs to the Autonomous

From smart assistants to goal-driven robots, Agentic AI is changing the way we think about intelligence. Equip yourself with the skills that employers are actively seeking.

#AgenticAI #AgenticAIProfessional #CertifiedAgenticAI #AITraining #AIinIndia #AIinUSA #FutureReady #AutomationSkills #AICertification #GSDC #AICareer #AIUpskill

For more details : https://www.gsdcouncil.org/agentic-ai-certification

Contact no : +41 41444851189

0 notes

bdigit24 · 4 days ago

Text

How To Start A Lucrative AI Agents Business In Nigeria and Africa: The Complete Guide

Artificial Intelligence (AI) is rapidly transforming industries across the globe, and one of its most exciting and scalable applications is the use of AI agents. These intelligent digital systems are now automating tasks, managing customer service, driving sales, making decisions, and even solving complex business problems—all without human intervention. With the world’s growing reliance on…

0 notes

softwaredevelopusa · 4 days ago

Text

Voice & Sentiment Analysis in Customer Feedback Platforms

The digital age has fundamentally reshaped the relationship between businesses and their customers. With every click, comment, call, and review, customers are generating an unprecedented volume of feedback. This rich tapestry of information, often unstructured and voluminous, holds the key to understanding customer satisfaction, identifying pain points, and driving product and service improvements. However, manually sifting through thousands, or even millions, of interactions is an impossible task. This is where the power of Voice and Sentiment Analysis in Customer Feedback Platforms becomes transformative.

No longer limited to simple surveys, modern Customer Feedback platforms leverage advanced Artificial Intelligence (AI) and Machine Learning (ML) to listen, comprehend, and quantify the emotions and intent behind what customers say and write. By analyzing both the content of spoken and written words, as well as the nuances of tone and behavior, these platforms provide businesses with unparalleled, real-time insights into the true "Voice of the Customer" (VoC). This capability allows organizations to move from reactive problem-solving to proactive customer engagement, fostering deeper loyalty and competitive advantage.

The Evolution of Customer Feedback Analysis

Traditional methods of collecting and analyzing Customer Feedback have inherent limitations:

Surveys (NPS, CSAT, CES): While structured and quantifiable, surveys often suffer from low response rates, selection bias, and the inability to capture the full context or underlying emotion. They provide "what" but rarely "why."

Manual Review of Text Feedback: Analysts manually read through emails, chat logs, and written reviews. This is time-consuming, prone to human bias, and not scalable for large volumes of data.

Ad-hoc Listening to Call Recordings: Listening to a small sample of calls offers anecdotal insights but lacks comprehensive, objective analysis across all interactions.

Voice and Sentiment Analysis, powered by Natural Language Processing (NLP) and Machine Learning, represent a paradigm shift. They enable automated, scalable, and objective analysis of unstructured Customer Feedback, allowing businesses to extract richer, deeper insights.

Demystifying Voice Analysis in Customer Feedback Platforms

Voice analysis, often referred to as Speech Analytics, goes beyond mere transcription to understand the nuances of spoken communication. It's particularly crucial for contact centers and any business that interacts with customers over the phone.

How Voice Analysis Works:

Audio Capture & Transcription (Speech-to-Text):

The first step involves capturing audio from customer calls, voicemails, or recorded interactions.

Advanced Automatic Speech Recognition (ASR) technology, often powered by deep learning, then transcribes these spoken words into text. Modern ASR models are highly accurate, even handling different accents, dialects, and speaking speeds.

Speaker Diarization:

Identifies and separates different speakers in a conversation (e.g., customer vs. agent). This allows for separate analysis of each participant's speech and sentiment.

Acoustic Feature Extraction:

Beyond words, voice analysis extracts acoustic features that convey emotion and meaning. These include:

Pitch: The perceived highness or lowness of a voice.

Volume/Loudness: Indicates intensity or stress.

Speaking Rate: Speed of speech, which can indicate urgency or frustration.

Vocal Energy: Reflects excitement or fatigue.

Pauses and Fillers: (e.g., "um," "uh") can signal hesitation or discomfort.

Tone of Voice: Overall emotional quality.

Language and Contextual Analysis:

The transcribed text, combined with acoustic features, is then subjected to NLP techniques to understand the linguistic content.

This includes identifying keywords, phrases, topics, and intents.

For example, identifying keywords like "billing issue," "product defect," or "account setup" to categorize the reason for the call.

Demystifying Sentiment Analysis in Customer Feedback Platforms

Sentiment analysis, also known as Opinion Mining, is the computational process of determining the emotional tone behind a piece of text or speech – whether it's positive, negative, or neutral. When combined with voice analysis, it paints a truly comprehensive picture.

How Sentiment Analysis Works:

Text Pre-processing:

The transcribed text (from voice calls, or direct text input like emails, chats, reviews, social media) is cleaned and normalized. This involves removing punctuation, converting text to lowercase, stemming/lemmatization (reducing words to their root form), and removing stop words (common words like "the," "is").

Feature Extraction:

This involves identifying features from the text that indicate sentiment. Common approaches include:

Lexicon-Based: Using pre-defined dictionaries of words associated with positive or negative sentiment (e.g., "amazing" = positive, "frustrating" = negative). Each word might have a sentiment score.

Rule-Based: Applying linguistic rules to analyze sentence structure, presence of negations (e.g., "not good" vs. "good"), intensifiers (e.g., "very good" vs. "good"), or emojis.

Machine Learning Model Application:

ML algorithms (e.g., Support Vector Machines, Naive Bayes, Recurrent Neural Networks, Transformer models) are trained on vast datasets of human-labeled text to learn patterns between words, phrases, and their associated sentiment.

Fine-Grained Sentiment: Beyond just positive/negative/neutral, advanced models can identify nuanced sentiments like "very positive," "slightly negative," or even specific emotions (joy, anger, sadness, surprise, frustration).

Aspect-Based Sentiment: This advanced technique identifies the sentiment towards specific aspects of a product or service. For example, in "The camera is excellent, but the battery life is terrible," the sentiment is positive towards "camera" and negative towards "battery life."

Emotion Detection (Beyond Sentiment):

Some advanced platforms go beyond general sentiment to detect specific human emotions from both textual and vocal cues. This often involves more sophisticated deep learning models trained on highly annotated datasets.

Behavioral Signals Integration:

Modern sentiment analysis often integrates behavioral data from digital interactions (e.g., website clicks, scroll patterns, time spent on pages, form abandonment, rage clicks) to provide a more complete understanding of customer frustration or engagement. A customer might not explicitly say "I'm frustrated," but repeated clicks on an error message or rapid scrolling could indicate negative sentiment.

Benefits of Voice & Sentiment Analysis in Customer Feedback Platforms

Implementing these advanced analytics capabilities offers a multitude of benefits for businesses:

Real-time Problem Detection and Resolution:

Monitor live calls and chats to detect rising frustration or anger, allowing agents or supervisors to intervene immediately. This can prevent customer churn before it escalates. Real-time sentiment analysis can improve first-call resolution rates by up to 20% (Gartner, estimated).

Quickly identify emerging issues with products or services as customers discuss them, enabling proactive fixes.

Enhanced Customer Experience (CX) and Personalization:

Agents can adapt their tone and approach based on real-time sentiment cues from the customer, leading to more empathetic and effective interactions.

Personalize follow-up actions: A highly frustrated customer might receive a call from a manager, while a highly satisfied one might be prompted for a review.

Customer satisfaction (CSAT) can increase by 15-20% when voice and sentiment analysis are effectively utilized to improve support interactions.

Deeper Customer Insights and Root Cause Analysis:

Uncover the underlying "why" behind customer satisfaction or dissatisfaction, going beyond what surveys reveal.

Automatically identify recurring pain points, common reasons for complaints, and trending topics across thousands of interactions. This helps product development, marketing, and operations teams prioritize improvements.

For example, if sentiment analysis reveals consistent negativity around "billing errors" or "delivery times," the business knows exactly where to focus its efforts.

Improved Agent Performance and Coaching:

Automatically identify calls where agents struggled with unhappy customers or where positive sentiment was successfully cultivated.

Provide targeted coaching based on specific emotional cues or conversation patterns (e.g., "agent needs training on handling frustrated customers," "agent excels at empathy").

Automated Quality Assurance (QA): Instead of manually reviewing a small sample of calls, AI can analyze 100% of interactions, offering objective performance insights and reducing manual QA efforts by 50-70%.

Proactive Churn Prevention:

By continuously monitoring sentiment across all touchpoints, businesses can identify customers at risk of churning early (e.g., sustained negative sentiment, repeated complaints about key features) and initiate proactive retention strategies.

Early churn detection can reduce customer attrition by 10-15% (industry average for effective predictive analytics).

Optimized Marketing and Product Development:

Uncover the emotional language customers use when they are excited or frustrated about specific product features or marketing campaigns.

Inform product roadmaps by understanding what features are highly valued and what areas need improvement based on aggregated sentiment.

Tailor marketing messages to resonate with customer emotions by identifying keywords and themes that evoke positive sentiment.

Sales Optimization:

In sales calls, sentiment analysis can help identify buyer hesitation, interest, or urgency in real-time, allowing sales reps to adjust their pitch or close more effectively.

Post-sale, analyze sentiment to identify upsell or cross-sell opportunities with highly satisfied customers.

Enhanced Brand Reputation:

Monitor public sentiment on social media, review sites, and forums to quickly address negative feedback before it escalates into a crisis.

Identify brand advocates and leverage positive sentiment for testimonials and marketing.

Challenges in Implementing Voice & Sentiment Analysis

Despite the immense benefits, implementing robust voice and sentiment analysis in Customer Feedback platforms comes with its own set of challenges:

Accuracy and Nuance:

Sarcasm and Irony: A major hurdle for AI. "Oh, great service!" can mean the opposite. AI struggles with contextual cues that humans easily pick up.

Context Dependency: The meaning and sentiment of words can change drastically based on context. "Sick" can mean ill or excellent depending on the phrase.

Domain Specificity: A general sentiment model might misinterpret industry-specific jargon or slang. Custom models often need training on domain-specific data.

Subjectivity: Distinguishing objective statements from subjective opinions can be difficult.

Data Quality and Volume:

Noisy Audio: Background noise, poor microphone quality, or overlapping speech can significantly reduce ASR accuracy, impacting subsequent sentiment analysis.

Volume and Storage: Capturing and processing vast amounts of audio and text data requires significant storage and computational resources.

Data Imbalance: In some cases, genuine emotional expressions might be rarer than neutral conversations, creating imbalanced datasets for training.

Language and Cultural Differences:

Multilingual Support: Building and maintaining accurate models for multiple languages is complex due to different linguistic structures, idioms, and emotional expressions.

Cultural Nuances: What is considered positive or negative sentiment can vary across cultures.

Privacy and Ethical Concerns:

Consent: Ensuring explicit consent for recording and analyzing customer voice data is crucial, especially under regulations like GDPR.

Data Security: Protecting sensitive customer conversations and personal data is paramount.

Bias in Algorithms: If training data is biased, the sentiment analysis model might inadvertently perpetuate stereotypes or misinterpret emotions from certain demographic groups.

Integration Complexity:

Integrating voice and sentiment analysis platforms with existing CRM systems, contact center software, and other business intelligence tools can be technically challenging.

Actionability Gap:

Generating insights is one thing; acting on them effectively is another. Organizations need robust workflows to translate sentiment insights into actionable improvements.

Ensuring that insights reach the right teams (product, marketing, support) in a timely and understandable format.

Future of Voice & Sentiment Analysis in Customer Feedback Platforms

The future of voice and sentiment analysis is characterized by increasing sophistication, deeper integration, and greater personalization:

Multimodal Sentiment Analysis: Combining insights from voice (tone, pitch), text (words, phrases), and visual cues (facial expressions in video calls) for a truly holistic understanding of emotion.

Generative AI for Personalized Responses: Beyond just identifying sentiment, AI will increasingly assist agents in crafting empathetic and highly personalized responses in real-time, even suggesting next best actions. 63% of service professionals believe generative AI is their ticket to faster, smarter support (Forbes, 2024).

Predictive Customer Behavior: More advanced models will move beyond current sentiment to predict future customer behavior, such as churn risk, likelihood to purchase, or propensity to escalate.

Hyper-Personalized Self-Service: Chatbots and virtual assistants will leverage voice and sentiment analysis to provide more emotionally intelligent and adaptive self-service options, guiding customers more effectively based on their emotional state.

Emotional AI and Empathy-as-a-Service: The ability of AI to understand and even simulate empathy will lead to more nuanced and human-like interactions in automated systems.

Ethical AI by Design: Greater emphasis on bias detection and mitigation, ensuring fairness, privacy, and transparency in sentiment analysis models. Regulations like the EU AI Act will drive this.

Deeper Integration with CRM and ERP: Seamless flow of sentiment insights directly into customer profiles within CRM systems, providing a 360-degree view of the customer and enabling enterprise-wide action.

Proactive Issue Resolution: Systems will automatically detect early signs of frustration and trigger interventions (e.g., a proactive call from an agent, a personalized offer) before a customer explicitly complains.

Leading Customer Feedback Platforms with Voice & Sentiment Analysis Capabilities

The market for Customer Feedback platforms leveraging voice and sentiment analysis is rapidly expanding. Key players and solution types include:

Unified CX Platforms:

Qualtrics XM: Offers comprehensive experience management, including Text iQ for advanced sentiment analysis across various feedback channels.

Medallia: A leading experience management platform that aggregates feedback from numerous touchpoints, providing deep insights with advanced sentiment analysis.

InMoment (Lexalytics): Utilizes AI to analyze text from multiple sources, translating unstructured feedback into actionable insights.

Speech Analytics & Contact Center AI:

CallMiner Eureka: Specializes in advanced speech analytics, providing real-time call monitoring, sentiment, and topic discovery within voice interactions.

NICE CXone: Offers comprehensive contact center solutions with integrated AI for speech and text analytics, sentiment analysis, and agent performance management.

Verint: Provides Voice of the Customer software with capabilities including speech and text analytics, and sentiment analysis for omni-channel interactions.

Calabrio ONE: A unified workforce engagement and customer experience intelligence platform with robust VoC analytics, including speech and text analytics.

Observe.AI: Focuses on contact center AI, providing real-time agent assist, sentiment analysis, and automated quality assurance from voice interactions.

SentiSum: AI-powered customer experience analytics platform with distinct offerings in support ticket, customer feedback, and customer review monitoring, including voice call sentiment analysis.

Text Analysis & Social Listening Tools:

MonkeyLearn: An AI tool specifically for analyzing customer sentiment from social media texts and other qualitative data.

Brandwatch: A comprehensive social listening and analytics platform that helps businesses understand online conversations and brand sentiment.

Sprout Social: A social media management software with AI that monitors user sentiment across various social platforms.

Zonka Feedback: Offers AI-powered Sentiment Analysis to gauge mood and emotions from feedback.

HubSpot Service Hub: Integrates communication tools with Customer Feedback analytics, including sentiment analysis.

Conclusion

In the hyper-competitive market of today, understanding and responding to the Customer Feedback is paramount for survival and growth. Voice and Sentiment Analysis in Customer Feedback Platforms are no longer just an advantage; they are becoming a necessity. By automatically transforming raw, unstructured interactions into quantifiable insights about customer emotions, intent, and pain points, these technologies empower businesses to:

Act quickly to resolve issues,

Personalize customer journeys,

Optimize product and service offerings,

Enhance agent performance, and

Ultimately build stronger, more loyal customer relationships.

While challenges related to accuracy, data quality, and ethical considerations persist, ongoing advancements in AI and ML are continuously refining these capabilities. Embracing Voice and Sentiment Analysis is a strategic investment that enables businesses to truly listen to, understand, and engage with the authentic Voice of the Customer, driving superior experiences and sustained success.

0 notes