#AI in Business: Fundamentals to Applications

[arnavswami905]

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[mariacallous]

The European Union today agreed on the details of the AI Act, a far-reaching set of rules for the people building and using artificial intelligence. It’s a milestone law that, lawmakers hope, will create a blueprint for the rest of the world.

After months of debate about how to regulate companies like OpenAI, lawmakers from the EU’s three branches of government—the Parliament, Council, and Commission—spent more than 36 hours in total thrashing out the new legislation between Wednesday afternoon and Friday evening. Lawmakers were under pressure to strike a deal before the EU parliament election campaign starts in the new year.

“The EU AI Act is a global first,” said European Commission president Ursula von der Leyen on X. “[It is] a unique legal framework for the development of AI you can trust. And for the safety and fundamental rights of people and businesses.”

The law itself is not a world-first; China’s new rules for generative AI went into effect in August. But the EU AI Act is the most sweeping rulebook of its kind for the technology. It includes bans on biometric systems that identify people using sensitive characteristics such as sexual orientation and race, and the indiscriminate scraping of faces from the internet. Lawmakers also agreed that law enforcement should be able to use biometric identification systems in public spaces for certain crimes.

New transparency requirements for all general purpose AI models, like OpenAI's GPT-4, which powers ChatGPT, and stronger rules for “very powerful” models were also included. “The AI Act sets rules for large, powerful AI models, ensuring they do not present systemic risks to the Union,” says Dragos Tudorache, member of the European Parliament and one of two co-rapporteurs leading the negotiations.

Companies that don’t comply with the rules can be fined up to 7 percent of their global turnover. The bans on prohibited AI will take effect in six months, the transparency requirements in 12 months, and the full set of rules in around two years.

Measures designed to make it easier to protect copyright holders from generative AI and require general purpose AI systems to be more transparent about their energy use were also included.

“Europe has positioned itself as a pioneer, understanding the importance of its role as a global standard setter,” said European Commissioner Thierry Breton in a press conference on Friday night.

Over the two years lawmakers have been negotiating the rules agreed today, AI technology and the leading concerns about it have dramatically changed. When the AI Act was conceived in April 2021, policymakers were worried about opaque algorithms deciding who would get a job, be granted refugee status or receive social benefits. By 2022, there were examples that AI was actively harming people. In a Dutch scandal, decisions made by algorithms were linked to families being forcibly separated from their children, while students studying remotely alleged that AI systems discriminated against them based on the color of their skin.

Then, in November 2022, OpenAI released ChatGPT, dramatically shifting the debate. The leap in AI’s flexibility and popularity triggered alarm in some AI experts, who drew hyperbolic comparisons between AI and nuclear weapons.

That discussion manifested in the AI Act negotiations in Brussels in the form of a debate about whether makers of so-called foundation models such as the one behind ChatGPT, like OpenAI and Google, should be considered as the root of potential problems and regulated accordingly—or whether new rules should instead focus on companies using those foundational models to build new AI-powered applications, such as chatbots or image generators.

Representatives of Europe’s generative AI industry expressed caution about regulating foundation models, saying it could hamper innovation among the bloc’s AI startups. “We cannot regulate an engine devoid of usage,” Arthur Mensch, CEO of French AI company Mistral, said last month. “We don’t regulate the C [programming] language because one can use it to develop malware. Instead, we ban malware.” Mistral’s foundation model 7B would be exempt under the rules agreed today because the company is still in the research and development phase, Carme Artigas, Spain's Secretary of State for Digitalization and Artificial Intelligence, said in the press conference.

The major point of disagreement during the final discussions that ran late into the night twice this week was whether law enforcement should be allowed to use facial recognition or other types of biometrics to identify people either in real time or retrospectively. “Both destroy anonymity in public spaces,” says Daniel Leufer, a senior policy analyst at digital rights group Access Now. Real-time biometric identification can identify a person standing in a train station right now using live security camera feeds, he explains, while “post” or retrospective biometric identification can figure out that the same person also visited the train station, a bank, and a supermarket yesterday, using previously banked images or video.

Leufer said he was disappointed by the “loopholes” for law enforcement that appeared to have been built into the version of the act finalized today.

European regulators’ slow response to the emergence of social media era loomed over discussions. Almost 20 years elapsed between Facebook's launch and the passage of the Digital Services Act—the EU rulebook designed to protect human rights online—taking effect this year. In that time, the bloc was forced to deal with the problems created by US platforms, while being unable to foster their smaller European challengers. “Maybe we could have prevented [the problems] better by earlier regulation,” Brando Benifei, one of two lead negotiators for the European Parliament, told WIRED in July. AI technology is moving fast. But it will still be many years until it’s possible to say whether the AI Act is more successful in containing the downsides of Silicon Valley’s latest export.

[iww-gnv]

The New York Film and Television Union Coalition is praising a pair of identical bills pending in New York State that would “prohibit applicants to the Empire State film production credit from using artificial intelligence that would displace any natural person in their productions.” The coalition is made up of SAG-AGTRA, the WGA East, the Directors Guild of America, the Cinematographers Guild (IATSE Local 600), the Editors Guild (IATSE Local 700), United Scenic Artists (IATSE Local 829), IATSE Local 52, and Teamsters Local 817. The use of artificial intelligence in the production of film and TV shows is a key strike issue for both the Writers Guild and SAG-AFTRA, which have been on strike since May 2 and July 14, respectively. The DGA’s new contract, which was ratified in June, contains guardrails on its use, and IATSE, which will begin contract negotiations next year, has said that artificial intelligence “threatens to fundamentally alter employers’ business models and disrupt IATSE members’ livelihoods.” 

[Read the rest]

[dertaglichedan]

Cloud and AI are ‘fundamentally changing’ ability to forecast weather, NOAA chief says

LAS VEGAS — National Oceanic and Atmospheric Administration head Rick Spinrad said this week that even as hurricanes, wildfires and other weather events worsen, the use of innovative tech like cloud computing and AI is improving the agency’s ability to predict and forecast them.

“This is fundamentally changing our ability to do probably the most important thing in our business, and that's predict, project and forecast,” said Spinrad, speaking at AWS’ re:Invent 2024 conference in Las Vegas.

Spinrad said NOAA, which houses the National Weather Service and owns or operates 18 weather satellites, archives about 230 terabytes of environmental data per month from various observational platforms, ranging from the sea, to ships and even space.

That data is then hosted by commercial cloud service providers and made available to the public and nascent climate industry through NOAA’s Open Data Dissemination program, or NODD. AWS, Spinrad said, now hosts 32 petabytes of environmental information, or about “half of [NOAA’s] holdings.”

“Our ability to collect these data, put them on NODD, store them in a system that allows AI applications, is really critical,” Spinrad said. “And it's not just for our use at NOAA. We are supporting a burgeoning climate industrial effort, a commercial weather enterprise. We are now collecting enough environmental information — enough environmental intelligence — to make a fundamental difference.”

According to Spinrad, the difference includes improved access to critical weather data for incident meteorologists in the field. Using another tool from Amazon, AppStream 2.0, incident meteorologists dispatched to the scenes of floods, droughts, storms and wildfires can “have the same kind of access to our advanced weather information and interactive processing system that they would back at the home office.”

Spinrad said forecasters have been able to make a “seven year advancement in forecast capability in a matter of months using AI, using the training data [NOAA] now has access to.” Such advancements would not be possible without the ability to assimilate new data types, he added, noting that autonomous systems, satellites and surface-based robots all provide data that can go into forecasts.

[pranjj]

Udaan by InAmigos Foundation:  Elevating Women, Empowering Futures

In the rapidly evolving socio-economic landscape of India, millions of women remain underserved by mainstream development efforts—not due to a lack of talent, but a lack of access. In response, Project Udaan, a flagship initiative by the InAmigos Foundation, emerges not merely as a program, but as a model of scalable women's empowerment.

Udaan—meaning “flight” in Hindi—represents the aspirations of rural and semi-urban women striving to break free from intergenerational limitations. By engineering opportunity and integrating sustainable socio-technical models, Udaan transforms potential into productivity and promise into progress.

Mission: Creating the Blueprint for Women’s Self-Reliance

At its core, Project Udaan seeks to:

Empower women with industry-aligned, income-generating skills

Foster micro-entrepreneurship rooted in local demand and resources

Facilitate financial and digital inclusion

Strengthen leadership, health, and rights-based awareness

Embed resilience through holistic community engagement

Each intervention is data-informed, impact-monitored, and custom-built for long-term sustainability—a hallmark of InAmigos Foundation’s field-tested grassroots methodology.

A Multi-Layered Model for Empowerment

Project Udaan is built upon a structured architecture that integrates training, enterprise, and technology to ensure sustainable outcomes. This model moves beyond skill development into livelihood generation and measurable socio-economic change.

1. Skill Development Infrastructure

The first layer of Udaan is a robust skill development framework that delivers localized, employment-focused education. Training modules are modular, scalable, and aligned with the socio-economic profiles of the target communities.

Core domains include:

Digital Literacy: Basic computing, mobile internet use, app navigation, and digital payment systems

Tailoring and Textile Production: Pattern making, machine stitching, finishing techniques, and indigenous craft techniques

Food Processing and Packaging: Pickle-making, spice grinding, home-based snack units, sustainable packaging

Salon and Beauty Skills: Basic grooming, hygiene standards, customer interaction, and hygiene protocols

Financial Literacy and Budgeting: Saving schemes, credit access, banking interfaces, micro-investments

Communication and Self-Presentation: Workplace confidence, customer handling, local language fluency

2. Microenterprise Enablement and Livelihood Incubation

To ensure that learning transitions into economic self-reliance, Udaan incorporates a post-training enterprise enablement process. It identifies local market demand and builds backward linkages to equip women to launch sustainable businesses.

The support ecosystem includes:

Access to seed capital via self-help group (SHG) networks, microfinance partners, and NGO grants

Distribution of startup kits such as sewing machines, kitchen equipment, or salon tools

Digital onboarding support for online marketplaces such as Amazon Saheli, Flipkart Samarth, and Meesho

Offline retail support through tie-ups with local haats, trade exhibitions, and cooperative stores

Licensing and certification where applicable for food safety or textile quality standards

3. Tech-Driven Monitoring and Impact Tracking

Transparency and precision are fundamental to Udaan’s growth. InAmigos Foundation employs its in-house Tech4Change platform to manage operations, monitor performance, and scale the intervention scientifically.

The platform allows:

Real-time monitoring of attendance, skill mastery, and certification via QR codes and mobile tracking

Impact evaluation using household income change, asset ownership, and healthcare uptake metrics

GIS-based mapping of intervention zones and visualization of under-reached areas

Predictive modeling through AI to identify at-risk participants and suggest personalized intervention strategies

 

Human-Centered, Community-Rooted

Empowerment is not merely a process of economic inclusion—it is a cultural and psychological shift. Project Udaan incorporates gender-sensitive design and community-first outreach to create lasting change.

Key interventions include:

Strengthening of SHG structures and women-led federations to serve as peer mentors

Family sensitization programs targeting male allies—fathers, husbands, brothers—to reduce resistance and build trust

Legal and rights-based awareness campaigns focused on menstrual hygiene, reproductive health, domestic violence laws, and maternal care

Measured Impact and Proven Scalability

Project Udaan has consistently delivered quantifiable outcomes at the grassroots level. As of the latest cycle:

Over 900 women have completed intensive training programs across 60 villages and 4 districts

Nearly 70 percent of participating women reported an average income increase of 30 to 60 percent within 9 months of program completion

420+ micro-enterprises have been launched, 180 of which are now self-sustaining and generating employment for others

More than 5,000 indirect beneficiaries—including children, elderly dependents, and second-generation SHG members—have experienced improved access to nutrition, education, and mobility

Over 20 institutional partnerships and corporate CSR collaborations have supported infrastructure, curriculum design, and digital enablement.

Partnership Opportunities: Driving Collective Impact

The InAmigos Foundation invites corporations, philanthropic institutions, and ecosystem enablers to co-create impact through structured partnerships.

Opportunities include:

Funding the establishment of skill hubs in high-need regions

Supporting enterprise starter kits and training batches through CSR allocations

Mentoring women entrepreneurs via employee volunteering and capacity-building workshops

Co-hosting exhibitions, market linkages, and rural entrepreneurship fairs

Enabling long-term research and impact analytics for policy influence

These partnerships offer direct ESG alignment, brand elevation, and access to inclusive value chains while contributing to a model that demonstrably works.

What Makes Project Udaan Unique?

Unlike one-size-fits-all skilling programs, Project Udaan is rooted in real-world constraints and community aspirations. It succeeds because it combines:

Skill training aligned with current and emerging market demand

Income-first design that integrates microenterprise creation and financial access

Localized community ownership that ensures sustainability and adoption

Tech-enabled operations that ensure transparency and iterative learning

Holistic empowerment encompassing economic, social, and psychological dimensions

By balancing professional training with emotional transformation and economic opportunity, Udaan represents a new blueprint for inclusive growth.

 From Promise to Power

Project Udaan, driven by the InAmigos Foundation, proves that when equipped with tools, trust, and training, rural and semi-urban women are capable of becoming not just contributors, but catalysts for socio-economic renewal.

They don’t merely escape poverty—they design their own systems of progress. They don’t just participate—they lead.

Each sewing machine, digital training module, or microloan is not a transaction—it is a declaration of possibility.

This is not charity. This is infrastructure. This is equity, by design.

Udaan is not just a program. It is a platform for a new India.

For partnership inquiries, CSR collaborations, and donation pathways, contact: www.inamigosfoundation.org/Udaan Email: partnerships@inamigosfoundation.org

[govindhtech]

Pegasus 1.2: High-Performance Video Language Model

Pegasus 1.2 revolutionises long-form video AI with high accuracy and low latency. Scalable video querying is supported by this commercial tool.

TwelveLabs and Amazon Web Services (AWS) announced that Amazon Bedrock will soon provide Marengo and Pegasus, TwelveLabs' cutting-edge multimodal foundation models. Amazon Bedrock, a managed service, lets developers access top AI models from leading organisations via a single API. With seamless access to TwelveLabs' comprehensive video comprehension capabilities, developers and companies can revolutionise how they search for, assess, and derive insights from video content using AWS's security, privacy, and performance. TwelveLabs models were initially offered by AWS.

Introducing Pegasus 1.2

Unlike many academic contexts, real-world video applications face two challenges:

Real-world videos might be seconds or hours lengthy.

Proper temporal understanding is needed.

TwelveLabs is announcing Pegasus 1.2, a substantial industry-grade video language model upgrade, to meet commercial demands. Pegasus 1.2 interprets long films at cutting-edge levels. With low latency, low cost, and best-in-class accuracy, model can handle hour-long videos. Their embedded storage ingeniously caches movies, making it faster and cheaper to query the same film repeatedly.

Pegasus 1.2 is a cutting-edge technology that delivers corporate value through its intelligent, focused system architecture and excels in production-grade video processing pipelines.

Superior video language model for extended videos

Business requires handling long films, yet processing time and time-to-value are important concerns. As input films increase longer, a standard video processing/inference system cannot handle orders of magnitude more frames, making it unsuitable for general adoption and commercial use. A commercial system must also answer input prompts and enquiries accurately across larger time periods.

Latency

To evaluate Pegasus 1.2's speed, it compares time-to-first-token (TTFT) for 3–60-minute videos utilising frontier model APIs GPT-4o and Gemini 1.5 Pro. Pegasus 1.2 consistently displays time-to-first-token latency for films up to 15 minutes and responds faster to lengthier material because to its video-focused model design and optimised inference engine.

Performance

Pegasus 1.2 is compared to frontier model APIs using VideoMME-Long, a subset of Video-MME that contains films longer than 30 minutes. Pegasus 1.2 excels above all flagship APIs, displaying cutting-edge performance.

Pricing

Cost Pegasus 1.2 provides best-in-class commercial video processing at low cost. TwelveLabs focusses on long videos and accurate temporal information rather than everything. Its highly optimised system performs well at a competitive price with a focused approach.

Better still, system can generate many video-to-text without costing much. Pegasus 1.2 produces rich video embeddings from indexed movies and saves them in the database for future API queries, allowing clients to build continually at little cost. Google Gemini 1.5 Pro's cache cost is $4.5 per hour of storage, or 1 million tokens, which is around the token count for an hour of video. However, integrated storage costs $0.09 per video hour per month, x36,000 less. Concept benefits customers with large video archives that need to understand everything cheaply.

Model Overview & Limitations

Architecture

Pegasus 1.2's encoder-decoder architecture for video understanding includes a video encoder, tokeniser, and big language model. Though efficient, its design allows for full textual and visual data analysis.

These pieces provide a cohesive system that can understand long-term contextual information and fine-grained specifics. It architecture illustrates that tiny models may interpret video by making careful design decisions and solving fundamental multimodal processing difficulties creatively.

Restrictions

Safety and bias

Pegasus 1.2 contains safety protections, but like any AI model, it might produce objectionable or hazardous material without enough oversight and control. Video foundation model safety and ethics are being studied. It will provide a complete assessment and ethics report after more testing and input.

Hallucinations

Occasionally, Pegasus 1.2 may produce incorrect findings. Despite advances since Pegasus 1.1 to reduce hallucinations, users should be aware of this constraint, especially for precise and factual tasks.

[samarthdas]

Exploring DeepSeek and the Best AI Certifications to Boost Your Career

Understanding DeepSeek: A Rising AI Powerhouse

DeepSeek is an emerging player in the artificial intelligence (AI) landscape, specializing in large language models (LLMs) and cutting-edge AI research. As a significant competitor to OpenAI, Google DeepMind, and Anthropic, DeepSeek is pushing the boundaries of AI by developing powerful models tailored for natural language processing, generative AI, and real-world business applications.

With the AI revolution reshaping industries, professionals and students alike must stay ahead by acquiring recognized certifications that validate their skills and knowledge in AI, machine learning, and data science.

Why AI Certifications Matter

AI certifications offer several advantages, such as:

Enhanced Career Opportunities: Certifications validate your expertise and make you more attractive to employers.

Skill Development: Structured courses ensure you gain hands-on experience with AI tools and frameworks.

Higher Salary Potential: AI professionals with recognized certifications often command higher salaries than non-certified peers.

Networking Opportunities: Many AI certification programs connect you with industry experts and like-minded professionals.

Top AI Certifications to Consider

If you are looking to break into AI or upskill, consider the following AI certifications:

1. AICerts – AI Certification Authority

AICerts is a recognized certification body specializing in AI, machine learning, and data science.

It offers industry-recognized credentials that validate your AI proficiency.

Suitable for both beginners and advanced professionals.

2. Google Professional Machine Learning Engineer

Offered by Google Cloud, this certification demonstrates expertise in designing, building, and productionizing machine learning models.

Best for those who work with TensorFlow and Google Cloud AI tools.

3. IBM AI Engineering Professional Certificate

Covers deep learning, machine learning, and AI concepts.

Hands-on projects with TensorFlow, PyTorch, and SciKit-Learn.

4. Microsoft Certified: Azure AI Engineer Associate

Designed for professionals using Azure AI services to develop AI solutions.

Covers cognitive services, machine learning models, and NLP applications.

5. DeepLearning.AI TensorFlow Developer Certificate

Best for those looking to specialize in TensorFlow-based AI development.

Ideal for deep learning practitioners.

6. AWS Certified Machine Learning – Specialty

Focuses on AI and ML applications in AWS environments.

Includes model tuning, data engineering, and deep learning concepts.

7. MIT Professional Certificate in Machine Learning & Artificial Intelligence

A rigorous program by MIT covering AI fundamentals, neural networks, and deep learning.

Ideal for professionals aiming for academic and research-based AI careers.

Choosing the Right AI Certification

Selecting the right certification depends on your career goals, experience level, and preferred AI ecosystem (Google Cloud, AWS, or Azure). If you are a beginner, starting with AICerts, IBM, or DeepLearning.AI is recommended. For professionals looking for specialization, cloud-based AI certifications like Google, AWS, or Microsoft are ideal.

With AI shaping the future, staying certified and skilled will give you a competitive edge in the job market. Invest in your learning today and take your AI career to the next leve

[besidekick]

Earlier this morning I saw a post on my "for you" feed from someone presenting in-class written exams as the obvious, easy answer to student cheating via generative AI. I wrote this whole response to add on a reblog but I'm not actually eager to get into a potential argument with a stranger, so instead I'm posting it here and even if it gets no notes, that's fine.

This is not meant to be any kind of personal attack on people who use timed, written exams, but instead to engage in the time-honored practice of academic debate. These are just my two cents.

Some of my colleagues have turned to the same solution suggested—in-class blue book exams to combat AI and other fears of plagiarism. I personally really, really do not want that to become the norm for several reasons, despite the fact that I teach in a humanities field where the written exam has continued to be popular even after its drop-off in other areas of study (which has been happening since at least the 70s).

To begin with, timed, in-class exams present issues for accessibility. If you teach with exams, you will have had the experience of having to arrange alternate proctored exam times for students who need reasonable accommodations or who were ill the day of the test. The good news is that universities typically have systems in place to assist with this, but clearly it's not a one-size-fits all easy solution.

At least in the humanities, where these exams are most common, an in-class written exam does not replicate any real-world task students might reasonably expect to encounter. What pedagogical goal does the exam serve? Unless writing under pressure is one of your learning objectives, students (and your course in general) would benefit more from a different approach to evaluation.

High-stakes, in-class exams have been shown over and over again to not be a particularly effective measure of student learning and inherently prioritize memorization over actual application of learned skills and knowledge. This is not a new critique. From "A review of the benefits and drawbacks of high-stakes final examinations in higher education" (2023):

In the scholarly literature on assessment in higher education, questions relating to the pedagogical value of final examinations surface repeatedly. For example, John Biggs (2001, p. 234) argues that “invigilated examinations are hard to justify educationally” citing concerns about plagiarism and contract cheating as the leading “distorted priority” for their ongoing use. Scholars such as Gibbs (1992) and Ramsden (1992) warn against the reliability of examinations as a measurement of student learning, noting that questions assessing the recall of facts can often be answered without an understanding of the fundamental principles of the topic or a more complex understanding of the ways in which concepts are integrated in real-world scenarios. Such critiques are by no means new. Indeed, examinations have received criticism since their inception in Imperial China when they were widely criticised for their emphasis on rote memorisation, testing of skills rather than knowledge, the prevalence of cheating, and for cases of mental disorders that were anecdotally attributed to failing the high-pressured examinations (Kellaghan & Greaney, 2019). Similar criticisms were made of the written examinations introduced at Oxford and Cambridge in the nineteenth century which were perceived to dissuade originality through their focus on recall and to contribute to social stratification by benefiting the most privileged (Kellaghan & Greaney, 2019).

I don't want to go on too long. Whether you do in-class exams or don't is entirely your business, but I'd hate for everyone to start pretending they're a perfect solution to cheating (which students do on in-class tests too) or that this is the way we've always done it (not really true) and therefore is the best way forward.

[diya00000000]

Top Skills to Learn in 2024: Elevate Your Career with These In-Demand Abilities

In 2024, the job market continues to evolve rapidly, shaped by technological advancements and shifting workplace dynamics. To stay competitive, it’s essential to develop both soft skills and technical skills that employers value. This article explores the top skills to learn in 2024 and provides actionable tips on incorporating them into your job applications to boost your career prospects.

Soft Skills: A soft skill is one that is applicable to all occupations. They are generally more concerned in how you interact with others and manage the job. In other words, these are teamwork, work ethic, work style, or interpersonal skills. These abilities not only allow you to be more adaptable in your business, but they also benefit your personal life. Soft skills are very important to modern employers, and career coaching may help you learn how to improve your soft skills and discover areas where you can improve.

Communication

Effective communication abilities continue to be the most desired attribute by employers. Clear presentations, correspondence, and teamwork are ensured by effective communication, which encompasses both written and spoken abilities.

Why It’s Important: Clear communication fosters teamwork, reduces misunderstandings, and enhances productivity. How to Develop It: Join public speaking clubs like Toastmasters, practice writing concise emails, or take online courses on communication.

Analytical Thinking

People who can analyze data, approach problems logically, and come up with creative solutions are sought after by employers. In professions involving decision-making, analytical thinking is essential and enhances technical abilities.

Why It’s Important: Analytical thinkers can navigate complex challenges and offer data-driven insights. How to Develop It: Engage in activities like puzzles, logic games, or courses on critical thinking and problem-solving.

Project Management

With remote and hybrid work environments becoming the norm, project management skills are indispensable. These include planning, organizing, and overseeing projects to achieve goals efficiently.

Why It’s Important: Successful project managers ensure timely delivery, manage budgets, and lead teams effectively. How to Develop It: Earn certifications like PMP (Project Management Professional) or take online courses on project management tools like Trello and Asana.

Leadership

Leadership goes beyond managing a team — it’s about inspiring, motivating, and guiding others toward success. In 2024, inclusive and empathetic leadership is particularly valued.

Why It’s Important: Strong leaders foster a positive workplace culture and drive organizational growth. How to Develop It: Volunteer for leadership roles, mentor others, or study leadership styles through books or courses.

Adaptability

The pace of change in today’s world demands professionals who can adapt quickly to new technologies, roles, and environments. Adaptability is the key to thriving amid uncertainty.

Why It’s Important: It shows resilience and a willingness to embrace change, both critical traits in dynamic industries. How to Develop It: Push yourself out of your comfort zone by taking on new challenges or cross-functional roles. Technical Skills: The Backbone of Modern Careers

Generative AI Generative AI tools like ChatGPT, DALL·E, and Bard are revolutionizing industries. Professionals skilled in utilizing these tools for content creation, problem-solving, and data analysis are in high demand.

Why It’s Important: Generative AI enhances efficiency and creativity, making it a must-know for almost every sector. How to Develop It: Explore AI tools and complete online certifications in AI fundamentals and machine learning.

Data Analysis

Data analysis involves interpreting raw data to make informed decisions. From finance to marketing, data skills are essential for extracting actionable insights.

Why It’s Important: Companies increasingly rely on data to optimize operations and improve customer experiences. How to Develop It: Learn tools like Excel, SQL, Tableau, or Python for data analysis through platforms like Coursera or Udemy.

Software Development

The ability to design and develop software is critical for tech-heavy industries. With constant innovations, software developers are at the forefront of technological advancement.

Why It’s Important: Software drives automation, apps, and enterprise solutions that businesses depend on. How to Develop It: Start with beginner-friendly programming languages like Python or JavaScript, then build your portfolio by working on real-world projects.

UI/UX Design

UI/UX design ensures user-friendly and aesthetically pleasing digital experiences. Businesses are investing heavily in UX to retain customers and enhance brand loyalty.

Why It’s Important: Good design is the foundation of successful websites and apps. How to Develop It: Master tools like Figma, Adobe XD, and Sketch, and study UX principles through industry blogs and courses.

Web Development

Web development remains a cornerstone skill in the digital age. Whether it’s front-end, back-end, or full-stack development, expertise in creating robust websites is highly sought after.

Why It’s Important: Businesses need fast, secure, and responsive websites to stay competitive. How to Develop It: Learn coding languages like HTML, CSS, JavaScript, and frameworks such as React or Node.js. How to Incorporate These Skills When Applying for Jobs

Highlight Skills in Your Resume

Create a dedicated “Skills” section to list both technical and soft skills relevant to the job. Use metrics and examples in your experience section to showcase how these skills contributed to your success. Example: “Led a team of 10 to complete a software development project 15% ahead of schedule.”

Taior Your Cover Letter

Use your cover letter to explain how your skills align with the job description. Mention specific instances where you applied these skills to solve problems or achieve goals.

Provide Evidence During Interviews

Share anecdotes or STAR (Situation, Task, Action, Result) stories demonstrating your soft and technical skills. Example: “In my last role, I used data analysis to identify a trend that saved the company 20% in operational costs.”

Showcase Skills in Your Portfolio

For technical skills like web development or UI/UX design, create a digital portfolio to showcase your work. Include case studies, designs, or live projects to demonstrate your expertise.

Leverage LinkedIn

Keep your LinkedIn profile updated with your skills and certifications. Use LinkedIn endorsements and recommendations to validate your expertise.

Conclusion The top skills to learn in 2024 encompass a mix of soft skills like communication and leadership and technical skills like generative AI and data analysis. Mastering these skills will not only future-proof your career but also make you a standout candidate in any job application process.

Remember, learning doesn’t stop at acquiring new skills—showcasing them effectively is equally important. Start by setting goals, enrolling in courses, and applying these skills to real-world scenarios. With dedication, 2024 could be your year of unprecedented professional growth!

[jcmarchi]

Generative AI’s Role in Job Satisfaction

New Post has been published on https://thedigitalinsider.com/generative-ais-role-in-job-satisfaction/

Generative AI’s Role in Job Satisfaction

Generative AI (GenAI) is a pivotal technology that enhances work in a myriad of ways. From automating complex analysis to simulating scenarios that assist in decision-making, GenAI use cases are making a big impact across a broad swath of industries, including financial services, consultancies, information technology, legal, telecommunication and more.

Certainly, organizations recognize GenAI’s potential with the increasing adoption of AI within organizations. According to a PWC survey, 73% of U.S. companies have adopted AI in some areas of their business. Yet, discussion persists about GenAI’s role within the workplace, given fears over job displacement, bias, decision-making transparency and more. Despite this, GenAI has made AI technology much more accessible to employees within organizations, regardless of their specific roles.

In fact, a LexisNexis Future of Work survey showed that 72% of professionals anticipate a positive impact from GenAI, and only 4% see it as a threat to job security. GenAI can automate mundane tasks, allowing users to focus on more specialized, impactful and strategic tasks. This, in turn, can increase employee productivity and job satisfaction while ensuring human ambition and innovation walk hand in hand.

AI’s Productivity Boost

GenAI’s rapid rise marks a crucial shift in how organizations must operate and strategize to augment every role. GenAI applications are as diverse as they are impactful. It’s not just hype; GenAI is already poised to increase labor productivity by 0.1 to 0.6% annually through 2040.

GenAI has also created value across multiple sectors and industries. Significant business functions, including Sales, Marketing, Customer Operations and Technology have leveraged GenAI to increase productivity. In technology, for example, GenAI-based coding assistants are a massive help to software developers in suggesting code snippets, refactoring code, fixing bugs, understanding complex code, writing unit tests, documentation and creating complete end-to-end applications.

As employees experiment and explore with GenAI tools, their comfort level with the technology increases. Eighty-six percent of professionals ‘agree’ or ‘strongly agree’ with a willingness to embrace GenAI for both creative and professional work. Sixty-eight percent of employees plan to use GenAI tools for work purposes, while 69% are already using these tools to assist with daily tasks. The data makes it clear that organizations that adopt GenAI can boost productivity, and employees are willing to use it to accelerate efficiency.

Productivity Gains Are a Given, But Also AI Helps with Job Satisfaction

One of the most significant opportunities around GenAI lies in its power to help with job satisfaction. While professionals have fairly balanced expectations on how far adoption will go, 82% expect generative AI to take over a range of repetitive administrative tasks by automating routine tasks and data analysis, freeing them to focus on more strategic aspects of their work.

When asked how they perceive GenAI’s role in the work environment, more than two-thirds of professionals see it as a ‘helpful tool’ or ‘supportive co-worker.’ As a result, they recognize AI’s potential to enhance, not hinder, job performance and are embracing it with a positive mindset toward eliminating repetitive tasks and freeing up time for more rewarding, higher-value work.

Most professionals do not see generative AI as a detriment to job satisfaction, either. Over half (51%) say job satisfaction has improved significantly or moderately thanks to GenAI, while only 10% felt that it decreases job satisfaction. A fundamental rethink is necessary where and how organizations implement GenAI tools within the workplace.

Recommendations to Improve Engagement and Job Satisfaction

Organizations need to consider employee engagement throughout the adoption process of GenAI tools. Here are some recommendations to improve engagement and thereby increase job satisfaction:

Engage your employees to identify the use cases that are most impactful for a particular role or group. Pick tasks that are most time-consuming and tedious, such that solving them would free up time to focus on more critical items.

Identify the GenAI tools and large language models (LLMs) that are most effective for solving the identified use case. Take the time to experiment, test and validate the output. Ensure that you account for a diverse set of inputs for the use case and measure the output quality, including the hallucination rate, to help build trust within your employee base using the solution.

Provide training to your team. Take advantage of the vast information available on the web, with videos, code samples, tool vendor resources and tutorials on using the specific tool, LLM, associated prompts and guardrails. Create mentors and experts within the team to help coach the rest. Showcase examples of lessons learned and success stories to inspire team members who may not see the value.

Identify and measure KPIs. These could include adoption, productivity gains, costs saved or repurposed, employee satisfaction, quality improvement and other KPIs that may be specific to the team or business.

Gen AI isn’t just for technologists anymore; it’s making potent tools accessible to everyone. Most business professionals who once viewed these technologies with skepticism now accept and even welcome them. And it’s no secret why, given GenAI’s power to present organizations and employees alike with unprecedented opportunities toward the future of work.

[iww-gnv]

Saying that artificial intelligence “threatens to fundamentally alter employers’ business models and disrupt IATSE members’ livelihoods,” the union Wednesday unveiled its “Core Principles” for the application of artificial intelligence and machine learning technologies in the entertainment industry. The move follows the union’s creation of a Commission on Artificial Intelligence in May. “With AI, the stakes for IATSE members in all crafts is high,” said IATSE president Matthew Loeb. “There is much work to do, but I am pleased to report the union’s efforts are already well underway.” AI is a key bargaining issue for both the Writers Guild and SAG-AFTRA, and it will be for IATSE as well when it begins negotiations for a new film and TV contract next year. The WGA has been on strike for 65 days, and SAG-AFTRA members have voted overwhelmingly to authorize a strike if they don’t get a fair deal by July 12.

[thetechempire]

Kai-Fu Lee has declared war on Nvidia and the entire US AI ecosystem.

🔹 Lee emphasizes the need to focus on reducing the cost of inference, which is crucial for making AI applications more accessible to businesses. He highlights that the current pricing model for services like GPT-4 — $4.40 per million tokens — is prohibitively expensive compared to traditional search queries. This high cost hampers the widespread adoption of AI applications in business, necessitating a shift in how AI models are developed and priced. By lowering inference costs, companies can enhance the practicality and demand for AI solutions.

🔹 Another critical direction Lee advocates is the transition from universal models to “expert models,” which are tailored to specific industries using targeted data. He argues that businesses do not benefit from generic models trained on vast amounts of unlabeled data, as these often lack the precision needed for specific applications. Instead, creating specialized neural networks that cater to particular sectors can deliver comparable intelligence with reduced computational demands. This expert model approach aligns with Lee’s vision of a more efficient and cost-effective AI ecosystem.

🔹 Lee’s startup, 01. ai, is already implementing these concepts successfully. Its Yi-Lightning model has achieved impressive performance, ranking sixth globally while being extremely cost-effective at just $0.14 per million tokens. This model was trained with far fewer resources than competitors, illustrating that high costs and extensive data are not always necessary for effective AI training. Additionally, Lee points out that China’s engineering expertise and lower costs can enhance data collection and processing, positioning the country to not just catch up to the U.S. in AI but potentially surpass it in the near future. He envisions a future where AI becomes integral to business operations, fundamentally changing how industries function and reducing the reliance on traditional devices like smartphones.

[thedevmaster-tdm]

Introduction - Generative AI for Business Leaders Generative AI is revolutionizing the business landscape, offering unprecedented opportunities for innovation, efficiency, and competitive advantage. For business leaders, understanding and leveraging generative AI is crucial to staying ahead in a rapidly evolving market. This introduction explores the fundamentals of generative AI, its applications in various industries, and strategies for successful implementation.

#GenerativeAI #BusinessLeadership #AIInnovation

[archiveofkloss]

“We’re just seeing the very beginning of what’s ahead and what will be possible,” the supermodel and entrepreneur tells ELLE.

karlie on the future of women in tech:

"I’ve been doing this work for almost a decade now, and so much has changed in ways that make me very optimistic. I went to a public school in Missouri. I’m 31 years old, so it’s been a while since I was in high school, but back when I was a student, they did not have computer science programs. Now they do, and so do many, many, many public schools and private schools across the United States. There are now entry points for women and girls to start to learn how to code. It is much more understood how much technology is a part of shaping our world in every industry—not just in Silicon Valley, but also in music, media, finance, and business. But there’s a lot more, unfortunately, that continues to need to happen."

on growing kode with klossy into a global nonprofit:

"Kode With Klossy focuses on creating inclusive spaces that teach highly technical skills. We have AI machine learning and web dev. We have mobile app development and data science. They all are very creative applications of technology. Ultimately, right now, our programs are rooted in teaching the fundamentals of code and scaling the amount of people in our programs. This summer, we’re going to have 5,000 scholarships for free that we are giving to students to be a part of Kode With Klossy. We’ve trained hundreds of teachers through the years. We’ll have a few hundred instructors and instructor assistants this summer alone in our program. So what we’re focused on is continuing to ignite creative passion around technology."

on using technology to advance the fashion industry:

"We’re just seeing the very beginning of what’s ahead and what will be possible. That’s why it’s so important people realize that tech is not just for tech alone. It is [a tool to] drive better solutions across all industries and all businesses. Fashion is one of the biggest polluters of water. The industry has a lot of big problems to solve, and that’s part of why I’m optimistic and excited about more people seeing the overlap between the two. There is intersection in these spaces, and we can drive solutions in scalable ways when we see these intersections."

on embracing your fears:

"Natalie Massenet, the founder of Net-a-Porter, is an amazing entrepreneur and somebody I feel lucky to call a friend. She asked me years ago, and it’s always stuck with me through different personal and professional moments, “What would you do if you weren’t afraid?” That has always resonated, because we can get so stuck in our heads about being afraid of all sorts of different things—afraid of what other people will think, afraid of failure."

on the value of community in entrepreneurship:

"It takes a lot of courage for anyone [to be an entrepreneur]. It doesn’t matter your gender, your age, your experience level, that’s where community really does make a difference. It’s not just a talking point. So many of our Kode With Klossy scholars have come back as instructor assistants, and are now in peer leadership positions. So many of them have gone on to win hackathons and scholarships. It comes down to this collective community that continues to support and foster new connections among each other."

on breathing new life into Life magazine:

"Part of why I’m so excited about what we can build and what we are building with Bedford [Media, the company launched by Kloss and her husband, Joshua Kushner] is this intersection of a creative space like media—print media—and how you can continue to drive innovation with technology. And so that’s something that we’re very focused on, how to integrate the two. Lots more that we’re going to share at the right time, but we’re heads down on building the team and the company right now. I’m super excited."

on showing up for the people you love:

"I have two young babies, and I want to be the best mom I can be. So many of us are juggling so many different responsibilities and identities, both personally and professionally. Having women in leadership positions is so important, because our lived experiences are different from our male counterparts. And by the way, theirs is different from ours. It matters that, in leadership positions, to have different lived experiences across ages, genders, geographies, and ethnicities. It ultimately leads to better outcomes. All that to say, I’m just trying the best I can every day to show up for the people that I love and do what I can to help others."

on the intrinsic value in heirloom pieces:

"For our wedding, my husband bought me a beautiful Cartier watch. Some day I will pass that on to our daughter, if I’m lucky enough to have one. Or [I’ll pass it on to] my son; I have two sons. For our wedding, I also bought myself beautiful diamond earrings. There was something very symbolic about that to me, like, okay, I can also buy myself something. That’s why jewelry, to me—as we’re talking about female entrepreneurship and women in business and women in tech—is something that’s so emotional and personal. So I bought myself these vintage diamond earrings from the ’20s, with this beautiful, rich history of where they had been and who had owned them and wore them before. That’s the power of jewelry, whether it’s vintage or new, you create memories and it marks moments in life and in time. And then to be able to share that with future generations is something I find really beautiful."

[annajade456]

Mastering DevOps: A Path to Tech Leadership and Innovation

In the ever-evolving landscape of technology, DevOps stands out as an indicator of innovation and efficiency. As we navigate the digital age, the role of DevOps, which seamlessly blends development and operations practices, has become increasingly important. It not only accelerates software delivery but also promotes collaboration, enhances automation, and ensures the delivery of high-quality applications. If you're considering a career in tech, DevOps is an enticing and promising option. In this comprehensive exploration, we'll dive deep into the world of DevOps careers, unveiling the manifold opportunities, challenges, and avenues for growth that it offers.

Why DevOps? The Irresistible Appeal

1. High Demand for DevOps Professionals

In a world where businesses are constantly striving for efficiency and agility, DevOps professionals are in high demand. Organizations of all sizes, from startups to Fortune 500 giants, recognize the value of DevOps in streamlining development processes, enhancing automation, and improving collaboration among cross-functional teams. This demand translates into a plethora of job opportunities for DevOps experts.

2. Competitive Salaries

In the realm of tech careers, compensation is often a significant factor. DevOps practitioners frequently enjoy competitive salaries, and experienced DevOps engineers, in particular, are handsomely rewarded. This makes DevOps not only a fulfilling career but also a financially rewarding one.

3. Versatility Across Industries

One of the striking features of a DevOps career is its versatility. DevOps skills are transferable across various industries, including finance, healthcare, e-commerce, and more. The fundamental principles and tools of DevOps are universally applicable, allowing you to explore different sectors while leveraging your expertise.

4. Continuous Learning and Adaptation

The tech world thrives on change, and DevOps is no exception. This dynamic field continuously evolves with the emergence of new tools and practices. Staying updated with the latest trends and technologies is not just a requirement but a thrilling aspect of a DevOps career. The pursuit of knowledge and adaptation are ingrained in the DevOps culture.

5. Enhanced Efficiency Through Automation

At the core of DevOps lies the principle of automation. DevOps practices emphasize automating manual processes, reducing errors, and accelerating deployment cycles. The result is enhanced efficiency in development pipelines, enabling teams to deliver software faster and with higher quality.

6. Collaboration as a Core Value

DevOps promotes collaboration and communication between traditionally siloed teams, such as development and operations. This cultural shift towards teamwork and shared responsibilities fosters a more inclusive and productive workplace environment.

7. A Path to Leadership

A DevOps career is not just about technical skills; it's also a pathway to leadership positions. As you gain experience and expertise, you'll find yourself well-equipped to step into roles like DevOps manager, architect, or consultant, where you can influence and shape the DevOps practices of your organization.

The Future of DevOps: A World of Innovation

As we peer into the future, the DevOps landscape promises even more exciting developments:

1. Advanced Automation and AI

Automation will continue to be a driving force in DevOps, with artificial intelligence (AI) playing a more significant role. AI-powered tools will enhance predictive analytics, optimize resource allocation, and further reduce manual intervention in the software development lifecycle.

2. DevOps in Edge Computing

The rise of edge computing, driven by the Internet of Things (IoT), presents new challenges and opportunities for DevOps. DevOps practices will expand to accommodate the unique demands of edge environments, enabling real-time data processing and analysis at the edge of the network.

3. Security-First DevOps

With cybersecurity concerns on the rise, DevOps will place an even greater emphasis on security practices. DevSecOps, the integration of security into the DevOps pipeline, will become standard practice, ensuring that security is not an afterthought but an integral part of the development process.

4. Hybrid and Multi-Cloud DevOps

Hybrid and multi-cloud environments are becoming increasingly prevalent. DevOps will continue to evolve to seamlessly integrate on-premises and cloud resources, providing organizations with the flexibility to choose the best infrastructure for their needs.

5. DevOps as a Service

DevOps as a Service (DaaS) is gaining traction. Organizations will increasingly turn to third-party providers for DevOps solutions, allowing them to focus on their core competencies while leveraging the expertise of specialized DevOps teams.

In a world driven by technology, a career in DevOps offers an exciting journey filled with opportunities for growth and innovation. Whether you're just starting your career or looking to make a transition, DevOps holds the promise of a rewarding path.

To embark on this journey, it's essential to equip yourself with the right skills and knowledge. ACTE Technologies, a renowned provider of DevOps training and certification programs, stands ready to be your guiding light. Their expert-led courses can help you build a complex foundation in DevOps principles, master the relevant tools, and stay ahead in this ever-evolving field.

So, embrace the future of technology with a career in DevOps, and let ACTE Technologies be your trusted companion on the road to excellence. As you explore the endless possibilities of DevOps careers, may your passion for innovation and your commitment to continuous learning lead you to success and fulfillment.

[dishachrista]

Exploring Game-Changing Applications: Your Easy Steps to Learn Machine Learning:

Machine learning technology has truly transformed multiple industries and continues to hold enormous potential for future development. If you're considering incorporating machine learning into your business or are simply eager to learn more about this transformative field, seeking advice from experts or enrolling in specialized courses is a wise step. For instance, the ACTE Institute offers comprehensive machine learning training programs that equip you with the knowledge and skills necessary for success in this rapidly evolving industry. Recognizing the potential of machine learning can unlock numerous avenues for data analysis, automation, and informed decision-making.

Now, let me share my successful journey in machine learning, which I believe can benefit everyone. These 10 steps have proven to be incredibly effective in helping me become a proficient machine learning practitioner:

Step 1: Understand the Basics

Develop a strong grasp of fundamental mathematics, particularly linear algebra, calculus, and statistics.

Learn a programming language like Python, which is widely used in machine learning and provides a variety of useful libraries.

Step 2: Learn Machine Learning Concepts

Enroll in online courses from reputable platforms like Coursera, edX, and Udemy. Notably, the ACTE machine learning course is a stellar choice, offering comprehensive education, job placement, and certification.

Supplement your learning with authoritative books such as "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron and "Pattern Recognition and Machine Learning" by Christopher Bishop.

Step 3: Hands-On Practice:

Dive into real-world projects using both simple and complex datasets. Practical experience is invaluable for gaining proficiency.

Participate in machine learning competitions on platforms like Kaggle to challenge yourself and learn from peers.

Step 4: Explore Advanced Topics

Delve into deep learning, a critical subset of machine learning that focuses on neural networks. Online resources like the Deep Learning Specialisation on Coursera are incredibly informative.

For those intrigued by language-related applications, explore Natural Language Processing (NLP) using resources like the "Natural Language Processing with Python" book by Steven Bird and Ewan Klein.

Step 5: Learn from the Community

Engage with online communities such as Reddit's r/Machine Learning and Stack Overflow. Participate in discussions, seek answers to queries, and absorb insights from others' experiences.

Follow machine learning blogs and podcasts to stay updated on the latest advancements, case studies, and best practices.

Step 6: Implement Advanced Projects

Challenge yourself with intricate projects that stretch your skills. This might involve tasks like image recognition, building recommendation systems, or even crafting your own AI-powered application.

Step 7: Stay updated

Stay current by reading research papers from renowned conferences like NeurIPS, ICML, and CVPR to stay on top of cutting-edge techniques.

Consider advanced online courses that delve into specialized topics such as reinforcement learning and generative adversarial networks (GANs).

Step 8: Build a Portfolio

Showcase your completed projects on GitHub to demonstrate your expertise to potential employers or collaborators.

Step 9: Network and Explore Career Opportunities

Attend conferences, workshops, and meetups to network with industry professionals and stay connected with the latest trends.

Explore job opportunities in data science and machine learning, leveraging your portfolio and projects to stand out during interviews.

In essence, mastering machine learning involves a step-by-step process encompassing learning core concepts, engaging in hands-on practice, and actively participating in the vibrant machine learning community. Starting from foundational mathematics and programming, progressing through online courses and projects, and eventually venturing into advanced topics like deep learning, this journey equips you with essential skills. Embracing the machine learning community and building a robust portfolio opens doors to promising opportunities in this dynamic and impactful field.