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Dev
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Part 15: Building Your Own AI - Ethical Considerations in AI Development
- Ensuring AI systems are fair, transparent, and respect privacy is critical for building trust and promoting responsible development.
- Key ethical challenges in AI development include algorithmic bias and fairness, transparency, privacy concerns, and accountability.
- Frameworks for ethical AI development include FAT-ML, AI Ethics Guidelines by the European Commission, The Asilomar AI Principles, and ISO/IEC Standards for AI.
- Practical steps to build ethical AI include starting with ethical design, continuous monitoring and evaluation, educating teams on ethical practices, and user-centric development.
- Case studies highlight solutions for bias in recruitment algorithms, privacy in healthcare AI, and explainability in financial AI.
- The future of ethical AI includes challenges of regulating AI autonomy, ensuring alignment with human values, and managing societal impact.
- Ethical AI development is a societal responsibility that requires collaboration among developers, ethicists, policymakers, and the public.
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Medium
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3 Proven Ways to Earn Serious Money with Google Gemini
- Gemini is gaining popularity as a way to earn money online.
- There are 3 proven ways to make money with Gemini: for beginners, intermediate, and experts.
- Using Gemini, you can create content more easily.
- Gemini can also write articles for you, but it's important to add your own touch before posting them on platforms like Medium.
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Technode
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247
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NVIDIA to launch GB300 in March 2025 with Foxconn as top supplier: report
- Taiwan-based electronics manufacturer Foxconn remains the largest supplier of NVIDIA's servers.
- NVIDIA plans to launch its next-generation GB300 AI server lineup at the GTC in March 2025.
- Foxconn secured the largest share of GB300 orders due to its vertically integrated resources.
- Industry analysis suggests that GB300 servers may adopt a 100% liquid cooling solution.
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Lambdatest
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216
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How to Use AI in QA Testing: A Complete Guide
- Incorporating AI in QA testing empowers teams to enhance their testing process.
- AI involves using artificial intelligence, machine learning algorithms, deep learning models, and other AI testing tools to improve efficiency and speed while reducing the error factor caused by humans and minimizing costs.
- AI in quality assurance refers to using artificial intelligence technologies to enhance the process of testing software applications.
- Leveraging AI in QA can help enhance the QA testing process and overcome challenges such as slow testing cycles, human errors, and difficulty managing large test suites.
- The six levels of AI in QA testing represent a spectrum of automation, from manual testing to fully AI-based testing.
- Using AI in QA testing involves a systematic approach to ensure accuracy, efficiency, and scalability.
- AI-driven tools are reshaping software testing by automating key processes and enhancing efficiency.
- AI is poised to bring transformations to the QA process, driving efficiency, accuracy, and innovation.
- By using AI in QA practices, organizations can accelerate release cycles, reduce testing costs, and enhance product quality.
- AI QA is a smart approach to maintaining competitiveness and ensuring robust software performance.
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Medium
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371
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Why Is It So Hard to Find a JOB Today? Here’s the Truth No One Tells You
- The job market has changed, making it harder to find a job.
- The rules, competition, and the entire job search process have changed.
- Algorithms, online portals, and confusing systems play a significant role in the application process.
- Understanding these changes can help you navigate the job market more effectively.
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Telecomtalk
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StarHub Partners with Infosys Compaz to Enhance AI and Cloud Capabilities
- Infosys Compaz, the joint venture between Infosys and Temasek, is strengthening its partnership with StarHub in Singapore to accelerate digital transformation.
- The focus will be on AI, cloud technologies, and cybersecurity solutions to transform StarHub into a cloud-first enterprise.
- Infosys Compaz will deliver workplace transformation tools and AI solutions to capture new market opportunities.
- The collaboration aims to co-create tailored AI models using telecommunications data and deliver solutions for specific industry use cases.
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Medium
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2.5k
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Do you think AI like Chatgpt are destroying digital marketing?
- AI is often seen as a disruptor, capable of automating content creation and data analysis.
- However, the core aspects of digital marketing - creativity, strategy, and emotional connection - remain distinctly human.
- AI can assist with tasks but not replace empathy, emotional intelligence, and cultural awareness in marketing.
- Instead of replacing marketers, AI enhances productivity and creativity, allowing for more effective campaigns.
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Pymnts
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AI, Omnichannel and Social Commerce: Inside the 2024 D2C Transformation
- D2C retailers are adopting AI-powered personalisation and tailoring options based on individual preferences, using advanced data analytics and marketing algorithms which process large amounts of consumer data.
- Omnichannel strategies are being adopted to offer seamless experiences to customers across various channels during their shopping journeys.
- The D2C model is proving popular, with US sales projected to reach $177.3 billion by 2024. Subscription models and loyalty programmes are key in driving growth and providing ongoing customer retention and incentive for loyalty.
- D2C retailers are embracing ethical and sustainable practices, including eco-friendly packaging, transparent supply chains and a focus on resale, repair and rental by 2030.
- Social media platforms and mobile shopping are becoming integral to the shopping experience, with social commerce accounting for over $67 billion US sales in 2023 and mobile sales reaching $2.2 trillion in 2023, representing 60% of global eCommerce sales.
- D2C retailers are responding to high cart abandonment rates by offering more flexible payment options including BNPL schemes, digital wallets and payment processors which prioritise certain payment options to meet consumer demand.
- Full Glass Wine Co. faced the challenge of launching its direct-to-consumer model in a traditional and fragmented industry, using AI to personalise wine recommendations and selections based on consumer behaviour, preferences and purchasing patterns.
- Instacart is implementing AI-powered Caper Carts, equipped with digital screens in the United States and Austria, aimed at streamlining the checkout process and enhancing the shopping experience.
- The lines between online and offline shopping continue to blur, with 73% of retail consumers opting for multiple touchpoints during their shopping journeys.
- Rent the Runway is launching a new $119 per month subscription tier aimed at expanding its customer base and setting the stage for D2C growth in 2025.
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Devopsonline
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Key AI Predictions within the growth and sustainability sector
- Data centres will face mounting pressure to reconcile AI’s surging energy requirements with strict sustainability goals.
- Companies that initially rushed into AI will reevaluate its financial and energy impact, shifting towards selective, high-value applications.
- Digital twins will be essential for data centres to meet AI goals sustainably and manage power, renewable sources, and cooling measures.
- EU's 2025 Energy Efficiency Directive will prompt data centre accountability and drive greener practices and adoption of tools like digital twins.
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Insider
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Meet the power players at Elon Musk's xAI, the startup taking on OpenAI with its Grok chatbot
- Elon Musk's xAI has raised more than $12 billion and is valued at $50 billion.
- The founding members of xAI have experience at the likes of Google, OpenAI and Tesla.
- To compete with rivals like OpenAI and Google, xAI plans to tenfold the size of its Memphis supercomputer.
- Meet the key figures at xAI who are driving the startup's rapid growth including Igor Babuschkin, Manuel Kroiss, Yuhuai (Tony) Wu, Christian Szegedy, Jimmy Ba, Toby Pohlen, Ross Nordeen, Greg Yang, Guodong Zhang and Zihang Dai.
- Launched by Musk in July 2023 to take on OpenAI, the company behind ChatGPT, xAI's lofty goal is to "understand the universe."
- Since its launch, xAI has released a chatbot called Grok, which is now free for everyone to use.
- Grok has been trained on user data from X, the company formerly known as Twitter, which Musk also owns.
- To compete with rivals like OpenAI and Google, xAI plans to tenfold the size of its Memphis supercomputer, which trains its AI models.
- Manuel Kroiss coauthored several research papers on machine learning.
- Yuhuai (Tony) Wu previously worked at Google and Google DeepMind and spent a few months as a research scientist intern at OpenAI.
- Christian Szegedy joined xAI from Google, where he spent 14 years of his career.
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Medium
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314
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The whispers inch the walls *The invisible terror*
- Emma moves into her green flat to escape her past.
- Whispers start coming from the walls, initially dismissed as settling noises.
- Whispers become louder and more distinct, mentioning secrets and observing Emma.
- Emma discovers an old journal hidden in the floorboards, revealing information about the previous tenant, Vincent.
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Lambdatest
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213
Image Credit: Lambdatest
Artificial Intelligence (AI) in Software Engineering
- Artificial intelligence is playing a crucial role in enhancing software design, development, testing and debugging processes, making tasks faster and more efficient.
- Advancements in machine learning and natural language processing further enable teams to handle complex activities like predictive maintenance, code generation, and intelligent debugging.
- The introduction of AI boosts software engineering in terms of efficiency, accuracy, decision-making, scalability, continuous improvement, and cost savings.
- Applications of AI in software engineering extend into numerous areas, such as design, development, testing and debugging. AI in software design allows smarter and data-driven decisions, revolutionizing design tools and methodologies.
- AI improves software development by providing tools and approaches that streamline coding processes, increase code quality, and enable intelligent automation, such as code generation, optimization, and refactoring.
- AI substantially contributes to testing and debugging through predictive analytics, self-healing test scripts, root cause analysis, intelligent bug detection and debugging, and AI-powered testing platforms, like LambdaTest's KaneAI.
- However, implementing AI in software engineering has several shortcomings, such as reliability and model interpretability, skill gaps, biases, ethical issues, system complexity and maintenance, and data privacy and security risks.
- To successfully integrate AI into software engineering, organizations should start small and scale, ensure high-quality data, mitigate biases, and foster continuous learning.
- The future of artificial intelligence in software engineering is set to bring advancements in automation, project management, resource optimization, explainable AI, and human-AI collaboration models, and futuristic tools like AI-based predictive maintenance and real-time debugging.
- Adopting AI would help software engineers develop more intelligent and flexible systems and applications.
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Medium
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62
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Personalized Customer Strategy in Healthcare in the Age of AI
- Personalized health care is a patient-centered strategy tailoring healthcare services to individual patient needs and aims to provide more efficient and effective healthcare. Early diagnosis and treatment and customized care and access to the best possible care promise to help patients stay in the comfort of their homes AI-powered tools and technologies integrate across numerous data sources, give a unified view of the patient, and anticipate patient needs through communication on multiple touch-points that include websites, mobile apps, and email campaigns. AI can take a person-centric approach to delivering technology-enabled healthcare services and solutions and on-demand access to key healthcare information for each individual.
- The use of AI in personalizing customer experiences within healthcare offers several opportunities for providers to meet patient needs more effectively. AI algorithms can automate administrative tasks and facilitate seamless communication among a care team, leading to improved patient outcomes. AI can reduce reliance on costly interventions and hospital admissions by preventing unnecessary procedures with accurate diagnosis and treatment recommendations resulting in huge cost savings for both healthcare providers and payors.
- Despite the many potential benefits of integrating AI into healthcare, there are several ethical issues, including data privacy and security, algorithmic bias, and transparency and explainability of AI. Ethics-related issues such as bias in algorithms, privacy concerns, and the ability of AI to make decisions inconsistent with human values all require bolstered considerations and support.
- Incorporating AI into healthcare practices provides several benefits including optimized healthcare operations, improved communication between healthcare providers and patients, and improved patient outcomes. Challenges include the need to be compliant with privacy regulations and the challenges of integrating the new technology with pre-existing healthcare structures.
- To mitigate ethical risks associated with AI, healthcare providers and policy framers must establish explicit ethical guidelines in developing AI. AI systems must comply with data anonymization, and developers must rigorously monitor AI systems for algorithmic bias to promote algorithmic equity. Regular dialogue must take place among healthcare providers, AI developers, and policymakers regarding ethical issues and responsible dissemination of AI throughout the healthcare industry.
- AI can improve personalized healthcare services by analyzing patient data and developing customized recommendations for treatment. AI can automate administrative tasks, streamline workflows, and provide more accurate and efficient diagnoses, leading to increased efficiency, cost savings, and patient satisfaction. AI will aid in the evolution of healthcare toward becoming more patient-centric, efficient, and effective.
- Several healthcare companies successfully using AI-powered tools and technologies include Kaiser Permanente, Cleveland Clinic, Mayo Clinic, MD Anderson Cancer Center, and Siemens Healthineers. These AI examples demonstrate significant success in providing more personalized patient care and improving patient outcomes through innovative AI-driven solutions.
- The ability of healthcare providers to adapt, address challenges and embrace the opportunities provided by AI will enable the healthcare industry to evolve significantly and meet patient needs more effectively. AI-powered tools will further improve personalized healthcare services, making patient experiences even more interactive and efficient.
- AI's use in healthcare is expected to continue being incorporated into various health-related sectors such as drug discovery and development. Future breakthroughs and advances can be expected in personalized patient treatment options and AI-powered engagement tools. As AI continues to evolve, the potential benefits provided by personalized healthcare are seemingly limitless.
- Additional challenges include regulatory compliance and customers' trust in the use of AI in healthcare. Ethical guidelines should be established to address privacy concerns and algorithmic bias prevalent in AI implementation in healthcare while ensuring that customers understand how these AI systems arrive at their recommendations. AI in healthcare offers numerous opportunities towards realizing the full potential of personalized customer strategy, thereby shaping a future wherein healthcare will be more patient-centric, efficient and effective.
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Medium
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3
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The Emergence Of Ai-powered Cryptos: What Investors Should Expect Come 2025
- The combination of AI and blockchain technology has created a powerful synergy where AI can accelerate and streamline blockchain networks while also ensuring an underlying secure environment for autonomous operation of AIs.
- AI in cryptocurrencies brings scalability, security, and decision-making for blockchain applications, enhancing DeFi platforms that combine AI with improved trading and liquidity optimization.
- AI-driven smart contracts enable greater versatility and tailor-made financial products, offering dynamic mechanisms for contract adaptation to real-world conditions and improving flexibility by providing a highly efficient self-packaging solution.
- Decentralized AI networks will become present for consumers to train machine learning models with their data while maintaining ownership over their information, enabling greater privacy and transparency in singularity technology pools.
- AI helps improve the security of blockchain networks by monitoring blockchain transactions using AI to recognize suspicious activities, enabling machine learning algorithms to remain adaptive even in the face of emerging threats.
- AI makes mining more efficient by ensuring that computational resources are used optimally while also reducing energy demand, ushering in a new era for cryptocurrencies that is much more eco-friendly.
- Fetch.ai and Cortex are already utilizing the power of AI and blockchain to create phenomenal solutions that could dominate the crypto market landscape in the future.
- Investors should be vigilant and pay attention to upcoming regulatory changes that can affect AI-built cryptos, as well as scalability and integration issues.
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Dev
1d
58
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How to integrate CopilotKit with Prisma Integration into your nextJs project
- CopilotKit is an AI assistant and agent integration tool for applications.
- To integrate CopilotKit with a Prisma-backed Next.js project, follow these steps:
- - Create a Next.js project using 'npx create-next-app@latest'
- - Install Prisma ORM and configure the Prisma schema
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