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Dev
25m
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Building a Real-Time Voice Assistant with Local LLMs on a Raspberry Pi
- Building a Real-Time Voice Assistant with Local LLMs on a Raspberry Pi
- In this project, the goal was to capture voice input through a web interface, process the text using a local LLM running on the Raspberry Pi, generate voice responses using a TTS engine, and stream everything in real-time via WebSockets.
- The Raspberry Pi was set up with the latest Raspberry Pi OS and the hardware interfaces were enabled. Ollama was installed to run local LLMs like Mistral on the Pi. Piper, an open-source TTS engine, was chosen for offline voice generation.
- A simple Node.js server was created to accept text from the client, process it using Mistral, convert the LLM response to speech with Piper, and stream the audio back to the client. For the frontend, a React app was developed to record voice input, display real-time text responses, and play the generated speech audio.
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Medium
37m
104
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The Deepfake Deception: How to Spot AI-Manipulated Videos and Audio Before They Fool You
- Deepfake technology is being used to create AI-generated videos and audio recordings that manipulate reality with near-perfect precision, posing significant threats to various sectors including journalism, finance, and politics.
- Common uses of deepfakes include political manipulation, corporate fraud, fake celebrity endorsements, and revenge tactics like non-consensual pornography, with 96% of deepfake videos targeting women.
- Detection methods for spotting deepfake videos and audio involve examining details like unnatural eye movements, facial expressions, lighting inconsistencies, robotic voice characteristics, and verifying video sources using tools like InVID.
- Tools like Microsoft's Video Authenticator, Deepware Scanner, and Reality Defender are aiding in the fight against deepfakes by providing deepfake probability scores, real-time video analysis, and identification of subtle distortions.
- To protect oneself, questioning the source before sharing, staying informed about AI advancements, reporting suspicious content to fact-checking organizations and social media platforms, and promoting media literacy are crucial steps.
- The battle against deepfakes requires continued vigilance, education, and regulatory measures to hold creators accountable and safeguard the truth from being manipulated for deceptive purposes.
- Critical thinking remains a vital tool in combating the spread of deepfake deception and ensuring that individuals, organizations, and society at large are equipped to identify and counteract these manipulations.
- As deepfake technology advances, it is essential for individuals to be proactive in understanding and addressing this digital threat to prevent financial losses, reputational damage, and the erosion of trust in media and information.
- By being informed, vigilant, and actively engaging in efforts to detect and combat deepfakes, individuals can play a crucial role in safeguarding against the harmful impact of AI-manipulated content on personal and societal levels.
- Defending the truth against deepfake deception requires a collective effort to promote awareness, accountability, and critical thinking to mitigate the detrimental effects of falsified information and safeguard the integrity of digital communication.
- Empowering individuals with the knowledge and tools to identify and address deepfake threats is essential in preserving trust, authenticity, and transparency in an increasingly interconnected and digitally mediated world.
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Medium
58m
163
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The AI Revolution: Automations & Agents Shaping 2025 and Beyond
- AI has transitioned from simple automation tools to autonomous agents capable of decision-making.
- AI agents are collaborators, actively participating in tasks that once required human expertise.
- Advancements in AI technology enable agents to handle increasingly complex tasks without human intervention.
- Organizations and individuals must adapt and embrace AI to stay competitive in the changing landscape.
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9 Likes
Medium
1h
52
Image Credit: Medium
Can AI-Generated Essays Pass the Test? Insights for Educators and Students
- AI-generated essays are becoming more prevalent as students seek assistance with writing.
- Educators can identify AI-generated essays through generic arguments, repetitive phrasing, inconsistencies in style, and detection tools.
- There is a debate regarding the ethicality of using AI in essay writing, with arguments for and against it.
- To navigate this landscape, educators and students should focus on the process over the product, teach ethical AI use, and leverage technology for enhanced learning.
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Medium
1h
223
Image Credit: Medium
AI Skills Every Product Manager Needs in 2025
- Product managers play a crucial role in overseeing projects and can benefit significantly from AI skills to improve processes and outcomes.
- AI product managers are expected to lead the field and have a significant impact by adopting AI knowledge in their roles.
- As AI becomes more integrated, ethical concerns and bias issues arise, and AI product managers will play a key role in addressing these issues.
- Product managers need to evaluate and implement AI-driven tools effectively to enhance workflow automation and decision-making.
- Understanding the functionalities of AI tools is crucial for product managers to justify their adoption to stakeholders and communicate their value.
- AI enables product managers to make data-driven decisions, assess different options, and strategically drive product success.
- Balancing human expertise with AI capabilities is essential for AI product managers in product development.
- Product managers need to navigate AI ethics, bias, and compliance issues to ensure ethical and responsible AI use.
- Mastering AI skills is becoming essential for product managers as AI continues to shape the future of product management.
- Employers are seeking professionals with AI expertise to drive innovation and business transformation in various industries.
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13 Likes
Medium
1h
255
Image Credit: Medium
How LLMs Learn: The Pre-Training Phase Explained
- Large language models (LLMs) learn during the pre-training phase by being fed a huge amount of text to understand language rules and context.
- Common Crawl provides data from 250 billion web pages for pre-training, but preprocessing to remove noise is crucial.
- Tokenization breaks text into manageable tokens for numerical processing, with methods like Byte Pair Encoding (BPE) being common.
- Models like GPT-4o use subword-based tokenization to handle large vocabularies more efficiently.
- Training involves Next Token Prediction and Masked Language Modeling to learn language structure and relationships between tokens.
- Base models learn to generate text one token at a time and serve as a starting point for further fine-tuning.
- Base models can memorize text patterns but may struggle with reasoning tasks due to limited structured understanding.
- In-context memory allows base models to adjust responses based on the provided context, demonstrating versatility without fine-tuning.
- Base models excel in replicating text based on memorized patterns but may lack originality and deep reasoning abilities.
- In the pre-training phase, LLMs develop foundational skills by learning from raw data before advanced techniques are applied for post-training.
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Dev
1h
24
Image Credit: Dev
Does a Robot Have a “Heart”? Or Why Should We Consider Ethics in AI?
- Ethics is the philosophical discipline that studies morality, moral principles, and norms regulating human behavior in society, addressing questions of good and evil, justice, responsibility, and proper conduct.
- Robots need ethics to handle moral dilemmas, as illustrated by the example from the movie I, Robot.
- Current AI developments emphasize autonomous decision-making, self-learning models, interactive agents, scientific research, security measures, and control methods to address moral concerns.
- To enable ethical choices in AI, understanding human value systems is crucial, suggesting the need to equip robots with similar frameworks.
- Establishing a Neural Network Value System (NNVS) based on ten basic human values can serve as a moral compass for AI agents.
- The NNVS aims to help AI agents orient themselves in the environment, analyze behaviors, and make decisions aligned with human values.
- The combination of an AI agent's mission and NNVS creates a framework similar to a ship captain's strategic direction and a coordinate grid for decision-making based on basic values.
- Future posts will delve into conceptual and mathematical explorations of each basic value, identifying values from various data sources, and promoting safe and ethically-oriented AI development.
- Continued discussions on creating ethical AI agents are essential, emphasizing the significance of integrating human values and ethical frameworks into artificial intelligence.
- Exploring the complex interplay between AI and ethics will pave the way for responsible and morally sound advancements in artificial intelligence.
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The Verge
1h
476
Image Credit: The Verge
Google’s co-founder tells AI staff to stop ‘building nanny products’
- Google co-founder Sergey Brin has urged the DeepMind AI division to accelerate efforts to win the AGI race.
- Brin suggested working longer hours, coming into the office every weekday, and prioritizing simple solutions to problems.
- He also highlighted the need to remove filters and punts from Google's AI products.
- The final race to AGI (Artificial General Intelligence) is underway, according to Brin.
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1 Like
Dev
2h
97
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A beginner's guide to the Wan-2.1-1.3b model by Wan-Video on Replicate
- This is a simplified guide to an AI model called Wan-2.1-1.3b by Wan-Video.
- The model excels at creating 5-second 480p videos from text descriptions.
- It is built on a diffusion transformer architecture enhanced with spatio-temporal variational autoencoders.
- The model supports both English and Chinese text input and offers configurable generation parameters.
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5 Likes
Medium
45m
30
Image Credit: Medium
Liquid Neural Network: Putting the Network to Test in the Chaotic World
- The article discusses the Liquid Neural Network (LNN) as an improvement to Recurrent Neural Networks (RNN), focusing on the training algorithm Backpropagation through Time (BPTT).
- The LNN proposes using the vanilla BPTT algorithm over the adjoint method to address memory consumption and calculation errors during training.
- The article highlights the importance of testing the stability of the LNN model regarding gradients, rapid changes, non-linear dynamics, and bounded hidden states.
- Testing for exploding or vanishing gradients showed stable results, followed by testing rapid changes and non-linear dynamics using the Lorenz System equations.
- The Lorenz System demonstrated chaotic behavior, but the LNN model showed stability and ability to process non-linear dynamics effectively.
- Further testing on the bounds of hidden states ensured stability over longer time steps and the ability to process complex patterns with greater stability compared to a standard RNN.
- Training the LNN against the Lorenz input involved ensuring the model's capability to predict values accurately without divergence in the curves.
- The results indicated the LNN's capacity to process chaotic and dynamic system inputs effectively, promising applications in dynamic AI scenarios.
- Future exploration may focus on the LNN architecture's challenges and further enhancements in subsequent parts of the study.
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1 Like
Medium
48m
30
Image Credit: Medium
The Correct Way to Use AI for Writing
- AI writing tools can be your best friend in the creative process.
- Using AI tools can be the hand holders for you in the writing process.
- AI image generators are now at your fingertips, saving you time in finding matching images for your story.
- Deep Seek offers research support and innovative content ideas for long-form writing.
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1 Like
Medium
54m
129
Image Credit: Medium
Trash Alchemy: Turning Waste into Wealth and Reimagining Our Planet’s Future
- Trash alchemy is the global revolution of transforming waste into a valuable resource.
- The circular economy offers a solution to turn waste into a resource that fuels industries, revitalizes communities, and heals the planet.
- Examples of waste transformation include plastic bricks for construction, biogas from food scraps powering cities, and Olympic medals made from recycled e-waste.
- Trash alchemy has the economic potential of unlocking trillions in global benefits and environmental wins, but challenges include the exploitation of informal recyclers and infrastructure gaps.
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7 Likes
TechCrunch
1h
655
Image Credit: TechCrunch
Sergey Brin says RTO is key to Google winning the AGI race
- Google co-founder Sergey Brin sent a memo to employees, urging them to return to the office 'at least every weekday' in order to help the company win the AGI race.
- Brin believes Google could build AGI, a superintelligent AI system on par with human intelligence.
- The memo shows the pressure Silicon Valley giants are feeling to compete in AI.
- Brin has returned to Google to help the company catch up in the AI race.
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17 Likes
Insider
2h
578
Image Credit: Insider
Elon Musk says there's 'only a 20% chance of annihilation' with AI
- Elon Musk believes there's a 20% chance of AI leading to human annihilation.
- Despite his concerns, Musk sees an 80% probability of a good outcome with AI.
- Musk expects AI to exceed human intelligence by 2029 or 2030.
- He believes AI poses an existential risk and has founded OpenAI to counteract it.
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7 Likes
Medium
2h
188
Image Credit: Medium
Want to save time and get more done?
- AI tools can enhance productivity and efficiency.
- Top 5 AI tools that can make you more productive.
- ChatGPT assists in generating emails, blog posts, and more.
- Grammarly fixes grammar and suggests better words.
- Notion AI helps in organizing tasks and notes.
- Jasper AI generates content and marketing copy.
- Otter.ai transcribes meetings in real time.
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