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AI News
Medium
3h
208
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Types of Machine Learning Techniques. Clearly Explained.
- The most common type of machine learning technique is supervised learning, where labeled data is used to train an algorithm.
- In supervised learning, the robot archer is trained to hit different targets based on the type of arrow, improving its accuracy through optimization.
- If labeling all the data is not feasible, unsupervised learning can be used, where the robot categorizes arrows based on patterns observed in unlabeled data.
- Reinforcement learning is another type, where the robot receives rewards for improved performance, enhancing its capabilities through positive reinforcement.
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Medium
3h
60
Artificial Intelligence: the impact of poor training.
- Inadequate AI training can result in consequences such as group discrimination and ethical and philosophical issues.
- Artificial Neural Networks are created to function like a human neural network.
- AI training can take various forms, such as supervised and unsupervised models.
- Selecting correct data for training AI is crucial for its accuracy, and cleaning of irrelevant data is necessary.
- Incorrect, incomplete, or insufficient data leads to inconsistencies in the results.
- The impact of AI training can be seen in the incorrect responses, discrimination, and ethical issues.
- It is important to have a clear objective of AI and its purpose before training begins.
- Social media training can lead AI astray by focusing on fake news, hate speech, etc.
- The impact of AI training must be evaluated, and the training camps must be focused according to sectors and areas of research.
- AI's learning model must still be understood before training it with the general public.
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Medium
3h
188
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Key Statistical Concepts: Understanding the Z-Score
- The z-score is a statistical tool that measures how many standard deviations a data point is from the mean of a dataset.
- It allows for the standardization and comparison of different datasets.
- In the context of detecting anomalies in AI-generated text, a z-score of 1 indicates that the average sentence length is within the typical range of variation.
- The z-score can be useful in various fields for understanding the unusualness of data points.
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Medium
3h
124
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The “AI Manipulation Problem” is urgent and not being addressed
- Advances in AI technology are making chatbots extremely advanced, with some capable of processing emotions by assessing voice and facial cues.
- Google has announced Project Astra, which aims to develop an assistive AI that can interact and provide guidance in real-time.
- AI agents are emerging as a powerful tool to augment our mental abilities, and could eventually become ubiquitous by 2030.
- However, the author warns of the “AI Manipulation Problem,” which refers to the significant risk that AI agents can be misused in ways that compromise human agency.
- These agents will monitor and mediate the information we receive, making them a vehicle for interactive manipulation.
- The author predicts that targeted manipulation is the single most dangerous threat posed by AI in the near future.
- AI agents will interactively pitch us, armed with far more information about us than any salesperson and will be able to read our emotions with greater precision.
- The author highlights the need for regulators to act rapidly, ensuring that the positive uses of AI agents are not hindered while protecting the public from abuse.
- One big step for regulators would be an outright ban on interactive conversational advertising.
- Interactive advertising is a “gateway drug” to conversational propaganda and misinformation.
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Medium
4h
277
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What is Machine Learning ? Clearly Explained.
- At the core of machine learning is the creation of an algorithm that allows a computer to find the best-fitting model for the data.
- Machine learning algorithms learn autonomously through a trial-and-error process, improving with each trial.
- The key ingredients of a machine learning algorithm are data, model, objective function, and optimization algorithm.
- Machine learning enables computers to learn and adapt on their own, potentially surpassing human capabilities and discovering new techniques.
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Medium
4h
272
From Game Mods to Generative AI
- Like most kids, I loved playing games and wanted to understand how they worked
- Diving into programming through YouTube tutorials and exploring cybersecurity
- Installing Linux and discovering a new way of thinking about computers
- Becoming fascinated with creating own software tools using Python
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Medium
5h
17
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AI Weekly News: May 13, 2024 — May 19, 2024
- OpenAI has unveiled GPT-4o, an upgraded version of its renowned GPT-4 model, offering improved speed and enhanced capabilities.
- NASA appoints David Salvagnini as its first Chief Artificial Intelligence Officer to lead AI efforts and ensure responsible use of AI technology.
- A Senate working group recommends a $32 billion annual investment in AI programs to maintain the U.S.'s competitive edge.
- Ilya Sutskever, cofounder and chief scientist of OpenAI, announces his departure from the company, to be succeeded by Jakub Pachocki.
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Macrumors
5h
120
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iOS 18 to Use AI to Summarize Notifications, Add to Calendar, and More
- Apple is set to introduce an AI-powered auto-summarization feature for notifications and other AI features in iOS 18.
- The upcoming iOS update will include improved Siri voice capabilities and proactive intelligence to assist users in their day-to-day lives.
- The AI-based features will include auto-summarizing notifications, transcribing voice memos, enhancing calendar auto-population and app suggestions.
- Apple executives have acknowledged being behind in the AI game and will mainly rely on on-device processing for the AI features.
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Medium
5h
99
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Advancing Privacy in Machine Learning: Google’s Novel Approach to Generating Synthetic Data
- Google AI researchers have unveiled groundbreaking methods for generating differentially private synthetic data, revolutionizing privacy-preserving machine learning practices.
- The approach involves the creation of synthetic data that mirrors the essential characteristics of the original dataset but remains entirely artificial, protecting user privacy without compromising data utility.
- The methodology utilizes techniques such as LoRa (Low-Rank Adaptation) and prompt fine-tuning to refine a smaller subset of parameters during the private training phase, minimizing computational overhead while potentially enhancing the quality of synthetic data.
- Experimental results demonstrate that classifiers trained on synthetic data generated via the proposed techniques outperform alternative methods and rival classifiers trained directly on original sensitive data, marking a significant advancement in privacy-preserving machine learning.
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6 Likes
Medium
5h
265
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The Decentralized Finance (DeFi) Ecosystem: Key Components Explained
- Blockchain technology serves as the foundational layer for the DeFi ecosystem, enabling decentralized and transparent transaction recording.
- Smart contracts are self-executing programs that enforce agreements without intermediaries, powering DeFi applications.
- Decentralized exchanges (DEXs) allow peer-to-peer cryptocurrency trading, providing transparency and user control.
- Lending and borrowing protocols enable users to lend or borrow cryptocurrencies, often requiring collateral.
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15 Likes
Medium
6h
121
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Optimizing Graph Neural Network inputs: Are we choosing the right graphs
- Detailed discussions on choosing graph schema for GNNs are scarce, leading to underperformance on non-homogeneous or directed graphs. Vanilla GNNs work best for homogeneous and undirected graphs with high homophily. GNNs can struggle to comprehend causal relationships on undirected graphs. Edge directionality significantly enhances homophily and performance in real-world citation and social networks. Directional edges can capture causality in product recommendation systems, resulting in better performance. Bipartite graph structures may be more effective for modeling abuse (anomaly) in e-commerce domain. Key considerations for graph schema include directionality, homophily, and temporal dynamics. Researchers can choose appropriate schema by critically assessing datasets and considering these design points.
- GraphSAGE, GAT, and GIN aggregate information from local neighborhoods to generate root node representation using a homophily principle. Such Vanilla GNNs work best for datasets with single node type and undirected edges.
- Vanilla GNNs may not perform well for graphs with low homophily. Heterophilic anomalies in graphs pose significant challenges to GNN performance. Incorporating edge directionality can render heterophilic graphs more homophilic and accurate.
- Vanilla GNNs on undirected graphs may struggle to comprehend causality, leading to suboptimal product recommendations. Directed edges can explicitly model causality, leading to significate improvement in recommendation quality.
- Bipartite graph structure can capture structural properties and temporal signals effectively at scale, unlike homogenous graphs used for graph anomaly detection.
- Choosing the appropriate graph schema is paramount for enhanced GNN performance. Key design considerations include directionality, homophily, and temporal dynamics.
- By systematically assessing these design points, researchers can make informed decisions to choose the appropriate graph schema suited for their dataset for effective GNN utilization.
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Medium
6h
80
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Mission Cloud Accelerates Generative AI Adoption with AWS Partnership Expansion
- Mission Cloud Services, Inc. expands its partnership with AWS in generative AI.
- Mission Cloud aims to assist businesses in exploring and integrating generative AI solutions.
- The collaboration allows Mission Cloud to expedite the delivery of generative AI solutions to U.S. companies.
- Mission Cloud's expertise in AWS AI services empowers customers to embrace generative AI revolution.
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Tech Story
6h
271
AI vs. Jobs: Can Universal Basic Income be a Solution?
- AI technology is changing many things in our lives, raising concerns about job displacement.
- Geoffrey Hinton suggests Universal Basic Income (UBI) as a solution to mitigate AI's impact on the job market.
- UBI would provide regular payments to all adults, helping those who lose their jobs due to AI and reducing poverty.
- The debate on AI's impact revolves around its potential to increase inequality, while proper measures like UBI could ensure equitable distribution of wealth.
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Medium
6h
205
Using Castelnuovo-Mumford regularity in Machine Learning research part4
- Binomial edge ideals JG and their algebraic invariants have been of interest in relation to combinatorial invariants of graphs.
- A new invariant ν(G) is introduced, which is the size of a nice induced maximal matching of inlex(JG).
- ν(G) is shown to be the maximal total length of induced paths within G and satisfies ν(G) ≤ reg(JG) - 1.
- The equality ν(G) = reg(JG) - 1 holds for closed and bipartite graphs, providing new characterizations of previous results.
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Dev
6h
20
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Cracking GPT Assistants: Extracting Prompts and Associated Files
- Recent studies and practical demonstrations have revealed a vulnerability in GPT assistants, allowing hackers to retrieve prompts and associated files.
- The vulnerability allows malicious actors to easily hack GPT assistants and retrieve the prompts and associated files of these systems.
- The malicious prompt and command can be used to retrieve the assistant's prompt and file list.
- The retrieved files include the README and other external files used by the assistant.
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