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Articles by Ori Abramovsky
When ChatGPT’s Web Search Fails: Lessons for Data-Driven Decision Making
Last Updated on January 14, 2025 by Originally published on Towards AI. ChatGPT recently gained a game-changing capability: web search. This new feature allows the model to incorporate up-to-date data from the web into its responses, unlocking an incredible range of possibilities. Tasks like validating leads, conducting market analysis, or investigating recent events have become dramatically easier.
The AI Productivity Paradox: Why Aren't More Workers Using ChatGPT?
Despite the transformative potential of tools like ChatGPT, most knowledge workers I’ve spoken to don’t use it at all. Those who do primarily stick to basic tasks like summarization. Only a little over 5% of ChatGPT’s user base pays for plus – a small fraction of potential professional users – suggesting a scarcity of power users leveraging AI for complex, high-value work.
Simplifying LLM Development: Treat It Like Regular ML
Last Updated on September 2, 2024 by Originally published on Towards AI. Simplifying LLM Development: Treat It Like Regular ML Large Language Models (LLMs) are the latest buzz, often seen as both exciting and intimidating. Many data scientists I’ve spoken with agree that LLMs represent the future, yet they often feel that these models are too complex and detached from the everyday challenges faced in enterprise environments.
Simplifying LLM Development: Treat It Like Regular ML
Last Updated on September 2, 2024 by Originally published on Towards AI. Large Language Models (LLMs) are the latest buzz, often seen as both exciting and intimidating. Many data scientists I’ve spoken with agree that LLMs represent the future, yet they often feel that these models are too complex and detached from the everyday challenges faced in enterprise environments.
PyPI Inundated by Malicious Typosquatting Campaign
Highlights: PiPI is one of the largest Indexes, with more than 800,000 users Check Point CloudGuard identified a typosquatting campaign on PyPI, comprising over 500 malicious packages. Installation of these packages exposed users to potential theft of their personally identifiable information (PII) and the installation of malware on their systems. Upon detection, we promptly notified PyPI about these packages, leading to their swift removal by the PyPI administrative team.
Classifying Source Code using LLMs - What and How Original
Sharing our experience at making LLM-based Source Code classifier Source Code AI has become a common use case with many practical and varied implementations (defects detection, code completion and much more).
CDN Service Exposes Users to Malicious Packages for Phishing Attacks Invisible to Security Tools
Malicious package previously removed are still accessible through jsdelivr and results in phishing attack Highlights: CloudGuard Spectral detected a malicious package on NPM that applied a phishing attack to gain its user’s credentials To do so, the package relied on a file from a malicious package, already detected and removed from NPM, which was still available through a popular NPM CDN service – ‘jsdelivr’ Once detected, we’ve alerted NPM and jsdelivr of the malicious package and the...
The inherent Train-Inference mismatch of SourceCodeAI
Source code AI has many unique features which differentiate it from the more general NLP applications (like the common practice to heavily process the input prior to feeding it to the model).
SourceCodeAI - Deep Learning for Source Code - Why and How
Photo by Enric Cruz López from Pexels Source code AI recently became a popular topic; more than ever before companies are spending efforts in that direction, trying to leverage it for their needs.
Question Pivoting -Handling Unachievable AI tasks
Photo by Gratisography from Pexels This is a common script; the Data Science team is asked to generate an insight which is supposed to be quite simple but due to reasons like lack of data is not feasible. A common example would be a request to predict customers’ state (happy with the product? Willing to recommend VS planning churn, etc..) only to find that none relevant labelled data exists (like customers feedback records or churn requests). What to do next?
How to utilise a manual labelling workforce
Photo by Littlehampton Bricks from Pexels In order to generate machine learning models we need first to find datasets to train it on. For manycases we can rely on public datasets (using datasets search indexes like Google or Kaggle to find the data we need). But some cases require generating the datasets on our own (due to the data scale, privacy concerns or just since the use case is a niche), and if the ML task is supervised then we also need to take care of labelling the dataset.
How to generate code dataset for machine learning applications
Photo by daniel wildman from FreeImages Code AI recently became a common topic; whether it is to classify a code topic, find vulnerabilities within it, summarise it or even guess the next token given the previous ones. What enables it is the recent NLP advances and the availability boost of rich and varied source code datasets. Having services like Github, Gitlab and Bitbucket, generating source code datasets may be seen like a trivial task — ‘crawl Github and start training the model’.
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