What is NLP? Natural language processing explained
When you click on a search result, the system interprets it as confirmation that the results it has found are correct and uses this information to improve search results in the future. Last Thursday (Feb. 14), the nonprofit research firm OpenAI released a new language model capable of generating convincing passages of prose. So convincing, in fact, that the researchers have refrained from open-sourcing the code, in hopes of stalling its potential weaponization as a means of mass-producing fake news.
Social media threat intelligence
NLP leverages machine learning (ML) algorithms trained on unstructured data, typically text, to analyze how elements of human language are structured together to impart meaning. Phrases, sentences, and sometimes entire books are fed into ML engines where they’re processed using grammatical rules, people’s real-life linguistic habits, and the like. An NLP algorithm uses this data to find patterns and extrapolate what comes next.
Improved accuracy in threat detection
It’s time to take a leap and integrate the technology into an organization’s digital security toolbox. Data quality is fundamental for successful NLP implementation in cybersecurity. Even the most advanced algorithms can produce inaccurate or misleading results if the information is flawed. Thus, ensuring the input is clean, consistent and reliable is crucial.
What is NLP? Natural language processing explained
- Closing that gap would probably require a new way of thinking, he adds, as well as much more time.
- This speed enables quicker decision-making and faster deployment of countermeasures.
- Natural language processing (NLP) is the branch of artificial intelligence (AI) that deals with training computers to understand, process, and generate language.
- It’s now possible to run useful models from the safety and comfort of your own computer.
“Our system is similar to how the human brain processes language,” says Hanrui Wang. “We read very fast and just focus on key words. That’s the idea with SpAtten.” Begin with introductory sessions that cover the basics of NLP and its applications in cybersecurity. Gradually move to hands-on training, where team members can interact with and see the NLP tools. Social media is more than just for sharing memes and vacation photos — it’s also a hotbed for potential cybersecurity threats. Perpetrators often discuss tactics, share malware or claim responsibility for attacks on these platforms.
To test their approach, the team used a common metric for assessing predictions made by machine-learning models that scores accuracy on a scale between 0.5 (no better than chance) and 1 (perfect). In this case, they took the top mutations identified by the tool and, using real viruses in a lab, checked how many of them were actual escape mutations. Their results ranged from 0.69 for HIV to 0.85 for one coronavirus strain. This is better than results from other state-of-the-art models, they say.
- Some people believe chatbots like ChatGPT can provide an affordable alternative to in-person psychedelic-assisted therapy.
- The researchers also integrated SpAtten into their previous work, to help validate their philosophy that hardware and software are best designed in tandem.
- It’s where NLP becomes incredibly useful in gathering threat intelligence.
- The algorithms provide an edge in data analysis and threat detection by turning vague indicators into actionable insights.
The researchers developed a system called SpAtten to run the attention mechanism more efficiently. Their design encompasses both specialized software and hardware. One key software advance is SpAtten’s use of “cascade pruning,” or eliminating unnecessary data from the calculations. Once the attention mechanism helps pick a sentence’s key words (called tokens), SpAtten prunes away unimportant tokens and eliminates the corresponding computations and data movements.
They integrate with Slack, Microsoft Messenger, and other chat programs where they read the language you use, then turn on when you type in a trigger phrase. Voice assistants such as Siri and Alexa also kick into gear when they hear phrases like “Hey, Alexa.” That’s why critics say these programs are always listening; if they weren’t, they’d never know when you need them. Unless you turn an app on manually, NLP programs must operate in the background, waiting for that phrase. We’re starting to give AI agents real autonomy, and we’re not prepared for what could happen next.
Researchers are watching advances in NLP and thinking up new analogies between language and biology to take advantage of them. But Bryson, Berger and Hie believe that this crossover could go both ways, with new NLP algorithms inspired by concepts in biology. Treating genetic mutations as changes in meaning could be applied in different ways across biology. Knowing what mutations might be coming could make it easier for hospitals and public health authorities to plan ahead. For example, asking the model to tell you how much a flu strain has changed its meaning since last year would give you a sense of how well the antibodies that people have already developed are going to work this year.
Beyond Inventory: Why ‘Actionability’ is the New Frontier in Cybersecurity
“We can improve the battery life for mobile phone or IoT devices,” says Wang, referring to internet-connected “things” — televisions, smart speakers, and the like. “That’s especially important because in the future, numerous IoT devices will interact with humans by voice and natural language, so NLP will be the first application we want to employ.” NLP is a powerful tool, but a team only unlocks its full potential when they use it correctly.
