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Risks in AI
AI in self-driving cars brings many good things. It makes roads safer, traffic smoother, and helps people who can't drive. These cars can be better for the earth too. They use electric power and drive in ways that cut down on pollution. They can save money on travel and create new jobs in tech and sRead more
AI in self-driving cars brings many good things. It makes roads safer, traffic smoother, and helps people who can’t drive. These cars can be better for the earth too. They use electric power and drive in ways that cut down on pollution. They can save money on travel and create new jobs in tech and services. But there are also risks when we put AI in cars. Things can go wrong with the tech, and hackers might try to break in. This can put people in danger. There are also tricky questions about who’s to blame if something goes wrong. It’s hard to make laws for this new tech. Many people who drive for a living might lose their jobs. Also, these cars collect a lot of info about people, which is worrying. It costs a lot to make these cars and the roads they need. Getting people to trust this new tech is key. To make AI cars work well, we need to test them a lot, make good rules, and talk to the public. We have to think about the good and bad sides to bring this big change into our lives the right way.
See lessHow can cyber security impact on society
Cybersecurity has a deep impact on society. It keeps personal data safe, builds confidence in online transactions, and shields national security and vital infrastructure. It's key to protecting people's privacy and stopping identity theft. This ensures that sensitive info like financial details andRead more
Cybersecurity has a deep impact on society. It keeps personal data safe, builds confidence in online transactions, and shields national security and vital infrastructure. It’s key to protecting people’s privacy and stopping identity theft. This ensures that sensitive info like financial details and health records stays safe from cyber threats. What’s more, cybersecurity forms the foundation of trust in online transactions and digital communication platforms. This supports the growth of e-commerce and allows for smooth global interactions.
Looking at the bigger picture, cybersecurity has a crucial role to protect national security interests, including defense systems and key infrastructure such as power grids and hospitals. Cyber attacks on these areas can interrupt vital services, put public safety at risk, and hurt government operations. In the world economy strong cybersecurity measures are needed to keep economic stability, encourage new ideas, and support efforts to go digital.
In the end good cybersecurity practices are needed to protect people, companies, and governments from the growing range of cyber threats. By making digital systems stronger and more trustworthy, cybersecurity helps to keep trust, safety, and success in today’s connected world.
See less“How are organizations responding to the rise of AI-powered cyberattacks?
Companies are taking action to fight AI-powered cyber attacks with a wide-ranging plan for online safety. They now use AI-based defense systems that can spot and react to complex threats as they happen. These systems apply machine learning to study huge sets of data finding patterns and odd things tRead more
Companies are taking action to fight AI-powered cyber attacks with a wide-ranging plan for online safety. They now use AI-based defense systems that can spot and react to complex threats as they happen. These systems apply machine learning to study huge sets of data finding patterns and odd things that might show harmful acts. They’re also working on better ways to gather info about threats using AI to collect and study data more . This helps them guess and stop possible cyber threats before they happen.
AI-powered methods to study behavior and find strange events are key in watching how users and networks act. These methods can spot changes that might mean someone got in without permission or wants to cause harm. Also, companies are using safety measures that change on their own based on new kinds of threats. This makes sure their defenses stay strong against cyber threats that keep changing.
The cybersecurity community stresses working together to share insights, threat info, and best practices. This teamwork helps boost everyone’s defense efforts. People also focus on ethics when developing AI to make sure it’s see-through, responsible, and follows legal and ethical rules.
When companies add AI tech to their cybersecurity plans, they want to get tougher against AI-powered cyberattacks. This helps protect important assets and keep trust in our more and more digital world.
See lessmachine learning
Automated language translation is an example of how machine learning is poised to revolutionize our day-to-day lives. Yet, current systems have yet to deliver on the promise of error-free translation of idiomatic expressions or more nuanced phrasing, and do not easily allow for incorporation of domaRead more
Automated language translation is an example of how machine learning is poised to revolutionize our day-to-day lives. Yet, current systems have yet to deliver on the promise of error-free translation of idiomatic expressions or more nuanced phrasing, and do not easily allow for incorporation of domain specific terminology. Machine learning approaches, and in particular neural networks with sequence-to-sequence architectures and attention mechanisms, are posed to bring about a radical shift in the status quo.
See lessTo achieve this end, it is important to have a very rich dataset of text in many languages so as to be able to train the models. The model learns how to code input sentences in numbers that represent semantic meaning and context, and later on decode them in the target languages. Attention mechanisms make the model more capable in focusing on relevant parts of sentences thus enhancing its accuracy and context retention.
Training requires tuning a wide variety of model parameters and evaluating performance on non-trivial metrics for translation quality. After building a translation model, many hours of engineering are still needed to construct a translation system that will automatically translate sentences between human languages with high performance over a wide range of topics and sentence types.11-13 However, once built, such a translation system would be able to massively and quickly produce high-quality translations from one language to any other language without needing any other form of help or data, such as parallel texts (e.g., machine-translated government documents), bilingual dictionaries (e.g., Wik tionary), comparable corpora (e.g., search query logs), information about the world (e.g., Wikipedia), or even monolingual text in either the source or target languages.
Machine Learning
One of the distinctions between supervised and unsupervised learning in AI is the further use of such models in legal language machine development. Labeled data are used when training models based on input examples rather than on estimated values as performed by unsupervised approaches. This methodRead more
One of the distinctions between supervised and unsupervised learning in AI is the further use of such models in legal language machine development. Labeled data are used when training models based on input examples rather than on estimated values as performed by unsupervised approaches. This method is particularly important with respect to tasks encountered in the legal domain, such as – document classification where the legal texts are classified to types eg contracts or court opinions, and named entity recognition who identifies entities like names and dates within documents. As effective as it can be; supervised has its own setback since it requires a huge amount of labeled data which also may take much time and money in case of legal contexts.
See lessUnsupervised learning differs from supervised learning in that it does not rely on labeled data. It instead focuses on finding patterns or representations in the data. In legal language modeling, unsupervised learning can be used for tasks like discovering “topics,” or underlying themes within a set of legal documents, as well as for “clustering,” or grouping together related documents based on similarities in their content. Unsupervised learning is particularly interesting within legal applications due to the relatively low availability of labeled instances (e.g., when compared to labeled reading materials available for training a comprehension test), but it can also be difficult to accurately interpret the results of unsupervised methods without a bit more explicit guidance from labeled examples.
Both of these approaches are important to the advancement of legal language models—supervised learning for tasks that need labeled data at the level of precision and recall we can currently achieve, and unsupervised learning to facilitate exploration and discovery in the vast space of unannotated legal text corpora.