... includes all of the major AI methods for (a) representing knowledge about a task or a problem area, and (b) reasoning about a problem.
An academic and a lawyer have teamed up to develop a robot lawyer, which, if successful, will make legal advice affordable to people from all backgrounds, while revolutionising the legal sector. Robots could take on significant parts of a lawyer's work, reducing the costs and barriers to access to legal services for everyone, rather than just those who can afford the high costs. The project, at the University of Bradford, is initially working on a machine learning-based application to provide immigration-related legal advice, but if successful, it could be replicated across the legal sector. The idea has received government backing in the form of a £170,000 grant from Innovate UK Knowledge Transfer Partnerships. Legal firm AY&J Solicitors is providing a further £70,000 as well as the vital knowledge of lawyers.
Artificial intelligence is becoming good at many "human" jobs--diagnosing disease, translating languages, providing customer service--and it's improving fast. This is raising reasonable fears that AI will ultimately replace human workers throughout the economy. Never before have digital tools been so responsive to us, nor we to our tools. While AI will radically alter how work gets done and who does it, the technology's larger impact will be in complementing and augmenting human capabilities, not replacing them. Certainly, many companies have used AI to automate processes, but those that deploy it mainly to displace employees will see only short-term productivity gains. In our research involving 1,500 companies, we found that firms achieve the most significant performance improvements when humans and machines work together. Through such collaborative intelligence, humans and AI actively enhance each other's complementary strengths: the leadership, teamwork, creativity, and social skills of the former, and the speed, scalability, and quantitative capabilities of the latter. What comes naturally to people (making a joke, for example) can be tricky for machines, and what's straightforward for machines (analyzing gigabytes of data) remains virtually impossible for humans.
Ontology encompasses problems about the most general properties and relations of the entities which do exist. Ontology is the way we can connect entities and understand their relationships, their types & tokens. With ontology one can enable such a description, but first we need to formally specify components such as individuals (tokens, instances of objects), classes (types), attributes (properties) and relations (limitations & restrictions, rules & axioms). Formal ontology gives precise mathematical formulations of the properties and relations of certain entities. Such theories usually propose axioms about these entities in question, represented as mathematical models or in some formal language based on some system of formal logic.
I trained a classifier on images of animals and gave it an image of myself, it's 98% confident I'm a dog. This is an exploration of a possible Bayesian fix. I trained a multi-class classifier on images of cats, dogs and wild animals and passed an image of myself, it's 98% confident I'm a dog. The problem isn't that I passed an inappropriate image because models in the real world are passed all sorts of garbage. It's that the model is overconfident about an image far away from the training data.
Description Here's a basic definition of machine learning: "Algorithms that parse data, learn from that data, and then apply what they've learned to make informed decisions" An easy example of a machine learning algorithm is an on-demand music streaming service. For the service to make a decision about which new songs or artists to recommend to a listener, machine learning algorithms associate the listener's preferences with other listeners who have a similar musical taste. This technique, which is often simply touted as AI, is used in many services that offer automated recommendations. Machine learning fuels all sorts of automated tasks that span across multiple industries, from data security firms that hunt down malware to finance professionals who want alerts for favorable trades. The AI algorithms are programmed to constantly be learning in a way that simulates as a virtual personal assistant--something that they do quite well.
Hyperparameter tuning is one of the fundamental steps in the machine learning routine. Also known as hyperparameter optimisation, the method entails searching for the best configuration of hyperparameters to enable optimal performance. Machine learning algorithms require user-defined inputs to achieve a balance between accuracy and generalisability. This process is known as hyperparameter tuning. There are various tools and approaches available to tune hyperparameters.
AI, ML, and NLP are making it far more feasible to automate many data analytics processes. It hasn't taken long for smart technologies such as Google Home and Amazon Alexa to become embedded in everyday life. In the process, millions of us have become accustomed to the idea of holding something approaching a natural conversation with a machine. Natural language processing (NLP) is one of the key enablers of this voice-controlled revolution. Going forward, we can expect NLP to play a similarly central role in transforming the way we interact with data analytics tools.
AI has become a game-changer tool in the IT sector. Artificial intelligence and automation have significantly transformed how organizations run their production lines. As AI tools can garner real-time insights, it has facilitated the companies' design and product innovation techniques. When applied correctly, AI and automation can help develop better, faster, and cheaper business techniques. Automation tools can be deployed to automate repetitive tasks, allowing the IT staff to focus on strategic tasks instead of administrative work.
When you download a dating app, fill out a profile with some of your most private information, and select "allow app to access location" to locate nearby potential love interests, you may feel a little exposed, but you proceed anyway, in order to find those dates. But there is reason to believe that by using these sites, you may be unknowingly submitting to government tracking--and we can't know for sure because of all of the secrecy involved with deals that data brokers make with government agencies. It's yet another demonstration of the need to bring transparency to the data-collection industry. Dating apps ask users for a variety of highly personal information and retain it indefinitely, potentially forever. This can include photos and videos, text conversations with other users, and information on gender, sexual orientation, political affiliation, religion, desire to have children, location, HIV status, and beyond.
In this post, I will show you how easy it is to use other state-of-the-art algorithms with PyCaret thanks to tune-sklearn, a drop-in replacement for scikit-learn's model selection module with cutting edge hyperparameter tuning techniques. I'll also report results from a series of benchmarks, showing how tune-sklearn is able to easily improve classification model performance. Hyperparameter optimization algorithms can vary greatly in efficiency. Random search has been a machine learning staple and for a good reason: it's easy to implement, understand and gives good results in reasonable time. However, as the name implies, it is completely random -- a lot of time can be spent on evaluating bad configurations.