"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
The primary purpose of Artificial Intelligence (AI) is to reduce manual labour by using a machine's ability to scan large amounts of data to detect underlying patterns and anomalies in order to save time and raise efficiency. However, AI algorithms are not immune to bias. As AI algorithms can have long-term impacts on an organisation's reputation and severe consequences for the public, it is important to ensure that they are not biased towards a particular subgroup within a population. In layman's terms, algorithmic bias within AI algorithms occurs when the outcome is a lack of fairness or a favouritism towards one group due to a specific categorical distinction, where the categories are ethnicity, age, gender, qualifications, disabilities, and geographic location. If this in-depth educational content is useful for you, subscribe to our AI research mailing list to be alerted when we release new material. AI Bias takes place when assumptions are made incorrectly about the dataset or the model output during the machine learning process, which subsequently leads to unfair results. Bias can occur during the design of the project or in the data collection process that produces output that unfairly represents the population. For example, a survey posted on Facebook asking about people's perceptions of the COVID-19 lockdown in Victoria finds that 90% of Victorians are afraid of travelling interstate and overseas due to the pandemic. This statement is flawed because it is based upon individuals that access social media (i.e., Facebook) only, could include users that are not located in Victoria, and may overrepresent a particular age group (i.e. To effectively identify AI Bias, we need to look for presence of bias across the AI Lifecycle shown in Figure 1.
Readers of this blog already know what loss functions are in AI but for people starting into the field let me define it again. The loss function is a mathematical equation that all the deep learning algorithm tries to minimize or optimize. As we all know that Deep learning takes an iterative process to learn things, in every step, it calculates some metric that tells it how close it is to the original label and based upon that it optimizes its parameters. So the metrics that we minimize or optimize are called loss functions. There are a lot of famous loss functions like Mean square error, categorical cross-entropy, Dice loss, and many more.
The forecasting tool assesses multiple patient-specific biological and clinical factors to predict the degree of response to immune checkpoint inhibitors and survival outcomes. It markedly outperforms individual biomarkers or other combinations of variables developed so far, according to findings published in Nature Biotechnology. With further validation, the tool may help oncologists better identify patients most likely to benefit from ICB. Discerning, prior to treatment, patients for whom ICB would be ineffective could reduce unnecessary expense and exposure to potential side effects. It could also indicate the need to pursue alternate treatment strategies, such as combination therapies. "It's important to know which treatment modalities patients are most suited for," said Dr. Chan, director of Cleveland Clinic's Center for Immunotherapy & Precision Immuno-Oncology.
The COVID-19 crisis forced businesses everywhere to fast track their digital transformation efforts. Faced with the stark choice of becoming a digital-first business, or having no business at all, companies that were previously behind the curve had to implement everything from remote working to entire digital storefronts in a matter of days. According to research by McKinsey, the digital initiatives unleashed in response to the pandemic leapfrogged seven years of progress in a matter of months as companies acted 20 to 25 times faster than they had believed was possible. In the process, this acceleration of digital during the crisis brought about a sea change in executive mindsets with regard to the role of technology in business. Fast forward to today, and corporate leaders are now investing in technology for competitive advantage, refocusing their entire business around cutting-edge technologies, and initiating a business culture where experimentation and innovation is actively encouraged.
Tristan covers human-centric artificial intelligence advances, quantum computing, STEM, Spiderman, physics, and space stuff. Pronouns: He/hi (show all) Tristan covers human-centric artificial intelligence advances, quantum computing, STEM, Spiderman, physics, and space stuff. The Holy Grail of AI research is called "general artificial intelligence," or GAI. A machine imbued with general intelligence would be capable of performing just about any task a typical adult human could. The opposite of general AI is narrow AI – the kind we have today.
Deep Learning and Computer Vision A-Z: OpenCV, SSD & GANs, Become a Wizard of all the latest Computer Vision tools that exist out there. Detect anything and create powerful apps. You've definitely heard of AI and Deep Learning. But when you ask yourself, what is my position with respect to this new industrial revolution, that might lead you to another fundamental question: am I a consumer or a creator? For most people nowadays, the answer would be, a consumer.
AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner What type of #AI generates something new from data it is fed? It might be the third wave of Artificial Human Intelligence, dubbed as Neuro-Symbolic AI using #DeepLearning to boost the Symbolic AI approach, and vice versa, by combining logic and learning to transcend both limitations. In terms of Deep Learning, some of the issues are as follows, #Machinelearning requires a massive amount of data to train neural networks, which is not easy to get every time. Selecting the right algorithm is crucial as the results may be biased and lead to a bad prediction. They lack the ability to generalize and are bound by their training data i.e. there is a lack of creativity and they are only efficient at what they already know.
Deep neural networks are machine learning systems that automatically learn a task if provided with necessary data. An artificial neural network (ANN) having numerous layers between the input and output layers is known as a deep neural network (DNN). Neural networks are made available in various shapes and sizes. However, they all include the same essential components: neurons, synapses, weights, biases, and functions. Recently, scientists have added a total of 301 validated exoplanets to the already existing exoplanet tally. The cluster of planets is the most recent addition to the 4,569 confirmed planets orbiting various faraway stars.
Interpreting a Machine learning model helps in not only understanding what is going inside the black box but also explaining the predictions of the model. Generally, Machine learning or Deep learning models are black boxes which means it is very difficult to interpret whatever is going in inside the model.