摘要： Since machine learning is a relatively new field, the limits of its application are constantly pushed outward. Virtual personal assistants were the stuff of dreams a few years ago, and now they’re seen in every other household. While some examples are conspicuous, here are some ways ML is changing our lives that you may not have thought of.
摘要： Automated machine learning, or AutoML, has generated plenty of excitement as a pathway to “democratizing data science,” and has also encountered its fair share of skepticism from data science’s gatekeepers. Complicating the conversation even further is that there is no standard definition of AutoML, which can make the debate incredibly difficult to follow, even for those well-versed.
摘要： The predictive prowess of machine learning is widely hailed as the summit of statistical Artificial Intelligence. Vaunted for its ability to enhance everything from customer service to operations, its numerous neural networks, multiple models, and deep learning deployments are considered an enterprise surety for profiting from data.
摘要： Artificial intelligence, machine learning, deep learning, neural networks. ML terms are often used synonymously, but their differences are important to understand.
摘要： Deep learning has yielded amazing advances in natural language processing. Tap into the latest innovations with Explosion, Huggingface, and John Snow Labs.
摘要： Analogies play a crucial role in commonsense reasoning. The ability to recognize analogies like “eye is to seeing what ear is to hearing,” sometimes referred to as analogical proportions, shape how humans structure knowledge and understand language. In a new study that looks at whether AI models can understand analogies, researchers at Cardiff University used benchmarks from education as well as more common datasets. They found that while off-the-shelf models can identify some analogies, they sometimes struggle with complex relationships, raising questions about to what extent models capture knowledge.
摘要： How are various organizations handling the accelerating transition of data to the cloud? What are the obstacles in data cleaning for analytics and the time constraints companies face when preparing data for analytics, AI and Machine Learning (ML) initiatives? Here is a look at some insights from a recent report by Trifacta that answer these questions.
摘要：From wild speculation that flying cars will become the norm to robots that will be able to tend to our every need, there is lots of buzz about how AI, Machine Learning, and Deep Learning will change our lives. However, at present, it seems like a far-fetched future.
