If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The technological advancements in the global Healthcare industry are hurtling at light speed. As the medical industry is undergoing immense changes, Healthcare OEMs look forward to the growing technological trends to improve all aspects of patient care. Today, Artificial Intelligence (AI) play significant roles in the evolution of the healthcare industry, so much that algorithms can now predict and detect the root cause of a certain disease, making an accurate and timely diagnosis. For example, AI can detect the underlying cause of cancer, which can eventually help pharmaceutical scientists develop new drugs accordingly. In one recent study, published by Healthcare IT News, "Google and medical partners including Northwestern University have unveiled a new AI-based tool that can create a better model of a patient's lung from the CT scan images. This 3-D image gives better predictions about the malignancy of tumors and incorporates learning from previous scans, enabling the AI to help clinicians in spotting lung cancer in earlier stages when it is vastly more treatable".
This tutorial is about image classification on the Zalando Clothing Store dataset using Monk Library. In this dataset, there are high-res, color, and diverse images of clothing and models. This is the best tool to use for competitions held in platforms like Kaggle, Codalab, HackerEarth, AiCrowd, etc. For other ways to install, visit Monk Library. This section is to give you a demo of this classifier before getting into further details.
A machine-learned AI system used to assess recidivism risks in Broward County, Fla., often gave higher risk scores to African Americans than to whites, even when the latter had criminal records. The popular sentence-completion facility in Google Mail was caught assuming that an "investor" must be a male. A celebrated natural language generator called GPT, with an uncanny ability to write polished-looking essays for any prompt, produced seemingly racist and sexist completions when given prompts about minorities.
Up to now, any robots brushing with the law were always running strictly according to their code. Fatal accidents and serious injuries usually only happened through human misadventure or improper use of safety systems and barriers. We’ve yet to truly test how our laws will cope with the arrival of more sophisticated automation technology — but that day isn’t very far away.
These days everyone needs their machines to talk, and the only way by which a computer can communicate is through Natural Language Processing (NLP). Take the case of Alexa, a conversational item by Amazon. An inquiry is passed to it by the mode of voice, and it can answer by a similar medium, i.e., voice. The market situation of NLP is quite promising. The buzz of NLP in the market is increasing in an aggressive way which is expected to reach the mark of $ 16 billion by 2021 with the compound growth rate of 16 % yearly.
Cutting-edge medical research is the talk of the town at the moment and this innovative discovery platform are growing their presents within the medical research field. AI and Machine Learning is at the full front of what they do and they're looking for an experienced (academic or commercial) Machine Learning Engineer to support their continued growth. You'll be working on architecting the AI powered GCP discovery platform, taking on big problems and doing lots of research! This vacancy will be closing application on 15th August 2020. If you have any questions or fancy a chat about the opportunity feel free to give George Bone a call or apply for the advert and George will be in contact.
Improvements in cloud technologies and processing power have provided a solid foundation for mainstream adoption of machine learning (ML). With the ability to analyze massive amounts of data to derive meaningful insights, ML can give business leaders new ways to innovate, create new revenue streams, improve operational efficiencies, and help all employees make faster, more informed decisions. In IDG's 2019 Digital Business Study, 78% of IT and business leaders said their organizations are considering or have already deployed machine learning technologies as part of their digital business strategy. "We've seen it day in and day out with customers we support, and organizations in general, that are benefiting by leveraging machine learning," says Sri Elaprolu, senior leader, Amazon Machine Learning Solutions Lab, a team of data scientists and machine learning experts that helps Amazon Web Services (AWS) customers successfully adopt ML. Amazon is a prime example of how ML can impact every area of the business.
Time series forecasting is something of a dark horse in the field of data science: It is one of the most applied data science techniques in business, used extensively in finance, in supply chain management and in production and inventory planning, and it has a well established theoretical grounding in statistics and dynamic systems theory. Yet it retains something of an outsider status compared to more recent and popular machine learning topics such as image recognition and natural language processing, and it gets little or no treatment at all in introductory courses to data science and machine learning. My original training is in neural networks and other machine learning methods, but I gravitated towards time series methods after my career led me to the role of demand forecasting specialist. In recent weeks, as part of my team's effort to expand beyond traditional time series forecasting capabilities and into a borader ML based approach to our business, I found myself having several discussions with experienced ML engineers, who were very good at ML in general, but didn't have much experience with times series methods. I realized from those discussions that there were several things specific to time series forecasting that the forecasting community takes for granted but are very surprising to other ML practioners and data scientists, especially when compared to the way standard ML problems are approached.