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) …
Thank you for joining us on "The cloud hub: From cloud chaos to clarity." Contact centers hold significant value for businesses, but they often have to deal with a disengaged workforce and unsatisfied customers. New AI systems can help contact centers become future-ready with a smarter workforce, happier customers, and stronger finances.
Thank you for joining us on “The cloud hub: From cloud chaos to clarity.” The transformative potential of algorithmic systems, the reach of their effects, combined with the paucity of supervision, can bring certain reputational, financial, and ethical risks. Responsible AI is required to provide assurance to users and build continuous trust in AI-based systems.…
Artificial intelligence has a wide range of uses in businesses, including streamlining job processes and aggregating business data. We will show you exactly how to succeed these applications, through Real World Business case studies. And for each of these applications we will build a separate AI to solve the challenge. In Part 1 - Optimizing Processes, we will build an AI that will optimize the flows in an E-Commerce warehouse. In Part 2 - Minimizing Costs, we will build a more advanced AI that will minimize the costs in energy consumption of a data center by more than 50%!
Broadcom Inc. has announced expanding opportunities for organizations to gain greater value from the company's advanced AI, security, and hybrid cloud solutions with "Day One" support for IBM's new z16. Broadcom's suite of software solutions, services, and unique "beyond code" programs provide clients an advantage to succeed in an increasingly challenging business environment. "Our strategic investments position clients to exploit the z16 along with advances in AI, cybersecurity, cloud integration, and agility," said Greg Lotko, senior VP and GM, Mainframe Software Division, Broadcom. What distinguishes Broadcom is our deep investment in technology and how we work side-by-side in partnership with our clients to overcome their unique challenges and create new opportunities." As a member of the z16 Early Ship Program, Broadcom collaborated with IBM to ensure clients can capitalize on the full range of our mainframe software solutions on the new platform to drive progress toward their innovation and business goals. "Nothing can match the transaction performance of a mainframe, and the way that we manage the platform using Broadcom technology is a real differentiator for us," said Johan Bosch, executive director for iOCO Infrastructure Services. "We can deliver our services at 25 percent of the cost when measured against standalone banking environments.
Machine learning and other artificial intelligence (AI) methods have had immense success with scientific and technical tasks such as predicting how protein molecules fold and recognising faces in a crowd. However, the application of these methods to the humanities is yet to be fully explored. What can AI tell us about philosophy and religion, for example? As a starting point for such an exploration, we used deep learning AI methods to analyse English translations of the Bhagavad Gita, an ancient Hindu text written originally in Sanskrit. Using a deep learning-based language model called BERT, we studied sentiment (emotions) and semantics (meanings) in the translations.
The models can have many hyperparameters and finding the best combination of the parameter using grid search methods. Grid search is a technique for tuning hyperparameter that may facilitate build a model and evaluate a model for every combination of algorithms parameters per grid. We might use 10 fold cross-validation to search the best value for that tuning hyperparameter. These values are called hyperparameters. To get the simplest set of hyperparameters we will use the Grid Search method.
Researchers at Memorial Sloan Kettering Cancer Center (MSK) have developed a sensor that can be trained to sniff for cancer, with the help of artificial intelligence. Although the training doesn't work the same way one trains a police dog to sniff for explosives or drugs, the sensor has some similarity to how the nose works. The nose can detect more than a trillion different scents, even though it has just a few hundred types of olfactory receptors. The pattern of which odor molecules bind to which receptors creates a kind of molecular signature that the brain uses to recognize a scent. Like the nose, the cancer detection technology uses an array of multiple sensors to detect a molecular signature of the disease.
Federal agencies are the latest to alert companies to potential bias in AI recruiting tools. As the AP notes, the Justice Department and Equal Employment Opportunity Commission (EEOC) have warned employers that AI hiring and productivity systems can violate the Americans with Disabilities Act. These technologies might discriminate against people with disabilities by unfairly ruling out job candidates, applying incorrect performance monitoring, asking for illegal sensitive info or limiting pay raises and promotions. Accordingly, the government bodies have released documents (DOJ, EEOC) outlining the ADA's requirements and offering help to improve the fairness of workplace AI systems. Businesses should ensure their AI allows for reasonable accommodations.They should also consider how any of their automated tools might affect people with various disabilities.
The Transformer soon became the most popular model in NLP after its debut in the famous article Attention Is All You Need in 2017. The capacity to analyze text in a non-sequential manner (as opposed to RNNs) enabled large models to be trained. The introduction of an attention mechanism proved tremendously valuable in generalizing text. Before the advent of Deep Learning, previous approaches to NLP were more rule-based, with simpler (pure statistical) machine learning algorithms being taught the words and phrases to look for in the text, and particular replies being created when these phrases were discovered. Following the publication of the study, numerous popular transformers emerged, the most well-known of which is GPT (Generative Pre-trained Transformer).