"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.
This Interesting Engineering piece highlights how even an AI built to find'helpful drugs', when tweaked just a little, can find things that are rather less helpful. Collaborations Pharmaceuticals carried out a simple experiment to see what would happen if the AI they had built was slightly altered to look for chemical weapons, rather than medical treatments. According to a paper they published in Nature Machine Intelligence journal, the answer was not particularly reassuring. When reprogrammed to find chemical weapons, the machine learning algorithm found 40,000 possible options in just six hours. These researchers had'spent decades using computers and A.I. to improve human health', yet they admitted, after the experiment, that they had been'naive in thinking about the potential misuse of trade'.
Technology is not showing signs of slowing down any time soon. As we move into cloud computing, big data, natural language processing and artificial intelligence, the employment sector is gearing up for a big boost in the number of opportunities. Organisations such as Google, Microsoft, Facebook and Apple are aggressively hiring people with expertise in these domains, which makes them highly lucrative. Artificial intelligence is particularly on the cusp of a breakthrough. Technologies such as machine learning, neural networks, genetic algorithms and deep learning are receiving a lot of spotlight.
Nowadays, Machine Learning and Deep Learning methods have become the state-of-the-art approach to solve data classification tasks. In order to use those methods, it is necessary to acquire and label a considerable amount of data; however, this is not straightforward in some fields, since data annotation is time consuming and might require expert knowledge. This challenge can be tackled by means of semi-supervised learning methods that take advantage of both labelled and unlabelled data. In this work, we present new semi-supervised learning methods based on techniques from Topological Data Analysis (TDA), a field that is gaining importance for analysing large amounts of data with high variety and dimensionality. In particular, we have created two semi-supervised learning methods following two different topological approaches.
To enhance clinical decision support capabilities for professional societies and healthcare institutions, Unbound developed Unbound Intelligence (UBI)‒exclusive artificial intelligence and machine learning tools to help clinicians keep up to date with current research, as well as discover and fill knowledge gaps. The first medical association to adopt Unbound Intelligence was the American Pediatric Surgical Association (APSA). In 2017 APSA selected Unbound's end-to-end digital publishing platform to develop and power its marquee digital resource, the APSA Pediatric Surgery Library (PSL). "We believe Unbound Intelligence can help transform medicine by delivering new, assistive technologies that empower healthcare providers to better serve their patients," says Bill Detmer, MD, CEO of Unbound Medicine, "We are delighted to partner with the visionary leadership at APSA to power a new era of clinical decision making." To learn more about how Unbound Intelligence can help your organization, contact Unbound Medicine.
In a recent study posted to Preprints with The Lancet*, researchers developed a machine learning approach to identify patients with long coronavirus disease (COVID). The post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are called long COVID. In the present study, researchers aimed to generate a robust clinical definition for long COVID using data related to long COVID patients. The team utilized data obtained from electronic health records that were integrated and harmonized in the secure N3C Data Enclave. This allowed the team to identify unique patterns and clinical characteristics among COVID-19-infected patients.
The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more. AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career.
Virtual assistants are wonderful at following your commands but absolutely terrible at giving life advice. Tidio editor Kazimierz Rajnerowicz spent over 30 hours asking half a dozen popular artificial intelligence (AI)-powered voice assistants and chatbots all kinds of questions and concluded that while virtual assistants are great at retrieving facts, they aren't advanced enough to hold a conversation. "AI today is pattern recognition," explained Liziana Carter, founder of conversational AI start-up Grow AI, to Lifewire in a conversation over email. "Expecting it to advise whether robbing a bank is right or wrong is expecting creative thinking from it, also known as AI General Intelligence, which we're far from right now." Rajnerowicz thought of the experiment in response to forecasts by Juniper Research that predicts the number of AI voice assistant devices in use will exceed the human population by 2024. "... a better approach may be to use that power to gain back time to spend on the things that make us unique as humans."
New software developed by Peter Mac and collaborators is helping patients diagnosed with acute lymphoblastic leukemia (ALL) to determine what subtype they have. ALL is the most common childhood cancer in the world, and also affects adults. "Thirty to forty percent of all childhood cancers are ALL, it's a major pediatric cancer problem," says Associate Professor Paul Ekert from Peter Mac and the Children's Cancer Institute, who was involved in this work. More than 300 people are diagnosed with the disease in Australia each year, and more than half of those are young children under the age of 15. Determining what subtype of ALL a patient has provides valuable information about their prognosis, and how they should best be treated.
As the metaverse industry is expected to be an $800 billion market by 2024, we continue to learn new ways this immersive, virtual environment might better enable us to connect with each other from anywhere in the world. This comes at a time when many are already participating in and benefitting from virtual activities that otherwise would not be possible due to constraints of distance, time or cost. In enabling new opportunities for virtual rather than in-person instruction, the metaverse has the power to transform access to education and the way we learn. The types of education that the metaverse can accommodate are varied, from school-based interactive learning and workplace training to professional accreditation. In so many ways, the metaverse is offering new chances for people to learn what they want by mitigating obstacles of accessibility.