"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.
If you are more than 30 years old then you must have witnessed how the scripts of Hollywood movies have been transformed into a reality by some of the best brains on the planet. There were many technology-related concepts that were considered as the hype and only as a part of the fiction but when you will look around yourself then you will witness those ideas and concepts used in the Hollywood movies turning into reality. From the ability to talk to anyone through videos sitting in any corner of the world to reaching Mars, there have been many technological advancements achieved by humans and that is just amazing. Now, when you will look around yourself and start reading some of the technological blogs then you will realize that there is more coming. What you have witnessed till now is only a glimpse of what we are going to achieve in a couple of years.
A lot has taken place in the world since I published my article titled "Artificial intelligence for when times are a-changin" in December 2019. There, I introduced you to machine learning (ML) as a subset of artificial intelligence (AI). By explaining in simple terms how a machine learning model works, I hoped to demystify this somehow scary-at-first new technology. Like electricity, which was once considered a magic trick and it is now assumed, AI technologies are for us all to use and benefit from – not just those working specifically in software development. As much as I get excited about any piece of new tech I can get my hands on, a key indicator that a particular technology is successful is not that it excites early adopters but that it becomes essential, helpful, and seamlessly integrated into our very human lives. Our industry's greatest challenges are well known by all of us.
Splunk's Data-to-Everything Platform is an all-encompassing suite of analytics tools that help enterprises to search, correlate, analyze, monitor and report on data in real time, available through its Splunk Cloud and Splunk Enterprise products. Today's slew of updates at the virtual event are all about expanding customer's multicloud capabilities, giving them new ways to set the right data strategy and improve access to the information their businesses generate, Splunk said. For example, the Splunk Data Stream Processor, an event streaming platform, is being updated with new capabilities that enable it to access, process and route real-time data from multiple cloud services, including Google LLC's Cloud Platform and Microsoft Corp.'s Azure Event Hub. In addition, event data now gets enriched with lookups and machine learning functionality that helps to minimize compute loads and provide more accuracy when searching through this data. Moreover, the Data-to-Everything Platform is getting a new Splunk Machine Learning Environment that will make it easy for companies to build and operationalize machine learning models by bringing data from multiple sources into a single platform.
This article is part of "Deconstructing artificial intelligence," a series of posts that explore the details of how AI applications work. One of the things that caught my eye at Nvidia's flagship event, the GPU Technology Conference (GTC), was Maxine, a platform that leverages artificial intelligence to improve the quality and experience of video-conferencing applications in real-time. Maxine used deep learning for resolution improvement, background noise reduction, video compression, face alignment, and real-time translation and transcription. In this post, which marks the first installation of our "deconstructing artificial intelligence" series, we will take a look at how some of these features work and how they tie-in with AI research done at Nvidia. We'll also explore the pending issues and the possible business model for Nvidia's AI-powered video-conferencing platform.
If you were worried about AI replacing and outperforming humans, well here's one more reason to be so, and probably the biggest one. To continue this revolution, here is GPT-3. GPT-3 is simply a neural-network-powered language model. "A language model is a model that predicts the likelihood of a sentence existing in the world" said daleonai. For example, a language model can label the sentence "I want to drink the water" to more likely exist than the sentence "I want to drink the pizza."
As per WHO one in four people in the world will be affected by mental or neurological disorder at some point in their lives. Around 450 million people currently suffer from such conditions, placing mental disorders among the leading causes of ill health and disability worldwide. Mood Disorders – These disorders are also called affective disorders which involves persistent feeling of sadness or period of feeling overly happy , or fluctuations from extreme happiness to extreme sadness. The most common mood disorders are depression , bipolar disorder and cyclothymic disorder. Another type of disorder is the Psychotic disorder – This involves distorted awareness and thinking. Two of the main common symptoms of psychotic disorders are hallucination, where the patient experiences images or sounds that are not real. Delusions- These are false fixed beliefs that the patient accepts as true, despite the evidence to the contrary . Schizophrenia is an example of psychotic disorder. These disorders cause detachment from reality. The question today is how technology advancement in the field of Artificial Intelligence can help in the diagnosis of the mental disorders. Therefore it is important to understand what Artificial Intelligence is ? Artificial Intelligence is a software program which can think and act like human . Basically we are designing programs which acts like our brain but with a higher level of computing power. The Artificial Intelligent program have multiple tools and subsets which have different functions, but they combine together to create an Artificial Intelligent program. One of the important subset of AI is Machine Learning – Machine Learning are algorithms that learn complex patterns from data and make predictions from it. Machine learning programs have the following steps:- It takes data to train the system. This data can be in the form of structured or unstructured data . The data can be extracted from the data base. It can be in the form of text, it can be in the form of images. After processing this data , the algorithm understands and learns the pattern shown by this vast data . It can classify the data that it has not seen before. Machine learning is trained by the features or the traits of the subjects. In case of patients who suffer from a mental health issue, this data can be in the form of text data that a patient may write on social media site, the spoken data , language and data captured through spoken media and then converted to text through the use of Natural Language processing. Artificial intelligent program can be used to detect the Depression , we take an example of a research paper where the researchers accessed the Facebook status which was posted by 683 patients who visited a large urban academic emergency department, 114 of whom had a diagnosis of depression in their medical record. The research was undertaken to detect and predict the diagnosis of the depression problem from the language used in the Facebook posts. Prediction performances of future diagnosis of depression in the EMR based on demographics and Facebook posting activity, reported as cross-validated out-of-sample AUCs. With the Facebook data in hand and using the ML model, researchers could identify the depressed patients with a fair degree of accuracy at AUC=0.69, approximately matching the accuracy of screening surveys bench marked against medical records. They found that the language predictor of depression include emotional(sadness), interpersonal(loneliness, hostility) and cognitive(preoccupation with self, rumination) process. From the result , it was also observed that the user who ultimately had a diagnosis of depression used more first person singular pronouns( I , My , me)suggesting a preoccupation with self. The results show that the Facebook language based prediction model performs similarly to screening surveys in identifying the patients with depression when using diagnostic codes in the EMR to identify diagnosis of depression. Growth of social media and the continuous improvement of machine learning algorithm suggest that social media based screening methods for depression may become increasingly feasible and more accurate. The present analysis therefore also suggests that the social media based prediction of future depression status may be possible as early as 3 months before the first documentation of depression in the medical record. Novel avenues are also becoming available to detect depression. These methods also include algorithmic analysis of phone sensors , GPS position on the phone, facial expression in images and videos shared on social platforms. The predictive model of Logistic regression was used. Ten language topics most positively associated with a future depression diagnosis controlling for demographics (*P < 0.05, **P < 0.01, and ***P < 0.001; BHP < 0.05 after Benjamini–Hochberg correction for multiple comparisons). As per WHO close to 800 000 commit suicide every year. Some of the companies are also involved in building healthcare applications. Ginger is a chat application that is used by the employers that provide direct counselling to its employees. The algorithm analyses the words someone uses and then relies on the training from more than 2 billion behavioural data samples , 45 million chat messages and 2 million clinical assessments to provide a recommendation. The CompanionMX system has an app that allows patients being treated with depression, bipolar disorders, and other conditions to create an audio log where they can talk about how they are feeling. The AI system analyses the recording as well as looks for changes in behaviour for proactive mental health monitoring. Bark, a parental control phone tracker app, monitors major messaging and social media platforms to look for signs of cyber bullying, depression, suicidal thoughts and sexting on a child’s phone. Advantages of Artificial Intelligence in Healthcare Support Mental Health professionals – AI can act as a support for the health professionals in doing their jobs. Algorithms can analyse data much faster than humans can suggest possible treatments, monitor a patient’s progress and alert the human professional to any concern. 24/7 access- Due to lack of human mental health professionals, it can take months to take an appointment. AI provides a tool that an individual can access without waiting for an appointment. Not expensive – The cost of care prohibits some individuals from seeking help. This is more affordable. Comfort talking to a bot- It is easier to disclose an information to a bot than to a human. Cognitive computers will analyse a patient’s speech or written words to look for tell-tale indicators found in language, including meaning, syntax and intonation. Combining the results of these measurements with those from wearable devices and imaging systems (MRIs and EEGs) can paint a more complete picture of the individual for health professionals to better identify, understand and treat the underlying disease, be it Parkinson’s, Alzheimer’s, Huntington’s disease, PTSD or even underdevelopment conditions such as autism and ADHD. In a study with Columbia University psychiatrists, were able to predict, with 100 percent accuracy, who among a population of at-risk adolescents would develop their first episode of psychosis within two years. In other research with our Pfizer colleagues, we’re using only about 1 minute of speech from Parkinson’s patients to better track, predict and monitor the disease. We’re already seeing results of nearly 80 percent accuracy. In five years, we hope to advance the study of using words as windows into our mental health. IBM is building an automated speech analysis application that runs off a mobile device. By taking approximately one minute of speech input, the system uses text-to-speech, advanced analytics, machine learning, natural language processing technologies and computational biology to provide a real-time, overview of the patient’s mental health. Artificial Intelligence will play a pivotal role in creating ground-breaking tools to analyse and detect mental health problems and will play a substantially positive role in increasing the treatment coverage by early diagnosis and possibly be able to reduce the death rates due to mental health problems. REFERENCE Eichstaedt, Johannes C., et al. “Facebook Language Predicts Depression in Medical Records.” PNAS, National Academy of Sciences, 30 Oct. 2018, www.pnas.org/content/115/44/11203. Marr, Bernard. “The Incredible Ways Artificial Intelligence Is Now Used In Mental Health.” Forbes, Forbes Magazine, 22 May 2019, www.forbes.com/sites/bernardmarr/2019/05/03/the-incredible-ways-artificial-intelligence-is-now-used-in-mental-health/#74cf5137d02e. Cecchi, Guillermo. “With AI, Our Words Will Be a Window into Our Mental Health.” With AI, Our Words Will Be a Window into Our Mental Health- IBM Research, www.research.ibm.com/5-in-5/mental-health/. IBM Research Editorial Staff. “IBM 5 in 5: With AI, Our Words Will Be a Window into Our Mental Health.” IBM Research Blog, 5 Jan. 2017, www.ibm.com/blogs/research/2017/1/ibm-5-in-5-our-words-will-be-the-windows-to-our-mental-health/. Bedi, Gillinder, et al. “Automated Analysis of Free Speech Predicts Psychosis Onset in High-Risk Youths.” Nature News, Nature Publishing Group, 26 Aug. 2015, www.nature.com/articles/npjschz201530. WHO. “Mental Disorders Affect One in Four People.” World Health Organization, World Health Organization, 4 Oct. 2001, www.who.int/whr/2001/media_centre/press_release/. Goldberg, Joseph. “Mental Health: Types of Mental Illness.” WebMD, WebMD, 6 Apr. 2019, www.webmd.com/mental-health/mental-health-types-illness#1.
Nearly a decade ago, back in 2011, when I had just completed the 4th semester of my Computer Science & Engineering Degree course, I found that even though I had fared well in all my exams, my practical knowledge in this field was (in the words of Lord Kelvin -- the famous mathematical physicist)"of a meager and unsatisfactory kind". This was due to the fact that, aside from a handful of really great courses, the majority of my coursework relied on rote-learning and the competitive pursuit of grades instead of practical knowledge. I had joined the field of Computer Science to satisfy my childhood dream of working with computers, but I found I was still far from my dream of understanding and creating software with my computer. In this dismal state, I spent the beginning of my semester-break searching for a motivation in the online universe. After Googling for a short while, I stumbled upon Mehran Sahami's CS106A video lectures on Stanford's Youtube channel, and thus began my tryst with online education.
Historians and nostalgic residents alike take an interest in how cities were constructed and how they developed -- and now there's a tool for that. Google AI recently launched the open-source browser-based toolset "rǝ," which was created to enable the exploration of city transitions from 1800 to 2000 virtually in a three-dimensional view. Google AI says the name rǝ is pronounced as "re-turn" and derives its meaning from "reconstruction, research, recreation and remembering." This scalable system runs on Google Cloud and Kubernetes and reconstructs cities from historical maps and photos. There are three main components to the toolset. Warper is a crowdsourcing platform,where users can upload photos of historical print maps and georectify them to match real world coordinates.
Artificial intelligence is changing the world of image editing and manipulation, and Adobe doesn't want to be left behind. Today, the company is releasing an update to Photoshop version 22.0 that comes with a host of AI-powered features, some new, some already shared with the public. These include a sky replacement tool, improved AI edge selection, and -- the star of the show -- a suite of image-editing tools that Adobe calls "neural filters." These filters include a number of simple overlays and effects but also tools that allow for deeper edits, particularly to portraits. With neural filters, Photoshop can adjust a subject's age and facial expression, amplifying or reducing feelings like "joy," "surprise," or "anger" with simple sliders.