Search Immortality Topics:

Page 49«..1020..48495051..6070..»


Category Archives: Machine Learning

Embedded AI and Machine Learning Adding New Advancements In Tech Space – Analytics Insight

Throughout the most recent years, as sensor and MCU costs dove and shipped volumes have gone through the roof, an ever-increasing number of organizations have attempted to exploit by adding sensor-driven embedded AI to their products.

Automotive is driving the trend the average non-autonomous vehicle presently has 100 sensors, sending information to 30-50 microcontrollers that run about 1m lines of code and create 1TB of data per vehicle every day. Extravagance vehicles may have twice the same number of, and autonomous vehicles increase the sensor check significantly more drastically.

Yet, its not simply an automotive trend. Industrial equipment is turning out to be progressively brilliant as creators of rotating, reciprocating and other types of equipment rush to add usefulness for condition monitoring and predictive support, and a huge number of new consumer products from toothbrushes, to vacuum cleaners, to fitness monitors add instrumentation and smarts.

An ever-increasing number of smart devices are being introduced each month. We are now at a point where artificial intelligence and machine learning in its exceptionally essential structure has discovered its way into the core of embedded devices. For example, smart home lighting systems that automatically turn on and off depend on whether anybody is available in the room. By all accounts, the system doesnt look excessively stylish. Yet, when you consider everything, you understand that the system is really settling on choices all alone. In view of the contribution from the sensor, the microcontroller/SOC concludes if to turn on the light or not.

To do all of this simultaneously, defeating variety to achieve troublesome detections in real-time, at the edge, inside the vital limitations isnt at all simple. In any case, with current tools, integrating new options for machine learning for signals (like Reality AI) it is getting simpler.

They can regularly achieve detections that escape traditional engineering models. They do this by making significantly more productive and compelling utilization of data to conquer variation. Where traditional engineering approaches will ordinarily be founded on a physical model, utilizing data to appraise parameters, machine learning approaches can adapt autonomously of those models. They figure out how to recognize signatures straightforwardly from the raw information and utilize the mechanics of machine learning (mathematics) to isolate targets from non-targets without depending on physical science.

There are a lot of different regions where the convergence of machine learning and embedded systems will prompt great opportunities. Healthcare, for example, is now receiving the rewards of putting resources into AI technology. The Internet of Things or IoT will likewise profit enormously from the introduction of artificial intelligence. We will have smart automation solutions that will prompt energy savings, cost proficiency as well as the end of human blunder.

Forecasting is at the center of so many ML/AI conversations as organizations hope to use neural networks and deep learning to conjecture time series data. The worth is the capacity to ingest information and quickly acknowledge insight into how it changes the long-term outlook. Further, a large part of the circumstance relies upon the global supply chain, which makes improvements significantly harder to precisely project.

Probably the most unsafe positions in production lines are as of now being dealt by machines. Because of the advancement in embedded electronics and industrial automation, we have ground-breaking microcontrollers running the whole mechanical production systems in assembling plants. However, the majority of these machines are not exactly completely automatic and still require a type of human intercession. In any case, the time will come when the introduction of machine learning will help engineers concoct truly intelligent machines that can work with zero human mediation.

Share This ArticleDo the sharing thingy

About AuthorMore info about author

Read more:
Embedded AI and Machine Learning Adding New Advancements In Tech Space - Analytics Insight

Posted in Machine Learning | Comments Off on Embedded AI and Machine Learning Adding New Advancements In Tech Space – Analytics Insight

AI: This COVID machine-learning tool helps swamped hospitals pick the right treatment – ZDNet

Spain has been one the European states worst hit by the COVID-19 pandemic, with more than 1.7 million detected cases. Despite the second wave of infections that has hit the country over the past few months, the Hospital Clinic in Barcelona has succeeded in halving mortality among its coronavirus patients using artificial intelligence.

The Catalan hospital has developed a machine-learning tool that can predict when a COVID patient will deteriorate and how to customize that individual's treatment to avoid the worst outcome.

"When you have a sole patient who's in a critical state, you can take special care of them. But when they are 700 of them, you need this kind of tool," says Carol Garcia-Vidal, a physician specialized in infectious diseases and IDIBAPS researcher who has led the development of the tool.

SEE: Managing AI and ML in the enterprise 2020: Tech leaders increase project development and implementation (TechRepublic Premium)

Before the pandemic, the hospital had already been working on software to turn variable data into an analyzable form. So when the hospital started to receive COVID patients in March, it put the system to work analyzing three trillion pieces of structured and anonymized data from 2,000 patients.

The goal was to train it to recognize patterns and check what treatments were the most effective for each patient and when they should be administered.

That work underlined to Garcia-Vidal and her team that the virus doesn't manifest itself in the same way for everyone. "There are patients with an inflammatory response, patients with coagulopathies and patients who develop super infections," Garca-Vidal tells ZDNet. Each group needs different drugs and thus a personalized treatment.

Thanks to an EIT Health grant, the AI system has been developed into a real-time dashboard display on physicians' computers that has become one of their everyday tools. Under the supervision of an epidemiologist, the tool enables patients to be classified and offered a more personalized treatment.

"Nobody has done this before," says Garca-Vidal, who says the researchers recently added two more patterns to the system to include the patients who are stable and can leave the hospital, thus freeing a bed, and those patients who are more likely to die. The predictions are 90% accurate.

"It's very useful for physicians with less experience and those who have a specialty that's nothing to do with COVID, such as gynecologists or traumatologists," she says. As in many countries, doctors from all specialist areas were called in to treat patients during the first wave of the pandemic.

The system is also being used during the current second wave because, according to Garca-Vidal, the number of patients in intensive care in Catalan hospitals has jumped. The plan is to make the tool available to other hospitals.

Meanwhile, the Barcelona Supercomputing Center (BSC) is also analyzing a set of data corresponding to 3,000 medical cases generated by the Hospital Clnic during the acute phase of the pandemic in March.

The aim is to develop a model based on deep-learning neural networks that will look for common patterns and generate predictions on the evolution of symptoms. The objective is to know whether a patient is likely to need a ventilator system or be directly sent to intensive care.

SEE: The algorithms are watching us, but who is watching the algorithms?

Some data such as age, sex, vital signs and medication given is structured but other data isn't, because it consists of text written in natural language in the form of, for example, hospital discharge and radiology reports, BSC researcher Marta Villegas explains.

Supercomputing brings the computational capacity and power to extract essential information from these reports and train models based on neural networks to predict the evolution of the disease as well as the response to treatments given the previous conditions of the patients.

This approach, based on natural language processing, is also being tested at a hospital in Madrid.

Go here to see the original:
AI: This COVID machine-learning tool helps swamped hospitals pick the right treatment - ZDNet

Posted in Machine Learning | Comments Off on AI: This COVID machine-learning tool helps swamped hospitals pick the right treatment – ZDNet

Hateful Memes Challenge Winners Machine Learning Times – The Predictive Analytics Times

By: Douwe Kiela, Hamed Firooz and Tony Nelli Originally published in Facebook AI, Dec 11, 2020.

AI has made progress in detecting hate speech, but important and difficult technical challenges remain. Back in May 2020, Facebook AI partnered with Getty Images and DrivenData to launch the Hateful Memes Challenge, a first-of-its-kind $100K competition and data set to accelerate research on the problem of detecting hate speech that combines images and text. As part of the challenge, Facebook AI created a unique data set of 10,000+ new multimodal examples, using licensed images from Getty Images so that researchers could easily use them in their work.

More than 3,300 participants from around the world entered the Hateful Memes Challenge, and we are now sharing details on the winning entries. The top-performing teams were:

Ron Zhu link to code

Niklas Muennighoff link to code

Team HateDetectron: Riza Velioglu and Jewgeni Rose link to code

Team Kingsterdam: Phillip Lippe, Nithin Holla, Shantanu Chandra, Santhosh Rajamanickam, Georgios Antoniou, Ekaterina Shutova and Helen Yannakoudakis link to code

Vlad Sandulescu link to code

You can see the full leaderboard here. As part of the NeurIPS 2020 competition track, the top five winners will discuss their solutions and we facilitated a Q&A with participants from around the world. Each of these five implementations has been made open source and is available now.

To continue reading this article, click here.

View post:
Hateful Memes Challenge Winners Machine Learning Times - The Predictive Analytics Times

Posted in Machine Learning | Comments Off on Hateful Memes Challenge Winners Machine Learning Times – The Predictive Analytics Times

Google’s Blob Opera combines machine learning with animated operatics – Newstalk ZB

With school out for the year and many taking their summer break, many families will be looking for something fun to do over the next few weeks.

Google's latest machine-learning game may be one way to pass the time, thanks to Blob Opera.

Four actual opera singers Christian Joel (tenor), Frederick Tong (bass), Joanna Gamble (mezzosoprano), and Olivia Doutney (soprano) recorded 16 hours of singing and their voices were used to train amachine learning model to create an algorithm for whatopera sounds like mathematically.

The algorithm was then combined with for very cute blob characters which represent the different opera voice typesand you can move them around to make them sing different notes. The algorithm then does it's magic and calculates how the other 3 blobs should sing to perfectly harmonise with your blob allowing you to compose opera of your own without having to sing a note!

Michelle Dickinson joined Francesca Rudkin to explain what this means.

LISTEN ABOVE

View post:
Google's Blob Opera combines machine learning with animated operatics - Newstalk ZB

Posted in Machine Learning | Comments Off on Google’s Blob Opera combines machine learning with animated operatics – Newstalk ZB

Machine learning in human resources: how it works & its real-world applications – iTMunch

According to research conducted by Glassdoor, on average, the entire interview process conducted by companies in the United Stated usually takes about 22.9 days and the same in Germany, France and the UK takes 4-9 days longer [1]. Another research by the Society for Human Resources that studied data from more than 275,000 members in 160 countries found that the average time taken to fill a position is 42 days [2]. Clearly, hiring is a time-consuming and tedious process. Groundbreaking technologies like cloud computing, big data, augmented reality, virtual reality, blockchain technology and the Internet of Things can play a key role in making this process move faster. Machine learning in human resources is one such technology that has made the recruitment process not just faster but more effective.

Machine learning (ML) is treated as a subset of artificial intelligence (AI). AI is a branch of computer science which deals with building smart machines that are capable of performing certain tasks that typically require human intelligence. Machine learning, by definition, is the study of algorithms that enhance itself automatically over time with more data and experience. It is the science of getting machines (computers) to learn how to think and act like humans. To improve the learnings of a machine learning algorithm, data is fed into it over time in the form of observations and real-world interactions.The algorithms of ML are built on models based on sample or training data to make predictions and decisions without being explicitly programmed to do so.

Machine learning in itself is not a new technology but its integration with the HR function of organizations has been gradual and only recently started to have an impact. In this blog, we talk about how machine learning has contributed in making HR processes easier, how it works and what are its real-world applications. Let us begin by learning about this concept in brief.

The HR departments responsibilities with regards to recruitment used to be gathering and screening resumes, reaching out to candidates that fit the job description, lining up interviews and sending offer letters. It also includes managing a new employees on-boarding process and taking care of the exit process of an employee that decides to leave. Today, the human resource department is about all of this and much more. The department is now also expected to be able to predict employee attrition and candidate success, and this is possible through AI and machine learning in HR.

The objective behind integrating machine learning in human resource processes is the identification and automation of repetitive, time consuming tasks to free up the HR staff. By automating these processes, they can devote more time and resources to other imperative strategic projects and actual human interactions with prospective employees. ML is capable of efficiently handling the following HR roles, tasks and functions:

SEE ALSO:The Role of AI and Machine Learning in Affiliate Marketing

An HR professional keeps track of who saw the job posting and the job portal on which the applicant saw the posting. They collect the CVs and resumes of all the applicants and come up with a way to categorize the data in those documents. Additionally, they schedule, standardize and streamline the entire interview process. Moreover, they keep track of the social media activities of applicants along with other relevant data. All of this data collected by the HR professional is fed into a machine learning HR software from the first day itself. Soon enough, HR analytics in machine learning begins analyzing the data fed to discover and display insights and patterns.

The opportunities of learning through insights provided by machine learning HR are endless. The software helps HR professionals discover things like which interviewer is better at identifying the right candidate and which job portal or job posting attracts more or quality applicants.

With HR analytics and machine learning, fine-tuning and personalization of training is possible which makes the training experience more relevant to the freshly hired employee. It helps in identifying knowledge gaps or loopholes in training early on. It can also become a useful resource for company-related FAQs and information like company policies, code of conduct, benefits and conflict resolution.

The best way to better understand how machine learning has made HR processes more efficient is by getting acquainted with the real world applications of this technology. Let us have a look at some applications below.

SEE ALSO:The Importance of Human Resources Analytics

Scheduling is generally a time-demanding task. It includes coordinating with candidates and scheduling interviews, enhancing the onboarding experience, calling the candidates for follow-ups, performance reviews, training, testing and answering the common HR queries. Automating these tedious processes is one of the first applications of machine learning in human resource. ML takes away the burden of these cumbersome tasks from the HR staff by streamlining and automating it which frees up their time to focus on bigger issues at hand.A few of the best recruitment scheduling software are Beamery, Yello and Avature.

Once an HR professional is informed about the kind of talent that is needed to be hired in a company, one challenge is letting this information out and attracting the right set of candidates that might be fit for the role. Huge amount of companies trust ML for this task. Renowned job search platforms like LinkedIn and Glassdoor use machine learning and intelligent algorithms to help HR professionals filter and find out the best suitable candidates for the job.

Machine learning in human resources is also used to track new and potential applicants as they come into the system. A study was conducted by Capterra to look at how the use of recruitment software or applicant tracking software helped recruiters. It found 75% of the recruiters they contacted used some form of recruitment or applicant tracking software with 94% agreeing that it improved their hiring process. It further found that just 5% of recruiters thought that using applicant tracking software had a negative impact on their company [3].

Using such software also gives the HR professional access to predictive analytics which helps them analyze if the person would be best suitable for the job and a good fit for the company. Some of the best applicant tracking software that are available in the market are Pinpoint, Greenhouse and ClearCompany.

If hiring an employee is difficult, retaining an employee is even more challenging. There are factors in a company that make an employee stay or move to their next job. A study which was conducted by Gallup asked employees from different organizations if theyd leave or stay if certain perks were provided to them. The study found that 37% would quit their present job and take up a new job thatll allow them to work remotely part-time. 54% would switch for monetary bonuses, 51% for flexible working hours and 51% for employers offering retirement plans with pensions [4]. Though employee retention depends on various factors, it is imperative for an HR professional to understand, manage and predict employee attrition.

Machine learning HR tools provide valuable data and insights into the above mentioned factors and help HR professionals make decisions regarding employing someone (or not) more efficiently. By understanding this data about employee turnover, they are in a better position to take corrective measures well in advance to eliminate or minimize the issues.

An engaged employee is one who is involved in, committed to and enthusiastic about their work and workplace. The State of the Global Workforce report by Gallop found that 85% of the employees in the workplace are disengaged. Translation: Majority of the workforce views their workplace negatively or only does the bare minimum to get through the day, with little to no attachment to their work or workplace. The study further addresses why employee engagement is necessary. It found that offices with more engaged employees result in 10% higher customer metrics, 17% higher productivity, 20% more sales and 21% more profitability. Moreover, it found that highly engaged workplaces saw 41% less absenteeism [5].

Machine learning HR software helps the human resource department in making the employees more engaged. The insights provided by HR analytics by machine learning software help the HR team significantly in increasing employee productivity and reducing employee turnover rates. Software from Workometry and Glint aids immeasurable in measuring, analyzing and reporting on employee engagement and the general feeling towards their work.

The applications of machine learning in human resources we read above are already in use by HR professionals across the globe. Though the human element from human resources wont completely disappear, machine learning can guide and assist HR professionals substantially in ensuring the various functions of this department are well aligned and the strategic decisions made on a day-to-day basis are more accurate.

These are definitely exciting times for the HR industry and it is crucial that those working in this department are aware of the existing cutting-edge solutions available and the new trends that continue to develop.

The automation of HR functions like hiring & recruitment, training, development and retention has already made a profound positive effect on companies. Companies that refuse to or are slow to adapt and adopt machine learning and other new technologies will find themselves at a competitive disadvantage while those embrace them happily will flourish.

SEE ALSO:Future of Human Resource Management: HR Tech Trends of 2019

For more updates and latest tech news, keep reading iTMunch

Sources

[1] Glassdoor (2015) Why is Hiring Taking Longer, New Insights from Glassdoor Data [Online] Available from: https://www.glassdoor.com/research/app/uploads/sites/2/2015/06/GD_Report_3-2.pdf [Accessed December 2020]

[2] [Society for Human Resource Management (2016) 2016 Human Capital Benchmarking Report [Online] Available from: https://www.ebiinc.com/wp-content/uploads/attachments/2016-Human-Capital-Report.pdf [Accessed December 2020]

[3] Capterra (2015) Recruiting Software Impact Report [Online] Available from: https://www.capterra.com/recruiting-software/impact-of-recruiting-software-on-businesses [Accessed December 2020]

[4] Gallup (2017) State of the American Workplace Report [Online] Available from: https://www.gallup.com/workplace/238085/state-american-workplace-report-2017.aspx [Accessed December 2020]

[5] Gallup (2017) State of the Global Workplace [Online] Available from: https://www.gallup.com/workplace/238079/state-global-workplace-2017.aspx#formheader [Accessed December 2020]

Image Courtesy

Image 1: Background vector created by starline http://www.freepik.com

Image 2: Business photo created by yanalya http://www.freepik.com

Read this article:
Machine learning in human resources: how it works & its real-world applications - iTMunch

Posted in Machine Learning | Comments Off on Machine learning in human resources: how it works & its real-world applications – iTMunch

Supporting Content Decision Makers With Machine Learning Machine Learning Times – The Predictive Analytics Times

By: Melody Dye, Chaitanya Ekanadham, Avneesh Saluja, Ashish RastogiOriginally published in The Netflix Tech Blog, Dec 10, 2020.

Netflix is pioneering content creation at an unprecedented scale. Our catalog of thousands of films and series caters to 195M+ members in over 190 countries who span a broad and diverse range of tastes. Content, marketing, and studio production executives make the key decisions that aspire to maximize each series or films potential to bring joy to our subscribers as it progresses from pitch to play on our service. Our job is to support them.

The commissioning of a series or film, which we refer to as a title, is a creative decision. Executives consider many factors including narrative quality, relation to the current societal context or zeitgeist, creative talent relationships, and audience composition and size, to name a few. The stakes are high (content is expensive!) as is the uncertainty of the outcome (it is difficult to predict which shows or films will become hits). To mitigate this uncertainty, executives throughout the entertainment industry have always consulted historical data to help characterize the potential audience of a title using comparable titles, if they exist. Two key questions in this endeavor are:

The increasing vastness and diversity of what our members are watching make answering these questions particularly challenging using conventional methods, which draw on a limited set of comparable titles and their respective performance metrics (e.g., box office, Nielsen ratings). This challenge is also an opportunity. In this post we explore how machine learning and statistical modeling can aid creative decision makers in tackling these questions at a global scale. The key advantage of these techniques is twofold. First, they draw on a much wider range of historical titles (spanning global as well as niche audiences). Second, they leverage each historical title more effectively by isolating the components (e.g., thematic elements) that are relevant for the title in question.

To continue reading this article, click here.

Read more:
Supporting Content Decision Makers With Machine Learning Machine Learning Times - The Predictive Analytics Times

Posted in Machine Learning | Comments Off on Supporting Content Decision Makers With Machine Learning Machine Learning Times – The Predictive Analytics Times