LinkedIn has been at the forefront of AI for years, and it employs it in a variety of ways that users may not be aware of.

LinkedIn is one of the most popular recruiting platforms available today. Recruiters from all around the world use LinkedIn every day to find and filter applicants for specific job openings.

LinkedIn is well-known for being one of the software behemoths that has pushed the limits of machine learning research and development. LinkedIn has been constantly exploring cutting-edge machine learning techniques in order to make artificial intelligence (AI) a first-class member of the LinkedIn experience, in contrast to cultivating one of the world’s wealthiest datasets.

Here are a few examples of how LinkedIn uses Artificial intelligence:

LinkedIn makes substantial use of data analytics and artificial intelligence to enhance the experiences of its users and customers. Members are exposed to it on a daily basis, whether they are receiving employment recommendations, suggestions for connecting with others, or reading helpful stuff in their feed. AI assists enterprise customers, such as recruiters, in finding new talent pools and receiving insightful data. AI systems have had a significant impact on both sides of the platforms, adding significant value.

According to a report, there was a 30% increase in job applications after the improvement of the personalization tool, which recommends jobs that members might be interested in. In general, job applications overall have grown more than 40% year-over-year, based on a variety of AI-driven optimizations that have been made to both sides of the member-recruiter ecosystem. The email responses to cold emails from recruiters have increased by 45% while at the same time cutting down on the notifications needed to send to the job seekers that have received such messages. Even better, the total number of continuing, two-way conversations started in LinkedIn Recruiter has doubled. Every message a recruiter sends is twice as likely to turn into a hire after the most powerful LinkedIn AI tools are implemented. AI has also improved article recommendations in the feed by 10-20% based on click-through rate. With all of these amazing statistics, the platform has reached 660 million users and engagement has increased 50% year over year. The value capture consists of increasing the number of users, specifically paid users and the number of enterprise subscriptions from companies.

LinkedIn also uses AI to help businesses overcome human biases that stifle equality. These AI features are included in LinkedIn’s Talent Insight product, which is geared at recruiters and focuses on diversity and inclusion.

LinkedIn can monitor what occurs in the recruitment process in terms of gender, providing firms with data and insights into how their job postings and InMail are functioning. In addition, the top search results in LinkedIn Recruiter will be re-ranked to be more representative.

Companies can observe the gender breakdown in each step of the hiring process according to LinkedIn’s reporting data on gender in the hiring process. They can also have a better understanding of the overall talent environment, such as how skill sets change over time and how to discover talent pools. Companies can also evaluate their gender breakdown to that of their counterparts in the sector to see how they might recruit from a more diverse pool.

AI also works in the background, protecting its members from hazardous information, routing connections to maintain a fast site performance experience, and ensuring that the notifications given to the users are informative but not obtrusive.

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Wikipedia employs AI to learn more about the issue it’s facing and consider possible solutions.

Wikipedia is a free online encyclopedia with open information that was built by a community of people known as Wikipedians. Anyone who has registered on the site can submit an article for publication; however, editing articles does not require registration. The name of the website is derived from the wiki, a server program that allows anyone to modify website content using their browser.

It has come to light that Wikipedia has decided to employ artificial intelligence to learn more about the issue it’s facing and consider possible solutions.

Here are some of the ways Wikipedia uses Artificial Intelligence:

1/ Wikipedia is an open-source online encyclopedia that anybody may contribute to. The non-profit website forever revolutionized the way we obtain information by crowdsourcing the building of an encyclopedia. It’s one of the top 10 most-visited websites on the web. However, it is not without problems. Anyone with the ability to edit Wikipedia can inadvertently add false information.

Computer scientist Aaron Halfaker describes how Wikipedia recently began implementing an AI system he designed to detect vandalism and bogus edits on articles using machine learning – it can identify common patterns in vandalous edits, such as a tendency for improper character spacing, according to a report for Wired.

On the one hand, this means less work for volunteers who monitor for suspicious changes, but Wikipedia expects the change will attract a flood of new editors.

It’s all about lowering the entry hurdle. Because Wikipedia relies on crowdsourcing for its content, it must establish rigorous guidelines for who can make modifications to key documents in order to prevent vandalism. On the other hand, it discourages many people who have excellent intentions and sound information.

Hopefully, more people will be devoted to authentic content by having smarter AI identification of flawed items and modifications. When the policing machines are clever enough, Wikipedia’s regulations for newbies could be relaxed a little. Some will, however, be concerned about whether machines will ever become intelligent enough to replace a major number of human editors.

2/ To combat the trolls, the Wikimedia Foundation teamed up with Jigsaw (the tech incubator previously known as Google Ideas) on a research project called Detox, which uses machine learning to identify comments that may be personal assaults. This project is part of Jigsaw’s open-source AI mission to tackle abuse on social media sites and web forums.

3/ According to Forbes, a Google Brain team trained software on how to summarize information from online pages and generate Wikipedia-style articles. Text summary turns out to be more challenging than most of us imagined. The efforts of Google Brain to get a machine to summarize text are slightly better than past attempts, but there is still work to be done before a machine can write with the cadence and flair that humans can. It turns out that we aren’t quite ready to have a machine generate Wikipedia entries automatically, but work is continuing to get us there.

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CTO & MD at AX Semantics, the SaaS-based, Natural Language Generation Platform that creates any content, in any language, at any scale.

2020 was a year most of us want to forget. The pandemic brought on economic, logistical and technological challenges on a massive global scale, leaving businesses scrambling to adapt. Amidst the upheaval, businesses turned to video conferencing platforms like Zoom and Google Meet to stay connected. Technologies like artificial intelligence (AI) and machine learning (ML) helped augment human efforts to take on everything from health to cybersecurity. Equally, businesses looked toward strategic execution and technology to remain agile among industry shifts and provide a greater return on investments. 

Businesses are now focused on what’s next and preparing for an economic surge in the latter half of 2021 once more people globally are vaccinated and the world returns to a more “normal” way of life. Here are four ways AI and ML will continue to shape multiple industries and integrate with other technologies to drive further innovation and change in the year ahead:

1. Increased Commercial Applications For “Federated ML”

“Federated ML” or a “cloud-in-a-pocket” approach will play a more prominent role. The applied principles and techniques employed with federated ML means data doesn’t need to be in the cloud anymore. Today’s devices can store more data than ever before and likely more than a user could ever produce. As a result, AI models that help to improve personalized services no longer need to be centralized on company servers, but can instead exist on the device itself.

Equally, the techniques used by federated ML ensure user data is kept on the device and not on a server, while still providing access to predictive AI modeling. Data isn’t shared in the same way either. The advanced ML models used in federated ML keep data in data owners’ hands, leading to greater privacy. This approach is a new take on data privacy and a growing megatrend. A good example of this is Siri running on your iPhone, but not sending all of your data to Apple’s servers.

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Federated ML and its principles are currently in use, but greater commercial applications in this area are on the horizon. The introduction of Apple’s M1 chip and the industry-leading neural engine, for example, was specifically designed for advanced ML processes. Federated ML will also provide increased use cases within the financial services sector in areas like loan risk prediction, while AI and ML applications will also advance many other industries.

2. Promising AI Applications Within The Health Sector 

The onset of Covid-19 was a catalyst for advancing technologies in pharma, medicine and the health sector, including a newly updated focus on nursing, patient care, remote patient monitoring and telehealth. 

The integration of AI technology promises use cases in terms of data aggregation, updating patients’ charts and analyzing tests and images to suggest possible diagnoses and more. The application of AI in health, in a supporting role, also frees up physicians’ workloads, allowing them to spend more time with patients and on actual patient care. Japan is already looking at augmenting their doctors with AI to combat their doctor shortage. 

AI technology is rapidly expanding into other healthcare areas, including early detection of diseases, treatment and research. The technology will only evolve in the year ahead and play a more prominent role, especially as the world continues to weather the effects of Covid-19.

3. Hyper-Personalization Within E-Commerce

It’s true many industries suffered under the weight of the pandemic. E-commerce, however, ballooned. Amazon saw over 5.2 billion visitors worldwide in June 2020 and even temporarily limited delivery to only essential items given an unprecedented flood of orders. Consumers will demand even more customized experiences in 2021, giving rise to hyper-personalization and greater customer experience within the e-commerce sector. 

“Algorithmic e-commerce” — or the smart, systemic digitization of business functions often handled manually — will usher in widespread adoption and utilization of AI and ML by enterprises in the e-commerce sector. For example, AI-powered natural language generation (NLG) content will produce an algorithmic e-commerce experience, where customers receive bespoke online shopping experiences through customized product and category descriptions that turn a product page into a personalized sales pitch. Ultimately, this burgeoning trend will lead to a market shift that delivers more value to consumers — where vendors take a more product-type approach to personalization and customer experience, versus a consulting-product approach, as they’ve done previously.

4. New AI And ML Innovations With NLG

Natural language from phonetics, understanding, processing and generation has seen significant advancements in the last few years. As a result, the combination of AI and ML technology with NLG is rapidly pushing the boundaries of what is possible. 

Large and small companies already utilize the technology across multiple sectors and industries. We’ve already seen consumer applications like Google phone calls and enterprise applications like business process automation based on unstructured data (i.e., text to voice). Facebook has also achieved impressive results in semi-supervised and self-supervised learning techniques utilizing AI and NLP.  

GPT-3, the brainchild of OpenAI, a San Francisco-based AI lab is the third in a series of autocomplete tools designed by the company that provides a “text-in-text-out” interface that provides automatic text completion. This area has gained commercial traction and we’ll see real-life innovations and advancing use cases in this area rapidly accelerate over the course of this year. Areas like CSS generation utilizing GPT-3 and applications similar to Google Smart Compose will gain ground in 2021.

IDC expects worldwide AI spending to reach $110 billion by 2024. Companies should review products and projects closely to capitalize on what AI and ML have to offer — and then laser focus on those that use existing first-party data instead of those that first require the setup and creation of large, complex big data sets and data crunching. 

AI and ML technologies are more than buzzwords or simple predictions: They offer businesses limitless possibilities to evolve use cases to improve productivity, expand their customer base, boost ROI and grow their bottom line.

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