In today’s competitive job market, having the right certifications can give you a significant edge over other candidates. The top 10 certifications in the world are those that are in high demand and offer a good return on investment. They can help you advance your career, earn a higher salary, and gain access to more job opportunities.

Here are the top 10 certifications in the world:

1. DevOps Certified Professional (DCP): A certification that validates an individual’s knowledge and skills in implementing DevOps practices and principles, including collaboration, automation, and continuous integration/continuous delivery (CI/CD).

2. DevSecOps Certified Professional (DSOCP): This certification focuses on integrating security practices and principles into the DevOps workflow. It covers areas such as vulnerability management, threat modeling, and secure coding.

3. Site Reliability Engineering Certified Professional (SRECP): SRECP certification validates an individual’s expertise in implementing the principles and practices of Site Reliability Engineering (SRE). SRE focuses on achieving reliability and operational stability of systems through a combination of software engineering and operations.

4. AIOps Certified Professional: This certification pertains to the implementation of Artificial Intelligence for IT Operations (AIOps). It encompasses using AI and machine learning techniques to automate and enhance various IT operations processes, such as monitoring, event correlation, and incident management.

5. DataOps Certified Professional: DataOps certification focuses on the principles and practices of DataOps, which involves streamlining and optimizing the entire data lifecycle, from data ingestion to analytics and insights. It emphasizes collaboration, automation, and data quality.

6. MLOps Certified Professional: MLOps certification covers the implementation of Machine Learning Operations (MLOps) practices, which aim to streamline and optimize the deployment and management of machine learning models within the software development lifecycle.

7. BizDevOps Certified Professional: BizDevOps certification combines business (biz), development (dev), and operations (ops) practices, emphasizing cross-functional collaboration and alignment. It validates an individual’s ability to integrate business requirements into the DevOps workflow.

8. CloudOps Certified Professional: This certification focuses on the implementation of cloud operations practices and principles. It covers topics such as cloud architecture, deployment, scalability, and cost optimization.

9. ITOps Certified Professional: ITOps certification validates expertise in traditional IT operations practices and principles. It encompasses areas such as IT infrastructure management, system administration, and incident response.

10. SysOps Certified Professional: SysOps certification focuses on system administration and operations within the context of cloud computing environments. It covers topics such as resource provisioning, monitoring, and troubleshooting in cloud-based systems.

In conclusion, certifications have become an essential aspect of advancing in one’s career. The article highlights the top 10 certifications that span various domains, greatly augmenting your professional opportunities. Whether your interests lie in DevOps, security, AI, data, or project management, there’s a certification that can distinguish you from others. Seize the opportunity and invest in your future by embarking on the pursuit of one of these certifications today!

To gain further insights into these certifications and initiate your path towards professional excellence, visit the DevOps School Certification Page. Discover the array of courses, resources, and prospects that these certifications provide.

The post World’s Top 10 IT Certifications in 2023 appeared first on Artificial Intelligence.

As part of new efforts toward accessibility, Google announced Project Euphoniaat I/O in May: An attempt to make speech recognition capable of understanding people with non-standard speaking voices or impediments. The company has just published a post and its paper explaining some of the AI work enabling the new capability.

The problem is simple to observe: The speaking voices of those with motor impairments, such as those produced by degenerative diseases like amyotrophic lateral sclerosis (ALS), simply are not understood by existing natural language processing systems.

You can see it in action in the following video of Google research scientist Dimitri Kanevsky, who himself has impaired speech, attempting to interact with one of the company’s own products (and eventually doing so with the help of related work Parrotron): The research team describes it as following:

It’s notable that they at least partly blame the training set. That’s one of those implicit biases we find in AI models that can lead to high error rates in other places, like facial recognition or even noticing that a person is present. While failing to include major groups like people with dark skin isn’t a mistake comparable in scale to building a system not inclusive of those with impacted speech, they can both be addressed by more inclusive source data.

For Google’s researchers, that meant collecting dozens of hours of spoken audio from people with ALS. As you might expect, each person is affected differently by their condition, so accommodating the effects of the disease is not the same process as accommodating, say, a merely uncommon accent.

A standard voice-recognition model was used as a baseline, then tweaked in a few experimental ways, training it on the new audio. This alone reduced word error rates drastically, and did so with relatively little change to the original model, meaning there’s less need for heavy computation when adjusting to a new voice.

The researchers found that the model, when it is still confused by a given phoneme (that’s an individual speech sound like an “e” or “f”), has two kinds of errors. First, there’s the fact that it doesn’t recognize the phoneme for what was intended, and thus is not recognizing the word. And second, the model has to guess which phoneme the speaker did intend, and might choose the wrong one in cases where two or more words sound roughly similar.

The second error in particular is one that can be handled intelligently. Perhaps you say, “I’m going back inside the house,” and the system fails to recognize the “b” in back and the “h” in house; it’s not equally likely that you intended to say “I’m going tack inside the mouse.” The AI system may be able to use what it knows of human language — and of your own voice or the context in which you’re speaking — to fill in the gaps intelligently.

The post Google details AI work behind Project Euphonia’s more inclusive speech recognition appeared first on Artificial Intelligence.

The chief of Amazon Web Services defended his company’s use of facial recognitiontechnology but said he believes it should be subject to government regulation.

Andy Jassy, the CEO of Amazon’s cloud computing services, speaking Monday at the Code Conference in Scottsdale, Arizona, acknowledged that concerns about technologies like its controversial Rekognition program are valid but maintained the technologies still have value.

“Just because technology could be misused doesn’t mean we should ban it and condemn it,” Jassy said, comparing it to how a knife could be used in a “surreptitious” manner. “All that said, I actually understand why people are worried about it. I think the government should regulate it.”

Amazon’s facial recognition software, dubbed Rekognition, has come under repeated criticism from privacy advocates in the past year. While facial recognition technology is commonly used for everyday tasks such as unlocking phones and tagging friends on social media, advances in artificial intelligence and the proliferation of cameras have made it increasingly easy to watch and track what individuals are doing.

Last year, the ACLU revealed that Amazon was selling its facial recognition technology, Rekognition, to law enforcement agencies in the US, including the Orlando Police Department. An ACLU test of Rekognition in July found that the system mistakenly confused 28 congressmen with known criminals.

Concerns about Rekognition also came up during public hearings before the New York City Council on Amazon’s failed proposal to build a new campus in the city, with council members repeatedly chastising the company for marketing Rekognition to US Immigration and Customs Enforcement.

Both Google and Microsoft have publicly said they won’t sell their facial recognition tech to law enforcement, instead asking for new laws to guide their use.

“There are lots of ways to get an investigation wrong,” Jassy said. “Just because you could get it wrong doesn’t mean you should use it.”

Jassy said there’s strong backing within the company to continue support for government agencies and it intends to do so.

“Any government department that’s following the law, we will serve,” he said.

The post AWS chief says facial recognition should be regulated appeared first on Artificial Intelligence.

Want to schedule an appointment? Just ask your phone. Need to turn on your bedroom lights? Google Home has you covered.

Now a $49 billion market, voice-activated systems have gained popularity among consumers, thanks to their ability to automate and streamline mundane tasks. But for people with impaired speech,  technologies that rely on voice commands have proved to be far from perfect.

That’s the impetus for Google’s newly formed Project Euphonia, part of the company’s AI for Social Good program. The project team is exploring ways to improve speech recognition for people who are deaf or have neurological conditions such as ALS, stroke, Parkinson’s, multiple sclerosis or traumatic brain injury.

Google has partnered with nonprofit organizations ALS Therapy Development Institute and ALS Residence Initiative (ALSRI) to collect recorded voice samples from people who have the neurodegenerative disease, one that often leads to severe speech and mobility difficulties.

For those with neurological conditions, voice-activated systems can play a key role in completing everyday tasks and conversing with loved ones, caregivers or colleagues. “You can turn on your lights, your music or communicate with someone. But this only works if the technology can actually recognize your voice and transcribe it,” says Julie Cattiau, a product manager at Google AI.

The company’s speech recognition technology utilizes machine learning algorithms that require extensive data training. “We have hundreds of thousands, or even millions, of sentences that people have read—and we use them as examples for the algorithms to learn how to recognize each,” says Cattiau. “But it’s not enough for people with disabilities.”

The post How Google Is Using AI To Make Voice Recognition Work For People With Disabilities appeared first on Artificial Intelligence.