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Deep Learning

Deepfake Is The Scariest Thing Happened To Mankind

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Deepfake Is The Scariest Thing Happened To Mankind

Let’s see how the deepfake scenario is an emerging challenge that has burdened many industries

Digital transformation has opened more avenues for businesses and simultaneously the challenges took shape. The deepfake scenario is an emerging challenge that has burdened many industries in these years.

 

So, let’s understand what are deepfakes?

As the name suggests, these are realistic-looking fake images, videos, and audio that leverage AI and deep learning technology. Deepfakes are created using deep neural networks (DNN) and generative adversarial networks (GAN).

 

A Threat to Media

Fake News: Content is the backbone of media and deepfakes directly affect them. Disinformation and fake news are eating away the credibility and trust in news and media.

Visual Communication: Deepfakes are adversely affecting the authenticity of visual communication by spreading synthetic re-enactment videos.

Social Media: The recent deepfakes of influential politicians like Barrack Obama and Donald Trump created havoc on social media. This reduces the reliability of social media and news platforms.

If wrongly used, deepfakes will reinforce false beliefs and provoke unpleasant actions among the audience, since the media is powerful and influential.

 

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A Threat to Politics

The emerging threat of deepfakes could have an unprecedented impact on politics. This AI-powered technology is already starting to threaten democracy, democratic elections, policy-making, and society at large.

Here are major areas where Deepfakes could be a risk:

Election: Deepfake technology could impact election processes by spreading fake news related to government policy, initiatives, etc.

Fictitious Content: Deepfakes can be used to create bogus content on digital platforms, including offensive or controversial statements to incite violence.

Political Advertising: By using deepfakes, bad actors can intentionally produce misinformation. They can deflect political ads made for national benefits.

If not regulated now, deepfakes could bring toxic politics in the future.

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Artificial Intelligence

GPT-3 Could Eat Up Humans In Spreading Misinformation And Fake News

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GPT-3 Could Eat Up Humans In Spreading Misinformation And Fake News

GPT-3 could Eat Up Humans in Spreading Misinformation and Fake News

Let’s see how misinformation experts have demonstrated how effectively use GPT-3 to misinform.

GPT-3 means Generative Pre-trained Transformer 3, is a language model that leverages deep learning to generate human-like text. Not only can it produce text, but it can also generate code, stories, poems, etc. And it is an auto-complete bot whose underlying Machine Learning model has been trained on vast quantities of text available on the Internet.

It is way better than any algorithm language program in existence and it makes huge pre-trained language models that will become an integral part of AI applications in the near future. The ability of GPT-3 to generate several paragraphs of synthetic content that people find difficult to distinguish from the human-written text in section 3.9.4 represents a concerning milestone.

AI powered misinformation:

OpenAI’s text-producing framework GPT-3 has captured a lot of mainstream attention. OpenAI isn’t the main association to have strong language models, the computing power and data used by OpenAI to model GPT-n.

AI algorithm capable of generating coherent text is GPT-3. Its makers cautioned that the device might actually be employed as a weapon of online misinformation.

Experts from Georgetown research team on misinformation have demonstrated how effectively GPT-3, could be used to mislead and misinform. The result is, that it could intensify a few types of trickiness that would be particularly challenging to detect.

The team used GPT-3 to generate misinformation, including stories around a false narrative, news articles altered to push a bogus perspective, and tweets riffing on particular points of misinformation.

The dataset on which GPT-3 was trained, got terminated in October 2019. So GPT-3 doesn’t know anything about the dataset after that. It can be the weapon of choice for actors who want to promote fake tweets to manipulate the price of crypto.

The team says GPT-3 or AI language algorithm, could prove especially effective for automatically generating short messages on social media, what the researchers call one-to-many misinformation. Making GPT-3 behave would be a challenge for agents of misinformation.

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The team showed example tweets written by GPT-3 about the withdrawal of US troops from Afghanistan and US sanctions on China. In the two cases, they observed that members were influenced by the messages. Subsequent to seeing posts contradicting China sanctions, for example, the level of respondents who said they were against such a strategy multiplied.

In another political situation, GPT-3 had the option to totally change a few people groups’ perspectives, with the assertions making respondents 54% bound to concur with the position subsequent to being shown one-sided AI-generated text.

AI researchers have built programs capable of using language in surprising ways of late, and GPT-3 maybe is the most alarming show of all. The scientists at OpenAI made GPT-3 by taking care of a lot of text scratched from web sources to a particularly enormous AI calculation intended to deal with language.

The Georgetown work features a significant issue that the organization desires to moderate. What’s more, they effectively work to address dangers related to GPT-3.

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Deep Learning

Top 10 Deep Learning Jobs At FAANG Companies To Apply For In 2022

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Top 10 Deep Learning Jobs At FAANG Companies To Apply For In 2022

Explore your enthusiasm for deep learning with these top 10 jobs in the biggest tech giants FAANG.

FAANG stands for Facebook (now Meta), Amazon, Apple, Netflix, and Google (now Alphabet). All the tech enthusiasts around the world are interested to grab a job in these biggest tech giants who are also popular for their wonderful work environment and best quality of teams. Here are the top 10 deep learning jobs at FAANG companies that you can apply for in 2022.

Business Intelligence Engineer II, Business Intel Engineer, Retail Business Service at Amazon

Location: Bengaluru, Karnataka, India

Amazon’s team of high-caliber software developers, applied scientists, data engineers, product managers, and Business Intelligence Engineers use rigorous ML and deep learning approaches to ensure that the company identifies & fixes the right catalog defect to ensure a good shopping experience for its customers. Amazon is looking for a customer-obsessed Business Intel Engineer that thrives in a culture of data-driven decision making and who will be responsible to help the company hold a high bar for the RBS Data Engineering Team.

Apply here.

MSI Full Stack Software Engineer- AI/ML & Big Data at Apple

Location: Bengaluru, Karnataka, India

The ideal candidate must have deep cross-domain experience. You will shape systems that span some of the most technically challenging domains in software today. Apple’s systems intersect the areas of big data, hardware, operations research, analytics & data warehousing, and user experience.

Apply here.

Software Engineer, Machine Learning at Meta

Location: Remote, Netherlands

Facebook is seeking Lead/Senior machine learning engineers to join its engineering team. The ideal person will have industry experience working on a range of classification and optimization problems, e.g., payment fraud, click-through rate prediction, recommendation systems, click-fraud detection, search ranking, text/sentiment classification, or spam detection. The position will involve taking these skills and applying them to some of the most exciting and massive social data and prediction problems that exist on the web.  You will bring the ability to own the whole ML life cycle, define projects and drive excellence across teams.

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Apply here.

Computer Vision & Machine Learning Engineer at Meta

Location: Tel Aviv, Israel

The selected candidate will have to design and develop novel computer vision and/or machine learning algorithms in areas such as real-time scene and object tracking, reconstruction, and understanding as well as, segmentation, face tracking, body tracking, key point estimation, depth sensing, generative approaches such as GANs, 3D stereo and volumetric reconstruction, avatars, reconstructions, and virtual try-on. They will also have to develop prototypes for future VR/AR/MR experiences, drive continued development, and integrate robust solutions into products.

Apply here.

Senior Research Scientist, Computer Graphics / Computer Vision / Machine Learning at Netflix

Location: Los Gatos, California

In this role, you will be focused on applied research to develop computer graphics, machine learning, and/or computer vision algorithms that enable storytellers around the world to create unique content that’s consumed by our members around the world. The algorithms will enable tools used by artists in virtual production, visual effects (VFX), post-production, and animation, and help them focus on the more creative aspects to achieve the output they desire. You will also develop algorithms that assist creatives in both the early and later stages of content production in aspects such as story visualization and audience understanding. You will partner with the content production teams in Studio as well as the product management and engineering teams to develop and test new models and algorithms.

Apply here.

Senior Software Engineer, Games Data at Netflix

Location: Los Gatos, California

Data Science and Engineering (‘DSE’) at Netflix is aimed at using data, analytics, and sciences to improve various aspects of our business. The company is looking for a Software Engineer to join our Games DSE team, supporting end-to-end analytics & data needs for the Games space. This role will be partnering closely with our Game stakeholders to develop software and data solutions that enable analysis across the entire library of games.

Apply here.

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Software Development Engineer at Amazon

Location: Bengaluru, Karnataka, India

Amazon is looking for a passionate, hard-working, talented with the highest quality standard software engineer with experience building mobile applications and test engineering, especially those with high computational demands and taking advantage of mobile cameras. You will have an enormous opportunity to make a large impact on the design, architecture, and implementation of cutting-edge products used every day by millions of people. You will be responsible to build mobile application software and test frameworks involving camera APIs and AR frameworks.

Apply here.

Cloud Technical Solutions Engineer, AI/ML, Google Cloud at Alphabet

Location: Pune, Maharashtra, India

The selected candidate will have to work with customers on production Machine Learning deployments to resolve issues and achieve production readiness, availability, and scale. They will have to manage issues of Google Cloud customers through effective diagnosis, resolution, documentation, or implementation of investigation tools and develop an in-depth understanding of Google Cloud’s AI/Machine Learning products/solutions and underlying architectures by troubleshooting, reproducing, and determining the root cause for customer issues. The candidates must also understand customer issues, advocate for their needs with internal Product and Engineering teams, find ways to improve the product and drive production changes. They can further act as subject matter experts for internal stakeholders in engineering, sales, and customer organizations to resolve technical deployment obstacles to improve Google Cloud and be part of a team of engineers/consultants that globally ensure 24-hour customer support.

Apply here.

AI Manager, Google Cloud, Delivery Center at Alphabet

Location: Gurugram, Haryana, India

Alphabet is looking for candidates who will work with customers to identify opportunities to apply machine learning in their business. They would also have to design and implement machine learning solutions for customer use cases, leveraging core Google products including TensorFlow, DataFlow, and Artificial Intelligence Platform. They get to manage and grow a team of consultants and engineers and work with customer technical leads, client executives, and partners to manage and deliver successful implementations of cloud solutions becoming trusted advisors to decision-makers throughout the engagement.

Apply here.

Technical Trainer, Google Cloud at Alphabet

Location: Tokyo, Japan

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The Google Cloud Platform team helps customers transform and evolve their business through the use of Google’s global network, web-scale data centers, and software infrastructure. As part of an entrepreneurial team in this rapidly growing business, you will help shape the future of businesses of all sizes using technology to connect with customers, employees, and partners. As part of this team, you will deliver training to a mix of customers, strategic partners, and Googlers. While the focus will be on technical training classes, you may at times also support training activities for Marketing events.

Apply here.

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Computer Vision

Top 8 Computer Vision Techniques Entwined With Deep Learning

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Top 8 Computer Vision Techniques Entwined With Deep Learning

Edge AI will be a crucial technology for bringing deep learning from the cloud to the edge.

In healthcare and medical imaging, computer vision has shown considerable promise. However, as technology advances, a growing number of medicinal applications are becoming available. To run computer vision in health care applications, nevertheless, privacy-preserving deep learning and picture identification will be necessary. As a result, Edge AI will be a crucial technology for bringing deep learning from the cloud to the edge. Edge devices interpret video streams in real-time without transferring sensitive visual data to the cloud by conducting machine learning activities on-device.

Here are 8 computer vision techniques entwined with deep learning.
1. Tumor Detection

In the medical area, computer vision and deep learning applications have shown to be quite useful, particularly in the precise diagnosis of brain cancers. If left untreated, brain tumors spread swiftly to other areas of the brain and spinal cord, making early discovery critical to the patient’s survival. Medical experts may employ computer vision software to speed up and simplify the detection procedure.

2. Medical Imaging

Computer vision has been utilized in a variety of healthcare applications to help doctors make better treatment decisions for their patients. Medical imaging, also known as medical image analysis, is a technique for seeing specific organs and tissues in order to provide a more precise diagnosis.

With medical image analysis, physicians and surgeons may get a better look into the patient’s interior organs and spot any problems or anomalies. Medical imaging includes X-ray radiography, ultrasound, MRI, endoscopy, and other procedures.

3. Cancer Detection

Deep-learning computer vision models have attained physician-level accuracy when it comes to diagnosing moles and melanomas. Skin cancer, for example, can be difficult to diagnose early since the symptoms are often similar to those of other skin conditions. As a solution, scientists have used computer vision technologies to successfully distinguish between malignant and non-cancerous skin lesions.

There are various benefits to employing computer vision and deep learning systems to identify breast cancer, according to research. It can assist automate the detection process and limiting the likelihood of human mistakes by using a large library of photos including both healthy and malignant tissue.

4. Medical Training

Not just for medical diagnosis, but also for medical skill development, computer vision is frequently employed. Currently, surgeons are not only reliant on the conventional method of learning skills via hands-on experience in the operating room. Simulation-based surgical platforms, on the other hand, have shown to be an excellent tool for teaching and testing surgical abilities.

Surgical simulation gives trainees the opportunity to practice their surgical abilities before entering the operating room. It allows them to receive thorough feedback and evaluations of their performance, helping them to develop a better understanding of patient care and safety before performing surgery on them.

5. Combating Covid-19

The Covid-19 epidemic has presented a major threat to the worldwide healthcare system. With governments all around the world attempting to battle the disease, computer vision can make a huge contribution to overcoming this obstacle.

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Computer vision applications can help in the diagnosis, treatment, control, and prevention of Covid-19 thanks to rapid technological improvements. In conjunction with computer vision programs like COVID-Net, digital chest x-ray radiography pictures may readily diagnose illness in patients. The prototype program, built by Darwin AI in Canada, has shown results in covid detection with a 92.4 percent accuracy.

6. Health Monitoring

Medical practitioners are increasingly using computer vision and AI technologies to track their patients’ health and fitness. Doctors and surgeons can make better judgments in less time using these assessments, especially in emergency situations.

Computer vision models can assess whether a patient has reached its final stage by measuring the volume of blood lost during surgery. One such application is Gauss Surgical’s Triton, which successfully monitors and calculates the volume of blood lost during surgery. It aids surgeons in determining how much blood the patient will require during or after surgery.

7. Machine-assisted Diagnosis

In recent years, advances in computer vision in healthcare have resulted in more precise diagnoses of illnesses. Computer vision techniques have shown to be superior to human specialists in recognizing patterns and accurately detecting illnesses.

These technologies can assist doctors in detecting malignancy by detecting tiny changes in tumors. Such instruments can assist in the discovery, prevention, and treatment of a variety of illnesses by scanning medical images.

8. Timely Detection of Disease

The patient’s life and death are dependent on prompt identification and treatment for a variety of disorders such as cancer and tumors. Early detection of symptoms increases the patient’s chances of survival.

Computer vision applications are educated with large volumes of data, such as hundreds of photos, in order to detect even the tiniest differences with high accuracy. As a consequence, medical practitioners may spot minor alterations that would otherwise go unnoticed by the naked eye.

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