Deepfake is a type of artificial intelligence created by combining the phrases deep learning and fake that facilitates in developing videos that have been fabricated via using deep learning techniques. It is a subset of AI that refers to algorithms that can learn and make intelligent judgments without human intervention. A deep-learning system can create convincing impersonations by examining images and videos of a target person from various perspectives and then copying their behavior and voice patterns. Once a prototype fake has been created, GANs (Generative Adversarial Networks) are used to make it more credible. The GANs method aims to find faults in the system and make adjustments to fix them.

How can you stay away from deepfake videos?

1. Deepfake videos are much simpler to spot than deepfake photographs. And you may accomplish so with the assistance of two factors. When a deepfake video of a person is created, for example, there is little difference between the person and the backdrop. However, you may spot a false video if the attention is solely on the face in the video and the surrounding is purposefully obscured.
2. Deepfake can be easily avoided by restricting personal images on social media and avoiding close-up photos of your face as much as possible.
3. Advanced artificial intelligence algorithms are under development which can swiftly identify deepfake videos thereby preventing people from falling prey to fake news and fake films.

When and where did deepfake start?

Deep Fake was a user on Reddit in 2017 who began employing face modification technology for pornography. It was from here that the term Deepfake was coined, and videos like this were known as Deepfake Videos. 

Deepfake as a boon to technology

MyHeritage, a software program, has been in the headlines for transforming any image into a 10-second movie. With this app, you may also breathe new life into old images using this program. With the use of this program, images of prominent personalities from past were transformed into movies. And these films show that if Artificial Intelligence is applied correctly, this approach may be beneficial to humans.

Deepfake as a threat to humanity

A.  Deepfakes were used to subvert democracy in the United States 

Facebook decided to prohibit the use of deep fakes after fake videos of politicians began spreading on social media. They allowed a few loopholes, such as the ability to keep sarcastic films and photos, but distinguishing between satire and agenda-driven content is difficult.

B.  Deepfakes began to be exploited by internet predators 

People began leveraging the ability to substitute anyone's face in an image or video to make pornographic content without their consent. As the deep fake technology allows them to do so by replacing face and expressions; all cybercriminals need is a profile photograph on social media to produce fake material to produce fake videos.

C.  Deepfake to tarnishing reputation of individuals 

A Pennsylvania mom, for example, was prosecuted for harassing cheerleaders at her daughter's school by employing deep fakes. The mother used manipulated recordings to carry out a cyberbullying campaign against girls she viewed as competitors to her daughter.

Deepfakes still continues pushing the digital media envelope where researchers suggest using NFTs (Non-Fungible Tokens) is the most effective strategy to combat deep fake. But NFTs, on the other hand, are still far away from being the standard on blockchains like Ethereum.

To know more about various cyber threats and methods to prevent them, contact Centex Technologies at (972) 375-9654.

DevOps methodology has significantly improved software development by breaking down the traditional barrier between development & IT teams. This collaboration of distributed teams helps in reducing the timeline of software development. However, the ultimate goal of DevOps – which is 100% automation across Software Development Lifecycle (SDLC) – remains unachieved. Some business organizations still seem to be struggling with how to integrate DevOps in overall business processes.

These challenges can be maneuvered by adopting AI. The highly distributed nature of AI toolsets helps in reducing operational complexities of DevOps methodology. AI also improves the accuracy, quality and reliability of DevOps by streamlining and accelerating different phases of software development.

Ways in which AI transforms DevoPS:

• Testing: DevOps includes a number of testing processes such as unit testing, regression testing, functional testing, and user acceptance testing. These testing processes generate a large amount of data and analyzing this data can be overwhelming for the DevOps team. AI implements pattern recognition to make it easier to analyze and categorize the data. After analyzing, it also provides insights on poor coding practices and errors to help code developers identify areas for better performance.
• Data Access: The productivity and efficiency of DevOps team is highly stalled by lack of adequate access to data. This hinders the team’s ability to leverage data for decision-making. AI-powered data mapping technologies integrate a myriad of data from different sources & streamline it for consistent & repeatable analysis. It helps teams uncover valuable insights for decision-making.
• Real-Time Alerts: Prompt alerts are helpful in promoting rapid response. However, when DevOps teams receive multiple alerts with same level of severity, it becomes difficult for them to react effectively. In such situations, AI helps in prioritizing most critical issues by collecting diagnostic information pertaining to every issue. In addition to prioritizing the issues, AI also suggests prospective solution based on magnitude of alert, past behavior, & source of alert. This facilitates faster remediation of the issue.
• Automation: Integration of AI with DevOps significantly improves the automation quotient by eliminating or reducing the need for human intervention across processes from code changes to deployment.
• Security: DevSecOps is an extension of DevOps that ingrains security into DevOps workflow. It automates core security tasks across software development lifecycle. AI based anomaly detection techniques help teams to accurately spot threats to their system and secure it proactively.
• Collaboration: AI plays an important role in improving collaboration between DevOps teams by facilitating a single, unified view into system issues across DevOps toolchain.
• Software Quality: AI improves the quality of software by auto-generating and auto-running test cases on the code. AI-based testing tools eliminate test coverage overlaps and fasten the process from bug detection to bug prevention.

Centex Technologies offers software development services for organizations. To discuss your software requirements, call Centex Technologies at (972) 375 - 9654.

IT has a major impact on the way a business operates. However, IT trends keep evolving in an unexpected fashion. As the new stage of digital evolution knocks on our doors in 2020, the new IT trends such as emotionally intelligent interfaces and hyper-intuitive cognitive capabilities promise to transform businesses in unpredictable ways. New IT trends open new opportunities for businesses to expand their market and user base.

Here is a list of business IT trends of 2020 to help you architect business systems that are designed to respond to new age IT trends:

• Artificial Intelligence: AI is not a new trend but it keeps widening its parameters. AI has now found applications in different fields of business operations. The latest AI business trends include smart assistants, chatbots and self driving cars. In 2020, AI can be experienced in every digital market; from cybersecurity to healthcare.

• Edge Computing: With an increase in the number of devices connected to the internet and each other, there has been an increase in demand on server infrastructure. The demand is even higher for video camera-based services that require constant video streaming. This has laid the pathway for edge computing. Edge computing is a computing network where computing is done at or near the data source to reduce the processing time. Video providers can benefit from this trend for reducing server costs and adding core functionalities that were not previously possible.

• Autonomous Vehicles: While it may take some time for the autonomous vehicles to carve a niche in the market, they seem to be a promising upcoming trend in B2B and consumer markets. This trend is sure to have an impact on the way automobile businesses operate as they will have new investment opportunities such as manufacturing of autonomous vehicles and developing new services/features that cater to health, entertainment, comfort, and productivity of the person being driven in the autonomous vehicle.

• 5G: 5G is being hailed as the base of new business revolution due to the speed and capabilities it promises. It has led to new investment opportunities for carriers and mobile device manufacturers. Additionally, it promises to enhance the surfing capabilities of the users thus, allowing businesses to offer applications with more features, plug-ins and usability.

For more information on Web Applications for enterprises, call Centex Technologies at (972) 375 - 9654.        

Outbreak of new diseases puts immense pressure on the healthcare system. Factors such as limited resources, a sudden rise in the number of patients, lack of established treatment, and vulnerability of medical staff add to the pressure on the healthcare system. In order to combat the outbreak of new diseases effectively, healthcare professionals require faster diagnosis, rapid response, effective use of human resources, and ensuring the safety of medical personnel.

In this scenario, medical AI is proving to be of immense use. Although the use of medical AI is not a new concept, the applications of medical AI have widened with the outbreak of new diseases and pandemics such as COVID-19.

• Diagnostics: Diagnosis of some diseases may require the doctors to compare a large number of CT Scans, check medical history, or other detailed reports. This is a time-consuming process and may result in delayed treatment causing negative effects, specifically in time sensitive cases. AI software helps in analyzing large amounts of patient data to assist doctors in diagnosing patients at a greater pace. This assists in lowering the treatment time.
• Risk Profiling: In the case of diseases that are contagious and spread easily, it becomes imperative to track the patients, travelers, tourists, etc. This helps in building a risk profile of the individuals. But manual profiling is time-consuming and prone to errors. On the other hand, AI-based risk profiling software has capabilities of historical geolocation and anomaly tracking of individuals in an efficient manner.
• Optimizing Drug Therapy: Effective and proper treatment of a new bacterial or viral infection requires a combination of different drugs. However, the process of searching for suitable drug molecules can be laborious, time consuming, and costly. It also requires numerous trials and can involve manual errors. AI-based systems help in optimizing the drug discovery process by searching the databases of complex drug molecules and matching the drug molecules with the target proteins. This reduces the research time and increases efficiency.
• Assistance For Medical Staff: AI-based apps can help the healthcare staff in gathering relevant information such as hospital-specific information, latest guidelines, operational directives, new treatments, protocols, drug dosage, and drug formulary. Well-trained AI systems can be used to analyze a patient’s reports and assign the case to a relevant specialist in the hospital.

For more information on increasing applications of medical AI, call Centex Technologies at (972) 375 - 9654.

Outbreak of new diseases puts immense pressure on the healthcare system. Factors such as limited resources, a sudden rise in the number of patients, lack of established treatment, and vulnerability of medical staff add to the pressure on the healthcare system. In order to combat the outbreak of new diseases effectively, healthcare professionals require faster diagnosis, rapid response, effective use of human resources, and ensuring the safety of medical personnel.

In this scenario, medical AI is proving to be of immense use. Although the use of medical AI is not a new concept, the applications of medical AI have widened with the outbreak of new diseases and pandemics such as COVID-19.

• Diagnostics: Diagnosis of some diseases may require the doctors to compare a large number of CT Scans, check medical history, or other detailed reports. This is a time-consuming process and may result in delayed treatment causing negative effects, specifically in time sensitive cases. AI software helps in analyzing large amounts of patient data to assist doctors in diagnosing patients at a greater pace. This assists in lowering the treatment time.
• Risk Profiling: In the case of diseases that are contagious and spread easily, it becomes imperative to track the patients, travelers, tourists, etc. This helps in building a risk profile of the individuals. But manual profiling is time-consuming and prone to errors. On the other hand, AI-based risk profiling software has capabilities of historical geolocation and anomaly tracking of individuals in an efficient manner.
• Optimizing Drug Therapy: Effective and proper treatment of a new bacterial or viral infection requires a combination of different drugs. However, the process of searching for suitable drug molecules can be laborious, time consuming, and costly. It also requires numerous trials and can involve manual errors. AI-based systems help in optimizing the drug discovery process by searching the databases of complex drug molecules and matching the drug molecules with the target proteins. This reduces the research time and increases efficiency.
• Assistance For Medical Staff: AI-based apps can help the healthcare staff in gathering relevant information such as hospital-specific information, latest guidelines, operational directives, new treatments, protocols, drug dosage, and drug formulary. Well-trained AI systems can be used to analyze a patient’s reports and assign the case to a relevant specialist in the hospital.

For more information on increasing applications of medical AI, call Centex Technologies at (972) 375 - 9654.

Artificial intelligence has garnered immense applications in various industries including banking, manufacturing, and healthcare. A branch of healthcare that is benefitting from Artificial Intelligence is the ‘Pharmaceutical Industry’. It is met with new challenges in the face of new viruses, mutated antigens, drug-resistant strains, etc. on a daily basis. Additionally, it has become common to see the rendition of once eradicated diseases such as polio. Under these conditions, traditional R&D can be very time consuming and costly.  

Traditional drug discovery methods are objective driven and work well for targets whose structure and interactions in the cell are understood. However, most of the cellular transactions have complex pathways.

In order to overcome these challenges, Artificial Intelligence-powered drug discovery offers an effective alternative. Following are the ways in which AI transforms the drug discovery process:

• AI-powered drug discovery follows a data-driven approach that is based on the vast patient datasets. The data is studied and categorized by complex algorithms into understandable information for facilitating drug discovery at a faster pace.
• It applies machine learning to study new incoming data for recognizing new opportunities and information.
• The algorithms search through vast databases of compound structures to identify a compound that can bind to the antigen protein (even if the structure of the target protein has not been yet identified). This saves a lot of time when compared to manual screening of compounds that can act as drug candidates.

How It Works?

• The first step is to take sample from people with and without a disease. Also, samples are taken from people who are at different stages of disease progression.
• The sample data is then extracted into genomics, proteomics, metabolomics and lipidomics for identifying the target.
• AI and Machine Learning software study the information to identify any differences between the disease and non-disease states, proteins, and other features that may impact the disease state.
• The identified proteins and metabolites are considered to be target candidates by the software.
• The candidates are then queried against databases of patents, publications, chemical libraries, clinical trials, and approved drugs. This facilitates a precision-medicine approach by offering a means to triage the patients in an in-silico manner before entering a clinical trial to determine the effectiveness of a potential drug.

Benefits Of AI-Driven Drug Discovery:

• AI does not rely on predetermined targets which rules out the chances of subjective bias.
• AI amalgamates the latest technology in biology and computing to develop algorithms for drug discovery.
• AI offers high predictive power to define meaningful interactions in drug screening. This reduces the chances of pursuing false potential drugs.
• AI moves drug discovery to a virtual lab where screening results can be obtained at a faster pace and efficiency.

For more information on transforming drug discovery through AI, call Centex Technologies at (972) 375 - 9654.

Customer service is an important aspect of any business organization. Businesses keep looking for new ideas to improve their customer service and offer better customer experience, which has led to the advancements in AI based customer service solutions. Although, businesses have been using AI in customer support for a while; the collaboration still holds many facets that are yet to be unfolded in the future.

Following are some of the applications of AI that can be explored for enhancing customer service by businesses:

• Brand Messenger: In recent years, there has been an increased user inclination towards messaging apps. The use of messaging has extended from personal communications to user engagement with brands. This has laid out a path for businesses to incorporate chatbots to interact with new and existing customers. As some major industries (like fashion, tourism, food chains, airline, e-commerce and hotels) have adopted this feature to increase user engagement; it would be exciting to know which industries will follow the suit in future.

• Quick Resolution: Wait time for resolving simple queries is an important determinant in customer satisfaction. Customers seek quick answers to general queries and tend to trust a brand that offers faster answers and streamlined action plans for their queries. Thus, businesses can exploit the capacity of AI to multi-task and handle multiple automated queries. This will help in limiting the response time and generating accurate resolutions.

• Customized User Experience: In addition to making self-service user interfaces more intuitive, AI can help in anticipating customer needs based on previous chat history, contexts and user preferences. AI integrated systems can capture a large amount of data for identifying customer issues, defining customer behavior, determining frequent decisions, prompting with proactive alert messages, suggesting personalized offers and discounts, etc. Such intelligent assistance and pre-emptive recommendations will help companies in offering a quality rich customer service.

• AI Controlled Multiple Support Channels: In addition to providing direct assistance to the customers, AI can be used to control multiple channels of customer support. For example, in case a telecommunications agent is unable to answer a query, AI can determine the issue and direct the customer towards dedicated support channel.

Undoubtedly, these applications support the strengthening of collaboration between AI & Customer Support. However, as the AI systems rely on collecting extensive user data for working efficiently, this gives rise to privacy concerns. The data collecting system can be compromised resulting in a data breach. Thus, business organizations need to pay due attention to data security policies before implementing AI supported customer service systems.

For more information about use of AI in customer service, call Centex Technologies at (972) 375 - 9654.

Artificial Intelligence (AI), Machine Learning and Deep Learning are commonly used interchangeably. However, in technological context; Machine Learning and Deep Learning are subsets of AI. In order to understand the difference between these terms, it is important to know the actual meaning of individual term.

Artificial Intelligence (AI): Artificial Intelligence is a term that defines the simulation of human intelligence processes by computer systems. The processes include learning, reasoning and self-correction. AI is broadly classified as weak (narrow) AI and strong AI. Weak AI systems are designed to do a particular task. The most common example of weak AI is the virtual personal assistants. On the contrary, strong AI systems are equipped with generalized human cognitive abilities. These systems are able to find a solution to any problem independent of human intervention.

Machine Learning (ML): ML is an application or subset of Artificial Intelligence. Under this application of AI, a machine is programmed to access and manipulate data. The machine can analyze the data to identify patterns and learn from these patterns. This allows the machine or computer system to modify decisions as per any change in data without explicit programming. Machine Learning is driven by algorithms and stat models. The common usage of Machine Learning can be found in apps such as email filtering, optimization, internet fraud detection, etc. Machine Learning methods are widely grouped as supervised and unsupervised ML.

• Unsupervised ML: These methods group interpret data based only on input data. Clustering methods are an example of unsupervised ML.
• Supervised ML: Supervised ML methods use both input and output data to develop a predictive model. Classification and Regression methods are listed as supervised ML.

Deep Learning (DL): It is a broader subset of AI. Deep Learning involves collection of large unstructured data and combing through it to generate classified structured information. The basic difference between Machine Learning and Deep Learning is that ML is task oriented learning, whereas DL is more general. It is used to derive meaning or identify patterns in unstructured data. This, in turn, helps in spotting large scale trends or irregularities. Some common applications of Deep Learning include self-driving cars, fraud news detection, natural language processing, visual recognition, etc.

For more information about Artificial Intelligence, Machine Learning and Deep Learning, call Centex Technologies at (972) 375-9654.