Mediaboss Marketing

Vinita Gupta-

Vinita Gupta is a Silicon Valley Entrepreneur and was the first Indian-American woman to take her company public. Since retiring, she has propelled herself through her journalism, mentoring women entrepreneurs and playing competitive bridge at the highest levels. She has won several National titles in bridge.

The world is excited about Artificial Intelligence (AI).In the last 5000 years, starting with the invention of thewheel, machines have saved humans from existential threats despite our smaller-sized bodies.

Human civilization has evolved due to our superior skills in taking machines to the next level because machines did the kinds of work that humans could not.Consequently, we could become city dwellers with high rises.Mining became possible.Drilling for oil in the ocean became possible.

As we well know human ingenuity has been at play, which helped rationalize how we overcome threats and adversities.

Now we are taking our intelligence to the next level, by making machines more intelligent.That is the ultimate promise of Artificial Intelligence (AI), hoping machines will get smarter than we are.

Will that be good or bad?

What have we learned by becoming super trainers of machines?

Tesla has the most real-life experience with AI, when they put autonomous cars on the streets, equipped with drivers to train the cars.The starting point of self-driving cars is AI algorithms based neural networks similar to those found in the brain.

Say the goal is to have aneural networkrecognize photos that contain a dog. The concept of neural networks entails thatthe machines be not explicitly told what makes a dog.When the computer sees something furry, has a snout, and has four legs it may conclude that it is a dog.Then the machines are shown a lot of images of dogs more data. With minimal training by reinforcement learningwhen the machines are told when they make a mistake the computers start learning from their own mistakes and begin to recognize dogs more reliably.

Tesla has hundreds of thousands of hours of experience based on more than ten years of data collected from training autonomous cars. Based on his experience is why in2017 Elon Muskwarned a bipartisan gathering of U.S. governors that AI is afundamental risk to the existence of human civilization.

Elon now wants to focus on Tesla Bot instead, incorporating Teslas automotive artificial intelligence and autopilot technologies.A car is a Bot on steroids at those speeds. He thinks that a Bot should be perfected first.

Major Hurdles to AI:

1. Humans know only what decision an autonomous car has made, but not why.AI does not permit reverse engineering.Letting machines learn on the fly, on their own, is dangerous when it comes to life-and-death situations or what they might do in the future.

2. Machines by definition do not have common sense, which comes from lived experiences.These broad set of rules of thumb are impossible to be incorporated into machines.Common sense is essential for the robots to operate usefully and safely in the human environment.When a deer jumps in front of an autonomous car, the algorithm will not know what to do. It is even harder to teach machines to make moral or ethical decisions.

3. Intelligent machines will not know not to kill the human specie that helps them survive.Machines will never evolve as organisms do perthe theory of natural evolution.

The idea of Teslas autonomous autos, with current technology, can work for delivery trucks, but it needs infrastructure. Such trucks for example can use dedicated special lanes with barriers maybe only at night.There can be stations along the way where the drivers can hop in, for safe last-mile delivery to the warehouses inside the cities.During commute hours similar concept could be applied to carpoolers.This may not even require the expansion of freeways.

Similarly, smaller walking or even flying robots for making home deliveries sounds promising. On city streets, they can drive in special lanes dedicated to them, just like bike lanes.Integrating the concept with delivery hubs on major street corners may be a more practical solution.

With more people working remotely, and reduced delivery truck traffic on highways and city streets, AI can help us dramatically reduce the carbon footprint to save the environment.

Musk is also planning to introduce a home robot as a personal valet. Some people think it will eliminate hired home help.Another example of machines replacing human labor.

Last but not least, regulatory bodies need to start building expertise in AI, expediently.When in 2018 Facebook CEO Mark Zuckerbergtestifiedbefore a joint hearing in Congress to address steps the social network was taking in light of the Cambridge Analyticas connection with the 2016 presidential elections interference,it was scary to see how little older legislators knew about social media. This was more than 12 years after Facebook was open for general business beyond university campuses.

If we have AI development without regulatory oversight, we will pay a catastrophic price when applied to warfare.According toBill Gates, A.I. is like nuclear energy both promising and dangerous

The rest is here:
Artificial Intelligence : Revolutionary without being Evolutionary - indica News

By the 1950s, several scientists, philosophers, mathematicians, and others had AI incorporated inside their minds. But human beings have now learned how to transform the concept into reality. In recent times, AI has widespread applications everywhere.

AI has the potential to learn quickly from a significant amount of data. It ensures that some of the most technical issues can be tackled without hassle. But it can also feed your excitement and squeeze out your creativity at work.

AI and Photo Editing

AI has transformed traditional photo editing and made it less time-consuming. It can take your hands off repetitive and manually-intensive tasks. AI understands what we want and helps us achieve it quite easily.

You will come across multiple AI-powered photo editing tools in the market. Each device has its own set of unique features and reduces the workload of photo editing. You can efficiently perform a lot of tasks with a single click. For instance, you can add textures, detect faces, and colorize and sharpen your photos.

You can also improve low-resolution images using AI-powered editing tools. Just imagine you got an excellent click in front of the Eiffel Tower. But a random stranger photobombed without their knowledge. Thanks to AI, you can now find a background remover tool like Slazzer. It is a powerful tool that helps businesses save time and money to make their products stand out against the background.

Removing Unwanted Objects from Your Photos

In the photo editing sector, AI has vast applications. Photo editors use AI-based tools to enhance magazine covers, wedding photos, nature shots, and whatnot. In the future, AI-powered software will be developed to meet specific needs according to the requirements of different forms of photography.

Photo background remover tools like Slazzer have already made life easier for editors. But AI-based photoediting tools will become even better at removing backgrounds. They will be able to detect unwanted elements in a picture and correct the mistakes more accurately.

Researchers have developed AI technology to remove unwanted shadows from photographs. The algorithm can focus on two different types of shadows. Shadows from external objects and the ones due to facial features can be removed.

Professional images are usually taken in a studio with sufficient lighting. But when photos are not taken under ideal conditions, dark shadows might obscure some parts of the subject and accessible highlight other parts. The newly developed AI can address the problem by targeting the undesired highlights and shadows.

It can remove and soften the shadows until the subject is clear. With the background remover tool working in a more realistic and controllable way, it will have a higher value than images captured in casual settings. It is beneficial for fixing images shot under circumstances where the lighting cannot be controlled.

What Does the Future of AI-Based Photo Editing Look Like?

With time, AI will become more useful for editing backgrounds. It will be able to take into account minor details like a persons cloth or hair and add lighting that seems natural.

When you consider popular trends such as NFTs, you will see how we view and acquire art is evolving. New options for selling and packaging digital works are constantly on the rise. AI will play a firm role in their faster arrival at the final product. AI will also provide opportunities to amateurs who wish to try their hands at creating art.

Does AI Mean the Job of Professional Photo Editors Are at Risk?

Its no surprise that the rise of AI concerns specific individuals. In every industry, people are worried that AI will replace human skills. Photographers and photo editors believe that artificially edited images will take their jobs.

But the truth is AI will become a powerful tool for these individuals to improve their performances.

AI is constantly reshaping our workflow. It enables us to move faster without compromising on creativity. We need to embrace these new technologies and integrate them within upcoming software creations. This way, the photo editing industry will be able to become more sophisticated.

Summing up

AI is here to take the photo editing industry to a new level. But theres still a lot of time before machines can replace the need for human skills in the photo editing industry.

Meanwhile, tools like Slazzer, with their ability to remove unwanted objects from a photograph, will make the job easier for editors.

Disclaimer: This article is a paid publication and does not have journalistic/editorial involvement of Hindustan Times. Hindustan Times does not endorse/subscribe to the content(s) of the article/advertisement and/or view(s) expressed herein. Hindustan Times shall not in any manner, be responsible and/or liable in any manner whatsoever for all that is stated in the article and/or also with regard to the view(s), opinion(s), announcement(s), declaration(s), affirmation(s) etc., stated/featured in the same.

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How Can Artificial Intelligence Shape The Future Of Photo Editing? | Mint - Mint

TURIN, Italy--(BUSINESS WIRE)--Today, cybersecurity represents an essential priority in the implementation of new technologies, especially given the crucial role that they have come to play in our private and professional lives. Smart Homes, Connected Cars, Delivery Robots: this evolution will not stop and so, in tandem, it will be necessary to develop automated and AI-based solutions to combat the growing number of security threats. The risks from these attacks are attributable to several factors, such as increasingly complex and widespread digital networks and a growing sensitivity to data privacy issues. These are the themes that emerge from the new Cybersecurity Automation research conducted by Reply, thanks to the proprietary SONAR platform and the support of PAC (Teknowlogy Group) in measuring the markets and projecting their growth.

In particular, the research estimates the principal market trends in security system automation, based on analysis of studies of the sector combined with evidence from Replys own customers. The data compares two different clusters of countries: the Europe-5 (Italy, Germany, France, the Netherlands, Belgium) and the Big-5 (USA, UK, Brazil, China, India) in order to understand how new AI solutions are implemented in the constantly evolving landscape of cybersecurity.

As cyberattacks like hacking, phishing, ransomware and malware have become more frequent and sophisticated, resulting in trillions of euros in damages for businesses both in terms of profit and brand reputation, the adoption of hyperautomation techniques has demonstrated how artificial intelligence and machine learning represent possible solutions. Furthermore, these technologies will need to be applied at every stage of protection, from software to infrastructure, and from devices to cloud computing.

Of the 300 billion in investments that the global cybersecurity market will make in the next five years, a large part will be directed toward automating security measures in order to improve detection and response times to threats in four different segments: Application security, Endpoint security, Data security and protection, Internet of Things security.

Application Security. Developers who first introduced the concept of security by design, an adaptive approach to technology design security, are now focusing on an even closer collaboration with the operations and security teams, termed DevSecOps. This newer model emphasizes the integration of security measures throughout the entire application development lifecycle. Automating testing at every step is crucial for decreasing the number of vulnerabilities in an application, and many testing and analysis tools are further integrating AI to increase their accuracy or capabilities. Investments in application security automation in the Europe-5 market are expected to see enormous growth, around seven times the current value, reaching 669 million euros by 2026. A similar growth is forecast in the Big-5 market, with investments rising to 3.5 billion euros.

Endpoint security. Endpoints, such as desktops, laptops, smartphones and servers, are sensitive elements and therefore possible sources of entry for cyberattacks if not adequately protected. In recent years, the average number of endpoints within a company has significantly increased, so identifying and adopting efficient and comprehensive protection tools is essential for survival. Endpoint detection and response (EDR) and Extended detection and response (XDR) are both tools created to accelerate the response time to emerging security threats, delegating repetitive and monotonous tasks to software that can manage them more efficiently. Investments in these tools are expected to increase in both the Europe-5 and Big-5 markets over the next few years, reaching 757 million euros and 3.65 billion euros respectively. There are also a multitude of other tools and systems dedicated to incident management that can be integrated at the enterprise level. For example, in Security Orchestration Automation and Response (SOAR) solutions, AI can be introduced in key areas such as threat management or incident response.

Data security and protection. Data security threats, also called data breaches, can cause significant damage to a business, resulting in risky legal complications or devaluating brand reputation. Ensuring that data is well-preserved and well-stored is an increasingly important challenge. It is easy to imagine how many different security threats can come from poor data manipulation, cyberattacks, untrustworthy employees, or even just from inexperienced technology users. Artificial intelligence is a tool for simplifying these data security procedures, from discovery to classification to remediation. Security automation is expected to reduce the cost of a data breach by playing an important role in various phases of a cyberattack, such as in data loss prevention tools (DLP), encryption, and tokenization. In an effort to better protect system security and data privacy, companies in the Europe-5 cluster are expected to invest 915 million euros in data security automation by 2026. The Big-5 market will quadruple its value, reaching 4.4 billion euros in the same timeframe.

Internet of Things security. The interconnected nature of IoT allows for every device in a network to be a potential weak point, meaning even a single vulnerability could be enough to shut down an entire infrastructure. By 2026, it is estimated that there will be 80 billion IoT devices on earth. The impressive range of abilities offered by IoT devices for different industries, though enabling smart factories, smart logistics, or smart speakers, prevents the creation of a standardized solution for IoT cybersecurity. As IoT networks reach fields ranging from healthcare to automotive, the risks only multiply. Therefore, IoT security is one of the most difficult challenges: the boundary between IT and OT (Operational Technology) must be overcome in order for IoT to unleash its full business value. As such, it is estimated that the IoT security automation market will exceed the 1-billion-euro mark in the Europe-5 cluster by 2026. In the Big-5 market, investments will reach a whopping 4.6 billion euros.

Filippo Rizzante, Replys CTO, has stated: The significant growth that we are witnessing in the cybersecurity sector is not driven by trend, but by necessity. Every day, cyberattacks hit public and private services, government and healthcare systems, causing enormous damage and costs; therefore, it is more urgent than ever to reconsider security strategies and reach new levels of maturity through automation, remembering that though artificial intelligence has increased the threat of the hacker, it is through taking advantage of AIs opportunities that cyberattacks can be prevented and countered.

The complete research is downloadable here. This new research is part of the Reply Market Research series, which includes the reports From Cloud to Edge, Industrial IoT: a reality check and Hybrid Work.

ReplyReply [EXM, STAR: REY, ISIN: IT0005282865] is specialized in the design and implementation of solutions based on new communication channels and digital media. Reply is a network of highly focused companies supporting key European industrial groups operating in the telecom and media, industry and services, banking, insurance and public administration sectors in the definition and development of business models enabled for the new paradigms of AI, cloud computing, digital media and the Internet of Things. Reply services include: Consulting, System Integration and Digital Services. http://www.reply.com

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Reply: Automation and Artificial Intelligence Are the Strategic Keys for an Effective Defense Against Growing Threats in the Digital World - Business...

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Grace' is a lifelike robot nurse, built withartificial intelligence to bring emotional care for patients duringthe pandemic and make them feel comfortable and at ease.

Artificial intelligence (AI), the self-didactic technology whichdetects patterns from historical data, is pervading all walks oflife, be it healthcare or the financial services industry.

The High-Level Expert Group on AI, tasked by the EuropeanCommission to draft AI ethics guidelines, defined AI assystems that display intelligent behaviour byanalysing their environment and taking actions with somedegree of autonomy to achieve specificgoals''.

In the finance world, AI has evolved substantially over therecent decades and its utility ranges from the performance datamonitoring, establishing creditworthiness and credit scoring, aswell as in combatting cybercrime and money laundering. However, theexponential use does not come without a fair amount of risksattached, in particular in machine learning applications whererisks of data bias can lead to erroneous results being generated bythe AI due to statistical errors or interference during the machinelearning process.

The paucity in AI regulation and the multiplicity in AIpractices led the European Commission to focus on this technologyin its Digital Finance Package, launched at the end of 2020 toensure that the EU financial sector remains competitive whilecatering for digital financial resilience and consumerprotection.

Towards the end of last year, the European Central Bankpublished its opinion welcoming the Artificial Intelligence Act.While noting the increased importance of AI-enabled innovation inthe banking sector, given the cross-border nature of suchtechnology, the supranational body held that the ArtificialIntelligence Act should be without prejudice to the prudentialregulatory framework to which credit institutions are subject.

The ECB acknowledged that the proposal cross-refers to theobligations under the Capital Requirements Directive (2013/36 orCRD V') including risk management and governanceobligations to ensure consistency. Yet the ECB sought clarificationon internal governance and outsourcing by banks who are users ofhigh-risk AI systems.

Raising its concerns as to its role under the new ArtificialIntelligence Act, the ECB reiterated that its powers derive fromarticle 127(6) of the Treaty on the Functioning of the EuropeanUnion (TFEU) and the Single Supervisory Mechanism regulation (EU)1024/2013 (SSM regulation), which instruments confer on the ECBspecific tasks concerning prudential supervision policies of creditinstitutions and other financial institutions.

Recital 80 of the proposal provides thatauthorities responsible for the supervision andenforcement of the financial services legislation, including whereapplicable the European Central Bank, should be designated ascompetent authorities for the purpose of supervising theimplementation of this regulation, including for marketsurveillance activities, as regards AI systems provided or used byregulated and supervised financial institutions.

The bank held that market surveillance' under theArtificial Intelligence Act would also consist in ensuring thepublic interest of individuals (including health and safety). In anutshell, the ECB informed the Commission that the ECB has nocompetence to regulate solutions like Grace the robot, but it willonly ensure the safety and soundness of credit institutions. Tothis effect, the bank suggested that (i) a relevant authority beappointed for health and safety risks related obligations; and (ii)another AI authority be set up at Union level to ensureharmonisation.

In parallel, the ECB also recommended that the ArtificialIntelligence Act be amended so as to mandate that, that in relationto credit institutions evaluating the creditworthiness of personsand credit scoring, an ex-post assessment be carried out by theprudential supervisor as part of the SREP, in addition to theex-ante internal controls that are already listed in theproposal.

Interestingly, the Bank for International Settlements, in itsnewsletter on artificial intelligence and machine learning, raisedits concerns in view of the cyber, security and confidentialityrisks, data governance challenges, risk management, biases,inaccuracies and potential unethical outcomes of AI systems,the committee believes that the rapid evolution anduse of AI/ML by banks warrant more discussions on the supervisoryimplications.

While the Artificial Intelligence Act has not been agreed uponin its final form and may be substantially changed before itsacceptance, it is safe to say that the financial sector is one inwhich the challenges relating to the use of AI need to be evaluatedwell, before and when deploying such technological solutions, inview of the risks and individual rights that are at stake.

Originally Published by Times of Malta

The content of this article is intended to provide a generalguide to the subject matter. Specialist advice should be soughtabout your specific circumstances.

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ECB Publishes Its Bug Report On The Proposed EU Artificial Intelligence Act - New Technology - Malta - Mondaq

In marketing, 'creative impact' was once too subjective to measure. But with modern AI solutions, creative efforts can now be effectively analyzed. As part of The Drum's Creativity in Focus Deep Dive, Meta's Maria Pavlova (marketing science partner), Karen Chui (creative partner manager, EMEA) and Safia Dawood (marketing science partner) look at how AI tools can help marketing professionals develop more effective advertising strategies through creative effectiveness insights.

Data-driven optimization is widely adopted in digital advertising. Todays marketing professionals have perfected audience profiles and campaign logistics. Yet, the uncomfortable truth is that creative effectiveness is often overlooked despite being the most important campaign element. Creative quality accounts for 70% of an ads success, eclipsing procedural elements like flighting or platform choice.

Audiences want to be dazzled and amazed, so its important to offer them exciting content. Unfortunately, only 60% of advertisers measure creative effectiveness. Of those, the majority collect feedback using post-campaign focus groups or surveys, making the data somewhat unreliable.

At first glance, creative effectiveness appears too subjective to measure. However, todays AI (artificial intelligence) solutions can effectively analyze your creative impact and are becoming increasingly accessible to marketers.

Solutions from Meta are optimizing campaigns creative prowess and advancing AI-enabled creative workflows. Read on to learn how, and discover case studies on brands that gained sales using creative effectiveness insights.

Cutting-edge marketers already use AI to maximize the potential within their advertising strategy. AI simplifies the creative process by helping to produce new creative assets and adjust existing ones at scale.

For example, automated software can translate content across languages, crop images to frame subjects better and generate image descriptions to make visual assets more accessible. As a result, marketing professionals are uncovering new artistic heights and reimagining how to convey messages across mediums.

However, AI is able to take an even larger role within the advertising life cycle and assist marketing teams in planning and optimizing content like never before.

AI tools can help marketing professionals develop more effective advertising strategies based on audience interaction data. This continuous approach to ad optimization is the future of marketing and will help brands lead with content that audiences love.

Measuring creative effectiveness begins with Illumination, which provides initial insights and builds a model to predict future campaign performance. The results help uncover your next big campaign idea and guide your creative process in later stages.

Algorithms begin by analyzing anonymized and aggregated historical data on consumer behavior and identifying which creative elements are most common among your highest-performing ads.

The result is a framework that helps marketers compare different advertising approaches and discover the most effective one before the campaign begins. In turn, marketing professionals can generate new creative assets with more confidence and exceed audience expectations more effectively.

AI can also help steer the ongoing creative strategy once your campaign begins.

Changes within the digital ecosystem mean advertisers must adopt a new playbook. By leveraging continuous optimization through a test-and-learn process, marketers can focus on what audiences like rather than who they are, and avoid privacy concerns from targeted advertising.

Performance evaluation using machine learning can highlight the potential of each creative element within your existing assets. Automated testing solutions can also enable brands to experiment with dynamic business outcomes more efficiently. As a result, creative teams can devise more effective strategic approaches while campaigns are in flight.

Businesses with multiple locations often have to balance variations in brand guidelines to connect with local audiences effectively. Consistent color palettes and tones of voice help brands feel unified and cultivate a consistent public image.

Yet, these variations present a challenge to marketers as communication must be consistent over time.

Todays AI tools measure how creative assets and copy fit within existing brand guidelines and identify areas for improvement. These insights help brands stay true to their guidelines, both between and within sub-brands, and ensure that public-facing communications are curated and consistent.

As a leading beer brand, Heineken needed a way to assess its creative effectiveness across its 300+ sub-brands. By partnering with Meta and CreativeX, a creative analytics company, Heineken uncovered new insights into its creative effectiveness and strategic potential.

[The] creative elements we measured are foundational to driving effectiveness in the Metaecosystem without restricting the creative process and flow, says Sander Bosch, global CMI manager communication effectiveness at Heineken.

Dominos Pizza is one of the worlds most recognized pizza brands, offering a seemingly infinite number of combinations of toppings and crust fillings.

Dominos wanted to improve their video creative using technology and AI and turned to Spirables creative intelligence suite. The two worked together to create three unique ad variants for a multi-cell split test on Facebook and Instagram across a four-week period.

Spirable determined that while motion was beneficial, moving the pizza out of the frame was not.

By utilizing these new insights in future ads, Dominoes increased return on ad spend by 20%, click-through rate by 6%, and reduced cost per result by 9%.

Have you considered how to measure your creative effectiveness?

Creative effectiveness is just the solution to help marketers measure campaign performance and brand recognition in the transition away from targeted online advertising. AI tools from Meta, CreativeX, Spirable, and others are available today and can elevate your creative effectiveness and remove the guesswork from future ad strategies.

By maximizing your creative effectiveness, you can mitigate signal loss and deliver high-impact content to followers at the same time. You can also establish creative best practices for future campaigns and curate your brand portfolio more effectively.

Moreover, automating routine creative tasks using AI allows you to focus on your campaigns long-term health and ensure ad budgets go further.

Discover tools that can help your business improve creative effectiveness with key insights from Meta Foresights.

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How Artificial Intelligence Is Helping To Measure Creative Effectiveness In Marketing - The Drum