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3 January 2025
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AI

AI-Generated Content: Revolutionizing or a Step into a wrong direction

Reading Time: 4 minutes

Introduction

Artificial intelligence (AI) has rapidly transformed the way we communicate, create, and influence. What was once the domain of science fiction is now an everyday reality, as AI-generated content reshapes industries from marketing and journalism to public relations and corporate communication.

However, while AI offers unparalleled efficiency and scalability, it also raises ethical concerns, challenges authenticity, and demands new skill sets from leaders in the communication space. Should brands fully embrace AI, or does over-reliance on machine-generated content risk eroding trust?

This article explores the opportunities and pitfalls of AI-generated content, focusing on how communication leaders must adapt to this evolving landscape while maintaining ethical responsibility and human authenticity.

The Growing Role of AI in Communication

AI’s presence in communication is no longer subtle—it is redefining how content is created, optimized, and distributed. The introduction of machine learning models, natural language processing (NLP), and generative AI has led to significant transformations in public relations, marketing, and media.

1. AI-Driven Content Creation

Companies are now using AI to generate content at unprecedented speeds:

• Persado – Uses AI-driven language analytics to craft marketing messages that maximize audience engagement.

• MarketMuse – Optimizes content for SEO, ensuring higher search rankings and better visibility.

• Automated Insights & Narrative Science – Employ Natural Language Generation (NLG) to produce financial reports, sports summaries, and news articles.

These technologies streamline content production, enabling businesses to create personalized, data-driven messages in seconds rather than days.

2. AI in Journalism and Media

Even journalism, an industry once reliant solely on human reporting, has embraced AI. News organizations use AI tools to automatically generate simple stories, such as:

• Financial reports – AI pulls real-time market data and writes stock updates.

• Sports recaps – Algorithms summarize game statistics instantly.

• Elections coverage – AI aggregates polling data and produces real-time updates.

While AI excels at handling structured, data-heavy reporting, it struggles with context, investigative nuance, and deep analysis, leaving room for human journalists to retain a critical role.

Implications for Communication Leaders

AI isn’t just changing the tools of communication—it’s reshaping the skills, strategies, and ethical responsibilities of those leading the industry.

1. Strategy Overhaul: Data-Driven Decision Making

Traditional communication strategies relied on creativity and intuition. Today, leaders must integrate AI-driven insights and analytics into their decision-making.

AI tools can analyze vast datasets to determine:

• Which messaging resonates most with target audiences.

• The optimal timing for content distribution.

• How to personalize communication at scale.

Leaders who ignore AI-driven strategy risk falling behind their competitors, who can fine-tune campaigns with precision.

2. The Rise of Multidisciplinary Skill Sets

Tomorrow’s Chief Marketing Officers (CMOs) and PR executives will need more than communication skills—they must understand:

• Machine learning and data analytics – To interpret AI-generated insights.

• SEO and content optimization – AI-powered tools influence visibility and ranking.

• Cybersecurity and data privacy – AI involves collecting vast user data, posing regulatory risks.

AI isn’t just a tool—it’s a force requiring communication leaders to evolve.

3. Ethical Dilemmas: Transparency & Authenticity

AI-generated content introduces ethical concerns that communication professionals must address:

• Should consumers know when AI writes content? Transparency is crucial to maintaining trust.

• How do brands maintain authenticity when AI produces their voice? Overuse of AI can lead to a robotic, impersonal tone.

• Who is accountable for AI-generated misinformation? Without human oversight, AI can spread false or biased information.

A strong ethical framework is necessary to guide AI’s role in content creation while safeguarding credibility and authenticity.

4. AI-Generated Content vs. Human Creativity

While AI can produce informational content, it struggles with creativity, storytelling, and emotional depth. AI-generated content often lacks:

• Human intuition – AI cannot replicate cultural nuances or humor.

• Original thought – AI models pull from existing data rather than creating truly new ideas.

• Emotional connection – Audiences engage with brands that feel genuine and relatable.

The best approach? Human-machine collaboration—where AI enhances efficiency but human creativity shapes the message.

5. Data Privacy & Security Risks

AI-driven communication platforms rely on vast amounts of data. This presents significant privacy risks, particularly in light of GDPR, CCPA, and evolving AI regulations.

Communication leaders must:

• Ensure compliance with data privacy laws when using AI-driven customer insights.

• Be transparent about AI’s role in audience engagement to avoid ethical concerns.

• Prioritize cybersecurity to protect AI-driven platforms from data breaches.

Failure to address these risks can lead to legal consequences and reputational damage.

How Leaders Can Navigate AI Challenges

To fully leverage AI while maintaining trust and ethical responsibility, communication professionals should adopt a balanced, strategic approach:

1. Human-Machine Collaboration

AI should be used to enhance, not replace, human creativity. Effective strategies include:

• Using AI for analytics and optimization but humans for emotional storytelling.

• Automating repetitive content tasks while keeping high-value creative work human-led.

• Combining AI insights with human intuition to craft better campaigns.

2. Implementing Ethical AI Guidelines

Every organization should establish ethical policies on AI usage, including:

• Transparency policies – Inform audiences when content is AI-generated.

• Content quality standards – Ensure AI-generated material meets editorial guidelines.

• Bias detection measures – Monitor AI outputs for misinformation and cultural insensitivity.

3. Continuous Learning & Adaptation

AI is evolving rapidly, making ongoing education essential for communication professionals. Leaders should:

• Invest in AI literacy training for their teams.

• Stay updated on AI ethics, regulations, and best practices.

• Explore partnerships with AI research institutions to remain ahead of trends.

Forbes Communications Council and similar professional networks provide insights, training, and peer collaboration to help leaders navigate this new landscape.

Final Thoughts: A Double-Edged Sword

AI-generated content is both a boon and a challenge for communication professionals. It offers unparalleled efficiency, scalability, and data-driven precision, yet it also raises ethical concerns, risks authenticity, and demands new skill sets.

Leaders in PR, marketing, and media must not resist AI but instead master it—adopting a human-machine collaboration approach that blends technology’s capabilities with human creativity and ethical oversight.

As AI continues to reshape the field, the organizations that proactively adapt will be the ones that thrive in this new era of communication.

AI Engine Used: Bard AI

https://www.techtarget.com/whatis/feature/Pros-and-cons-of-AI-generated-content
https://councils.forbes.com/blog/the-rise-of-ai-generated-content-for-communication-execs
https://www.ibm.com/think/insights/ai-generated-content
https://www.conductor.com/academy/ai-generated-content/
Machine Writing vs. The Human Touch: An In-Depth Look at AI-Generated Content
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12 November 2023
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Embracing the Robotic Revolution: The Convergence of AI and Robotics is Within Reach.

Reading Time: 3 minutes
IMAGE CREDITS: DeepAi

Artificial Intelligence (AI) has witnessed a transformative phase with the introduction of large language models (LLMs) like ChatGPT and Bard. These models have revolutionized AI for language processing and problem-solving. However, the next frontier for AI lies in robotics. Building AI-powered robots that can learn to interact with the physical world has the potential to enhance various industries, from logistics and manufacturing to healthcare and agriculture. In this article, we will explore the parallels between the success of LLMs in language processing and the upcoming era of AI-powered robotics.

Building on the Success of GPT.

To understand how to build the next generation of robotics using the principles that made LLMs successful, we need to look at the core pillars of their achievements.

  1. Foundation Model Approach: The concept of foundation models, as seen in GPT, focuses on training a single AI model on a vast and diverse dataset. Unlike previous approaches where specific AI models were created for distinct tasks, a foundation model can be universally utilized. This general model performs well across multiple tasks and leverages learnings from various domains, improving its performance overall.
  2. Training on a Large Proprietary and High-Quality Dataset: The success of LLMs can be attributed to training them on large and diverse datasets. In the case of GPT, the models were trained on a wide range of data sources, including books, news articles, social media posts, and more. The high-quality dataset, informed by user preferences and helpful answers, has been instrumental in achieving unprecedented performance.
  3. Role of Reinforcement Learning (RL): Reinforcement learning, combined with human feedback, plays a crucial role in fine-tuning and aligning the AI model’s responses with human preferences. GPT utilizes reinforcement learning from human feedback (RLHF) to enhance its capabilities. This approach allows the model to move towards its goal through trial and error, achieving human-level capabilities through learning from human feedback.

Applying GPT Principles to Robotics

The foundation model approach, training on a large proprietary dataset, and incorporating reinforcement learning have paved the way for the development of AI-powered robots. Just as GPT models can process text and images, robots equipped with foundation models can understand their physical surroundings, make informed decisions, and adapt their actions to changing circumstances.

  • Revamping Robotics: Exploring New Frontiers with Advanced Techniques:Similar to language models, applying the foundation model approach to robotics enables the development of one AI model that works across multiple tasks in the physical world. This shift allows the AI to respond better to edge-case scenarios and achieve human-level autonomy. Training on a diverse dataset collected from real-world interactions is essential for teaching robots how to navigate and operate effectively.
  • Harnessing the Power of Training on Etensive, Exclisive, and High-Quality Datasets: Unlike language or image processing, there is no preexisting dataset that represents how robots should interact with the physical world. Consequently, training robots to learn from real-world physical interactions is difficult, but crucial. Deploying a fleet of robots in production environments becomes necessary to gather the data needed for training comprehensive robotics models.
  • Empowering Robots throught the Role of Reinforcement Learning: In robotics, as in language processing, pure supervised learning is insufficient. Robotic control and manipulation require reinforcement learning (RL) to seek progress toward goals without a unique correct answer. Deep reinforcement learning (deep RL) enables robots to adapt, learn, and improve their skills as they encounter new scenarios and challenges.

The Future of AI Robotics

The combination of these principles and advancements in AI and robotics sets the stage for a revolution in the field. The growth trajectory of robotic foundation models is rapidly accelerating. Already, applications such as precise object manipulation in real-world production environments are being deployed commercially. In the coming years, we can expect to see an exponential increase in commercially viable robotic applications across various industries.

Conclusion

The GPT moment for AI robotics is on the horizon. By leveraging the foundation model approach, training on large datasets, and incorporating reinforcement learning, AI-powered robots are poised to transform industries by enhancing repetitive tasks and adapting to dynamic physical environments. As we enter this new era of AI robotics, the possibilities for automation and efficiencies in the physical world are vast and promising.

Links worth visiting:

Role of Artificial Intelligence and Machine Learning in Robotics

AI in Robotics: 6 Groundbreaking Applications

Sources:

The article was written using Copy.ai and based on a TechCrunch article

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28 October 2020
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DRONE PROTECTION SYSTEM

Reading Time: 2 minutesOver past years, drones evolved from an underdeveloped dream to highly advanced tool that is widely available. As a result, many companies thought of different ways to implement drones to their businesses. The new idea brought by Amazon or UPS is being able to deliver goods using drone technology. Undoubtedly, this project seems to be a huge step forward of delivery service. Nonetheless, Nirupam Roy and Nakul Garg, a pair of engineers at the University of Maryland, are afraid that drone deliveries will be frequent target of vandalism. As we know, a well-aimed stone or tennis ball can easily take a drone down. Therefore, the men decided to build a lightweight self-defence mechanism which lets a drone get out of the way of missile.

Dr Roy and Mr Garg think that they can replace power-hungry radar antennae or spinning lidar units by harnesses sound waves and the Doppler effect. The pair plan to equip drones with diminutive loudspeakers which would broadcast an ultrasonic tone outward from the drone. Also, those tiny microphones would listen for reflection for incoming objects. All those actions would give a chance to recognise the incoming threat and thus permit the drone to get out of its way.

Dr Roy and Mr Garg called their new invention DopplerDodge drone defence system. To test it, they have been throwing objects of various sizes at it and found out that system detects objects from distances of up to four metres away. However, the two researchers will attempt to raise the detection range to 30 meters, what will make drones impossible to hit by any irritated neighbour.

 

 

Sources:

https://www.economist.com/science-and-technology/2020/02/27/as-delivery-drones-multiply-they-may-need-to-protect-themselves

 

 

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20 October 2019
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Lightning fast MIT Robot

Reading Time: 2 minutesMIT is worldwide known for its robotics research. In the past, they managed to create a robot breaking the world record in solving a Rubik’s Cube in only 0.38 seconds, the first four-legged robot to do a backflip, etc.
Sounds impressive, but with their newest development, they can’t just look cool, but it can be used in many ways and bring the robots on to the next level of productivity.
Picking up objects and flipping them around is easy for people. We do it every day, e.g., when we are trying to take notes at University, work, or at home. We pick up the pen and bring it into the right position and start writing. The same scenario is when we are eating a sandwich: We move it a little bit to bite from the other corner.
For our robot friends, however, it is tough to pick up things without either dropping them or destroying them and then to add the factor of turning the object they just mastered to hold? It sounds like a difficult task.
Therefore it took robots a long time to plan and calculate all the factors like geometry, friction, all the possibilities of how the object can be turned, etc. This whole process took tens of minutes previously, which sounds still impressive, bearing in mind that if we measured and calculate these numbers, we would sit there for hours and probably still fail.
MIT mastered to bring down the planning time of the robot to less than a second.
How is that possible? The robot is pushing the object against a stationary surface and slides its claw down the object until it has it in the right position.

For the future, this can mean that instead of a specialized tool like a screwdriver, machines would have more something like a hand, giving them the ability to pick up different kinds of tools and do various tasks.
This improvement would most likely save the companies space and also money since, for multiple steps, they would need one robot.

This is another case were thinking out of the box, by simply using the surroundings, has a huge effect.

References:

https://bigthink.com/technology-innovation/rubiks-cube

http://news.mit.edu/2019/mit-mini-cheetah-first-four-legged-robot-to-backflip-0304

https://techcrunch.com/2019/10/17/mit-develops-a-way-for-robots-to-grasp-and-manipulate-objects-much-faster/

http://news.mit.edu/2019/robotic-faster-grip-adjust-1017

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25 January 2019
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The biggest problem of the ecommerce fashion industry

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Year by year we can observe a huge increases in fashion sales. Online sales are slowly becoming the most important part of fashion companies’ revenues. But the Internet creates kind of a wall, which is slowing the whole process and contains us from successful shopping.

In the recent years, fashion and apparel has become the fastest growing e-commerce category and the second largest after consumer electronics, growing form $375.6 billion in 2012 to $481 billion last year. Analysts predicts that the revenue will reach over $700 billion by 2022.

Buying clothes online means that we are not able to try them on and make sure the size fits us. This is why fashion companies have to struggle with a huge percentage of returned products (especially in the winter holiday season), which is something between 30-50% of the whole sales, which causes tremendously high costs connected not only with a return shipping, but also other processes needed to get the product back to the stock. Returns are growing even faster than the revenues, increasing 94.8% over the most recently measured five-year period. Moreover, over 4B pounds of returned apparel end up in landfills annually.

“What we hope to do with technology is make that growth rate slower.” Said Roger Graell, director of e-commerce at Spain’s Mango, which expects to make at least one-fifth of sales online by 2020.

Since 2012 there was plenty of startups which were trying to solve the problem with size fitting. Millions of dollars were invested in projects which were making kind of virtual fitting rooms (Avametric, Fitiquette), 3D scanners (Body Labs, Nettelo) and AI based algorithms (Virtusize, Pixibo). Most of them do not survive these days.

However, in 2016 three Ukrainian engineers created a technology, which allows people to scan their bodies using a smartphone’s camera. Their startup (called 3DLook) is implementing its technology to fashion retailers’ online stores. Thanks to that customers are able to measure themselves by taking 2 photos (from the front and from the side) and thanks to that can find out what is the perfect size for them. Technology is simply comparing customer’s measurement with producer’s size table.

3DLook claims to use advanced machine learning, AI and image processing to achieve their results. Recently, they got funding of $1 million for further development. Their technology can be a huge step forward successful apparel online shopping and, according to my research, fashion retailers hope it to be so.

And what is your opinion on this case? How many times did you buy a wrong size of some new clothes? Do you think that such technology is going to solve this problem? Let me know down below!

 

Sources:

https://www.reuters.com/article/us-onlineapparel-returns-focus/online-clothing-retailers-hunt-for-better-fit-to-cut-costly-returns-idUSKCN1OK1E2

https://www.ted.com/talks/aparna_mehta_where_do_your_online_returns_go?language=en#t-98979

https://www.retaildive.com/news/forrester-online-fashion-spending-outpaces-overall-digital-sales/522227/

https://independentretailer.com/2018/12/27/ecommerces-serial-return-problem/

https://www.businessoffashion.com/articles/fashion-tech/can-technology-solve-fit-problem-fashion-e-commerce

https://www.shopify.com/enterprise/ecommerce-fashion-industry

https://techcrunch.com/2018/07/30/body-scanning-app-3dlook-raises-1-million-to-measure-your-corpus/

https://3dlook.me

 

 

 

 

 

 

 

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9 January 2019
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A NEW GREEN REVOLUTION TO BEGIN?

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Tobacco plants that grow as much as 40% larger than usual have been designed by genetic engineers in Illinois. This is the first step in producing larger plants like potatoes which could be distributed to the poor countries throughout the world.

The genetically modified plants are wider and taller from the regular ones. As a team at the University of Illinois and the United States Department of Agriculture said, they gave tobacco something called “photorespiratory bypass”. It is a kind of a cassette of genetic changes that let the plants turn sunlight into energy more efficiently.

What is interesting, in 2016 University of Illinois bioengineers managed to make tobacco plants grow 20% bigger by helping them respond more quickly to changes in light and shade. This time actually they made plants that use less energy during photorespiration, which is a process that plants need to clear out a toxin called glycolate created during photosynthesis. To achieve this, the team had to add about 16,000 letters of novel DNA instructions to tobacco plants.

You are probably wondering why all of these experiments were carried out with the use of tobacco. Not only do tobacco plants grow quickly, but also are easy to genetically modify. The team is known to be already working on similar gene changes to potatoes, soybeans , and cowpeas.

However, as Heike Sederoff, a plant scientist at North Carolina State University claims, there is a long way for researchers to prove that these modifications actually work. He believes that we should not expect such plants in the markets earlier than 20 years from now.

 

Nevertheless, there are plenty of philanthropes and foundation which are hoping that genetic engineering could lead to a huge step forward in farm productivity. Scientists at Illinois were donated by more than $80 million by the Bill & Melinda Gates Foundation.

The goal of the whole project, which is called RIPE (Realizing Increased Photosynthetic Efficiency) is to increase the yields of staple food crops and improve global food security. As we can find out from their website, the Green Revolution’s advances have already reached their biological limits. They find it crucial to keep pace with this century’s growing population (which is forecasted to grow to nearly 10 billion by 2050) and provide food to the biggest possible number of people by making new innovations to agriculture industry.

There are also other organizations trying to find new solutions, like C4 consortium led by Paul Quick at the International Rice Research Institute (IRRI) in the Philippines, which is developing a process called C4 photosynthesis. It boosts plants’ growth by capturing carbon dioxide and concentrating it in specialized cells in the leaves, which allows the photosynthetic process to operate much more efficiently. Researchers claim that engineering C4 photosynthesis into rice and wheat could increase yields per hectare by roughly 50 percent. Alternatively, they believe that it would be possible to use far less water and fertilizer to produce the same amount of food.

What are your thoughts on this topic? Would you buy such genetically modified plants without any concerns? Let me know down below!

 

Sources:

https://www.technologyreview.com/s/535011/supercharged-photosynthesis/

https://ripe.illinois.edu/objectives/our-story

https://www.technologyreview.com/s/612710/gene-engineers-make-super-sized-plants-that-are-40-larger/

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2 December 2018
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RIVIAN – THE ULTIMATE VEHICLE?

Reading Time: 3 minutes

Last week, on the 2018 Los Angeles Auto Show, we could witness a birth of a new player in the electric cars’ game – Rivian.

To start with, I’ll explain what is hiding under this rather modern name. Rivian’s owner describes his products (because they already launched two, not one) as “The world’s first Electric Adventure Vehicles”. As it turned out, they’ve been working on their project for the last seven years in their headquaters in California (obviously), developing their product with the smallest details and avoiding mistakes previously made by competitors.

So, they made two models – a seven-seat SUV called R1S and R1T, which is a five-seat pick-up. Both of them are based on Rivian’s invention named ‘skateboard’ platform (sounds cool, doesn’t it?), which basically means that parts like the battery, suspension or drive units are placed below the height of each wheel. Not only does it provide much more passenger space, but also a low centre of gravity, which might make the difference in the car that weights 2.6-tonnes.

Potential buyers can opt for three battery sizes: 180kWh (400 mile range) and 135kWh (300 mile range) and a 105kWh battery (230 mile range). Rivian also promises that their products’ interiors are provided with the best quality materials, such as actual wood on the dashboard.

Right, but it all sounds like we’ve already heard that story about a complete revolution in the electric cars industry, best materials, biggest batteries and even bigger numbers attached. Rivian’s owner, RJ Scaringe claims  that he “started Rivian to deliver products that the world didn’t already have.” So, what is so special about this brand?

First of all, their cars are provided with kind of artificial intelligence of the new level. While driving the Rivian, an algorithm constantly analyze your driving style and learn how to better optimize its charge. Welcome to the future!

 

As the Electric Adventure Vehicles, they are expected to provide you some load space, right? Well, in the R1T, there’s 330-litres under the hood (because there’s no engine inside), a 350-litre space in the R1T’s so called ‘gear tunnel’, and a 200-litre bin beneath the rear bed floor.

 

Moreover, both models are equipped with tools like cameras, lidar, radar, ultrasonic and GPS, which allow for ‘Level 3’ autonomy. What does it exactly mean? In Level 3 cars, you’re still needed as a driver, but you are able to transfer safety-critical functions to the vehicle, depending on traffic and other conditions, so they are really close to producing fully autonomous cars with a highest Level 5 autonomy.

And the last but not least – speed. These things are TREMENDOUSLY quick. Both are claimed to reach 60mph in 3 seconds. In a car of this weight, these numbers are far more than impressive.

 

You can already order both of Rivian’s models. The pick-up is expected to be delivered in 2020, and the SUV no earlier than a year after.

So, what do you think about the whole project, apart from the these cars’ names which you probably associate more with a Star Wars’ characters rather than cars? Will Rivian find its niche or make a huge loss? Let me know below!

 

References:

https://www.topgear.com

https://cleantechnica.com/2017/12/02/autonomous-driving-levels-0-5-implications/

 

 

 

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8 November 2015
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Hyper-fast charger UsBidi collected on Kickstarter 14 times more than required

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A few days charging cable is funded almost 8000 Becker, who invested in UsBidi half a million Australian dollars (about $ 360,000). But the campaign will last 27 days, and most likely, “smart” charger collect half a million in US currency. The goal of the developers was to gather Sydney 35 000 Australian dollars, and at the moment they already have 14 times the amount.

UsBidi creators claim that the speed of charging by USB while using the product is doubled. The device charges more quickly due to the fact that unlike other chargers. UsBidi can not synchronize the data simultaneously with the battery charge. To do this, UsBidi a special button that allows you to turn off synchronization and double the speed of the charge.

Charging is automatically turned off when fully charged, thus protecting it from overheating, and short-term cycles, reduces the service life. Indicator lights UsBidi will know about the status of charging and convenient magnetic mount will lock the cable.

The cable works with smartphones and tablets. Also, the buyer can choose the version with the Lightning connector for Apple devices or for Micro USB devices working on Android. The length of the charging cable can be 30, 90 or 180 cm.

Approximate price for UsBidi would be $18 (+ shipment). Thus the buyer can select the connector, color and length.

More info: UsBidi

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