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Although copywriting is an essential part of any business, online or offline it can also be a significant investment. You can either hire many freelancers or have your own copywriters. It is a large investment and takes time. Potential customers will see your brand through emails, product descriptions, and articles.
Low-quality content can lead to negative results. Employees have the option of using AI software to write copy. These software programs are not only cheaper but also more efficient than traditional copywriting software. The question is: Is AI software the future of content marketing and copywriting?What’s copywriting?
Before we get into AI (” AI”) or copywriting, let’s first explain what copywriting is. To create written content for companies, copywriting is essential. This style of writing is more focused on selling products than entertaining.
Copywriting is also a part of SEO. SEO uses specific keywords to create articles that can be found easily on search engines such as Google.
A copywriter’s job is to create high-quality content to sell the product or service, while also using SEO skills to rank the article highly on search engines. This sounds like a job you would love to do. It is! It is!Can AI be the future of Copywriting?
Both yes and no. Yes and no! While AI offers many potential uses in copywriting, it is lacking variety at the moment. AI software programs might be helpful if your goal is to create as much generic content as possible.
However, if you are trying to reach a large audience and get them to continue reading, AI is not the right tool for you. AI has been proven to be successful in some areas, including AI trading robots that automate trades.No emotions
AI can only implement specific keywords and information. It cannot apply emotions. If your goal is to sell products you must have articles that elicit an emotional response from the reader. This is something that only a skilled copywriter can achieve.Just reproduction
AI software programs cannot reproduce content because they only take information and phrases from existing articles. Although they may have put these phrases together in a unique way, it is still reproduction at the end.This must be double-checked
Also read: 30+ Loan Apps Like MoneyLion and Dave: Boost Your Financial Emergency (#3 Is Popular 🔥 )AI copywriting can be used for product descriptions
Does that mean AI copywriting doesn’t work? AI copywriting programs can be used to create product descriptions. This is especially true if there are many products. These descriptions don’t require emotion or exceptional writing skills. Instead, you should simply give basic information about your product.
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The racial profiling and police brutality in the George Floyd incident and #BlackLivesMatter protests and rioting unfolded debate on many levels. One of them is flaws in Artificial Intelligence that end up creating a racial bias in technology which is deemed as an instrumental force to bring digital age. However, all hope is not lost, especially in the Australian start-up sector. Presenting, Akin and Unleash Live, AI-backed companies founded by Liesl Yearsley and Hanno Blankenstein respectively. While Akin, uses AI to build bots that can converse with humans in a lifelike way, Unleash Live employs AI for real-time analysis of video footage coming from security cameras and drones. Both of these companies were founded with a common mission to have an excellent ethical AI culture as well as one that caters to good business policies. Therefore, neither of the companies uses any personal information to manipulate nor ‘keep an eye on’ the public. Yearsley had previously sold her AI company, Cognea Artificial Intelligence, to IBM in 2014. Later after a brief hiatus, she came out of her retirement to establish Akin. She learned about the ability of AI to manipulate human behavior to questionable or unsustainable ends while building customer service bots at Cognea. This happens because, unlike humans, AI bots are programmed to optimize themselves towards a goal continuously. “AI, it turns out, has a frightening ability to bring about change in human behavior,” she says. “Those customer service bots are used for tasks such as encouraging consumers to take on more credit card debt, were already capable of altering people’s behavior by anywhere between 30 percent and 200 percent,” she adds. Meanwhile, Sydney based Unleash Live, denied to be involved in using AI that uses personal information. Instead, its AI analyzes video feed inputs from a security camera to help a city decide whether footpaths should be made wider or to detect that hordes of people are running from some incident that law enforcement or emergency services should be notified about. The company’s selling point is that it will never collect nor analyze personal information. Hence it is free from the possibility to be used by the government to identify people. In an interview with the Australian Financial Review, Blankenstein said, “Not only were the ethical implications of mass, computer-based surveillance too troubling but, in any event, that market was already saturated with powerful companies that were all too willing to provide the technology to governments and police forces around the world.” Such a type of ethical use of AI is crucial now than ever. After the outrage over George Floyd’s murder in Minneapolis, Minnesota, leaders in technology corporations are contemplating whether to continue providing services like facial recognition to the law enforcement unit. After such eye-opener incidents, which include recent mess by Microsoft’s editor AI , tech majors like IBM, Amazon, and Microsoft have currently withdrawn their AI services for mass surveillance , last week. The call for a humanistic, neutral, or lesser biased AI is louder and clear than ever. The pressure is mounting on government across the US and other regions to come up with better regulation for AI. According to Toby Walsh, professor, the University of NSW, “Such AI-based surveillance systems are now at risk of becoming ‘toxic assets’ for the companies that develop and sell them, to the point where many companies will be forced to abandon the technology altogether. He further points out that face recognition along with other misuses of surveillance is going to be a topic that will trouble us increasingly. Australia’s Human Rights Commissioner, Edward Santow firmly believes that Australia can position itself also position itself as a supplier of ethically safe technology, especially when it comes to artificial intelligence. Just like it did in positioning itself as a supplier of safe food to the world. And with companies like Akin and Unleash Live, this future is not far from becoming a reality.
As the name suggests, Generative AI means a type of AI technology that can generate new content based on the data it has been trained on. It can generate texts, images, audio, videos, and synthetic data. Generative AI can produce a wide range of outputs based on user input or what we call “prompts“. Generative AI is basically a subfield of machine learning that can create new data from a given dataset.
If the model has been trained on large volumes of text, it can produce new combinations of natural-sounding texts. The larger the data, the better will be the output. If the dataset has been cleaned prior to training, you are likely to get a nuanced response.
Similarly, if you have trained a model with a large corpus of images with image tagging, captions, and lots of visual examples, the AI model can learn from these examples and perform image classification and generation. This sophisticated system of AI programmed to learn from examples is called a neural network.
At present, GPT models have gotten popular after the release of GPT-4/3.5 (ChatGPT), PaLM 2 (Google Bard), GPT-3 (DALL – E), LLaMA (Meta), Stable Diffusion, and others. All of these user-friendly AI interfaces are built on the Transformer architecture. So in this explainer, we are going to mainly focus on Generative AI and GPT (Generative Pretrained Transformer).What Are the Different Types of Generative AI Models?
Amongst all the Generative AI models, GPT is favored by many, but let’s start with GAN (Generative Adversarial Network). In this architecture, two parallel networks are trained, of which one is used to generate content (called generator) and the other one evaluates the generated content (called discriminator).
Basically, the aim is to pit two neural networks against each other to produce results that mirror real data. GAN-based models have been mostly used for image-generation tasks.
GAN (Generative Adversarial Network) / Source: Google
Next up, we have the Variational Autoencoder (VAE), which involves the process of encoding, learning, decoding, and generating content. For example, if you have an image of a dog, it describes the scene like color, size, ears, and more, and then learns what kind of characteristics a dog has. After that, it recreates a rough image using key points giving a simplified image. Finally, it generates the final image after adding more variety and nuances.What Is a Generative Pretrained Transformer (GPT) Model
Google subsequently released the BERT model (Bidirectional Encoder Representations from Transformers) in 2023 implementing the Transformer architecture. At the same time, OpenAI released its first GPT-1 model based on the Transformer architecture.
Source: Marxav / commons.wikimedia.org
So what was the key ingredient in the Transformer architecture that made it a favorite for Generative AI? As the paper is rightly titled, it introduced self-attention, which was missing in earlier neural network architectures. What this means is that it basically predicts the next word in a sentence using a method called Transformer. It pays close attention to neighboring words to understand the context and establish a relationship between words.
Through this process, the Transformer develops a reasonable understanding of the language and uses this knowledge to predict the next word reliably. This whole process is called the Attention mechanism. That said, keep in mind that LLMs are contemptuously called Stochastic Parrots (Bender, Gebru, et al., 2023) because the model is simply mimicking random words based on probabilistic decisions and patterns it has learned. It does not determine the next word based on logic and does not have any genuine understanding of the text.How Google and OpenAI Approach Generative AI?
Both Google and OpenAI are using Transformer-based models in Google Bard and ChatGPT, respectively. However, there are some key differences in the approach. Google’s latest PaLM 2 model uses a bidirectional encoder (self-attention mechanism and a feed-forward neural network), which means it weighs in all surrounding words. It essentially tries to understand the context of the sentence and then generates all words at once. Google’s approach is to essentially predict the missing words in a given context.
In contrast, OpenAI’s ChatGPT leverages the Transformer architecture to predict the next word in a sequence – from left to right. It’s a unidirectional model designed to generate coherent sentences. It continues the prediction until it has generated a complete sentence or a paragraph. Perhaps, that’s the reason Google Bard is able to generate texts much faster than ChatGPT. Nevertheless, both models rely on the Transformer architecture at their core to offer Generative AI frontends.Applications of Generative AI
We all know that Generative AI has a huge application not just for text, but also for images, videos, audio generation, and much more. AI chatbots like ChatGPT, Google Bard, Bing Chat, etc. leverage Generative AI. It can also be used for autocomplete, text summarization, virtual assistant, translation, etc. To generate music, we have seen examples like Google MusicLM and recently Meta released MusicGen for music generation.
Apart from that, from DALL-E 2 to Stable Diffusion, all use Generative AI to create realistic images from text descriptions. In video generation too, Runway’s Gen-1, StyleGAN 2, and BigGAN models rely on Generative Adversarial Networks to generate lifelike videos. Further, Generative AI has applications in 3D model generations and some of the popular models are DeepFashion and ShapeNet.Limitations of Generative AI
While Generative AI has immense capabilities, it’s not without any failings. First off, it requires a large corpus of data to train a model. For many small startups, high-quality data might not be readily available. We have already seen companies such as Reddit, Stack Overflow, and Twitter closing access to their data or charging high fees for the access. Recently, The Internet Archive reported that its website had become inaccessible for an hour because some AI startup started hammering its website for training data.
Apart from that, Generative AI models have also been heavily criticized for lack of control and bias. AI models trained on skewed data from the internet can overrepresent a section of the community. We have seen how AI photo generators mostly render images in lighter skin tones. Then, there is a huge issue of deepfake video and image generation using Generative AI models. As earlier stated, Generative AI models do not understand the meaning or impact of their words and usually mimic output based on the data it has been trained on.
It’s highly likely that despite best efforts and alignment, misinformation, deepfake generation, jailbreaking, and sophisticated phishing attempts using its persuasive natural language capability, companies will have a hard time taming Generative AI’s limitations.
Commercial aviation alone contributes around three percent of total global carbon emissions. But the industry is actively looking for green solutions in the form of sustainable jet fuel, and in one case, that fuel may have had a previous life as your household food scraps.
In a study released this week in the Proceedings of the National Academy of Sciences, a team of researchers from the National Renewable Energy Laboratory in Golden, Colorado and other institutions details a method of converting food waste into sustainable jet fuel that can be used in existing engines.
Biomass, such as manure and food waste, can be converted into biofuels, which are renewable liquid fuels made from organic matter. Ethanol and biodiesel are two common types of renewable biofuel, but making sustainable aviation fuel is a more complicated process—it’s got to be so similar to the petroleum-based jet fuel we use today so it can “drop-in” existing engines and aircraft. Reimagining the airplane engine to run on different types of fuel will take time, so the goal is to design a fuel that can be used now.
The researchers were able to use volatile fatty acids from fermenting old food waste and convert it to simple paraffin molecules that can be used in fuel and really aren’t all that different chemically from traditional fossil fuels. Other sustainable aviation fuels have been made from biomass, specifically oil, fat, and grease from vegetables and animals, but using our ever-mounting pile of food waste to fuel flight broadens those possibilities.
Derek Vardon, a senior research engineer at the National Renewable Energy Laboratory and study author, and Nabila Huq, a postdoctoral researcher at NREL and the lead author of the study say that their fuel worked at the 90 percent conventional petroleum jet fuel, 10 percent alternative jet fuel blend that is required from the industry currently. They also showed they could push it to a 70/30 blend, but more time and testing are required to allow that ratio in the real world.
Vardon says major companies are eager to get involved in sustainable aviation fuel because some sustainable solutions, such as battery-operated commercial planes, just aren’t possible yet with current battery technology. A battery-powered plane would be too heavy to fly long distances—so fuel that works in the same way as the fuel we have is a simpler way to trade out emissions-heavy fossil fuels.
“There’s been an explosion in the last year of companies looking to use sustainable aviation fuel, like, Microsoft, Amazon, FedEx,” he says.
Vardon says that because the wet waste used in the process would normally go to a landfill and break down to release greenhouse gases, the process of making and using sustainable aviation fuel could actually have a negative carbon footprint when scaled up.
“To do that and to meet the requirements for a sustainable aviation fuel is actually not trivial,” he says. He compares being able to make sustainable jet fuel as hitting a “bullseye” on a dartboard because of tight regulations for safety and performance.
Not only do these sustainable fuels put the industry on a path toward net-zero emissions, but they also produce fewer sooty aerosols when burning. Soot particles provide the nucleus for clouds known as contrails to form from plane exhaust, which then serves as a “blanket” and trap heat in the atmosphere.
To get this product into the market in the near term, Huq says they tried to “match the specifications of existing conventional jet fuel without trying anything too fancy as far as modifications.”
Commercial airlines are on board to find an affordable and sustainable solution to the carbon-intensive process of air travel. Airlines are looking to hit aggressive sustainability goals by 2050, including decreasing net carbon dioxide emissions by 50 percent. “We’re excited to partner with NREL as we continue our journey to make commercially-viable SAF [sustainable aviation fuel] a reality and a part of Southwest’s future,” Michael AuBuchon, Southwest’s senior director of fuel supply chain management, said in a statement.
A major question as we go forward in this type of research is if it is possible to run an airplane engine on fully renewable fuel. Rolls Royce recently did a demonstration on one of their company’s engines at 100 percent sustainable airline fuel and it worked. “This [fuel] is not crazy and we can solve these problems,” Vardon says.
Online education that has reached its zenith during the lockdown has been progressing at a much faster pace because learners have realized its hidden benefits. And at the same time, Ed-tech has become a crowded market, lending impossible for students and parents alike difficult to choose the right platform. TeacherOn, a modern age Edtech platform promises incomparable learning experiences with seamless integration of teacher-student communication. Analytics Insight has engaged in an exclusive interview with Arun Verma, Founder & CEO of1.Kindly brief us about the company, its specialization, and the services that your company offers
We are a cutting-edge platform that connects students and teachers for both online and in-person tutoring. While I was in London in 2009 looking for work, I realized on a cold November morning that, despite technological developments, many gaps were still prominent in the education industry. So, I decided to construct a platform to solve the common problem of connectivity for both students and teachers, and I began executing the concepts through coding. We later encountered certain technological issues, and after overcoming various difficulties, we launched the platform TeacherOn in 2010.2.With what mission and objectives, the company was set up? In short, tell us about your journey since the inception of the company.
We started the brand with the core belief of providing quality education with ease of access to the information and tutors at their fingertips. With the goal of creating a free and user-friendly website for all participants, we provide over 10,000 subjects having a worldwide reach, and also supply local teachers so that students can locate them in their communities and teachers may reach out to relevant students to deliver their services. Apart from tutoring, we also offer online assignment help to students. Furthermore, we envision the exponential growth of the brand by providing education all around the world, regardless of geographical location.3.What is your biggest USP that diﬀerentiates the company from competitors?
The websites that have teachers from all around the world solely provide online tutoring. We are the only website that lists teachers for offline instruction all around the world. The majority of websites have 5-10 key topics. Others go all the way up to 100. We have over 10,000 subjects in our database. All websites charge a certain fee for their services. Our pricing varies depending on where you are – in the city or beyond. This enables us to serve poor locations at no cost. We are open and honest about how we work, how the program functions, how we make judgments, and what our limitations are. Currently, the platform has more than 500,000 unique visitors every month.4.How do you see the company and the industry in the future ahead?
E-learning was an industry that was growing exponentially, especially picking up pace over the last five years or so. However, the sudden emergence of the pandemic prompted all schools to opt for online education to avoid educational and learning loss for children. Over the years, numerous technologies and e-learning trends have driven profound changes in the education space. EdTech has witnessed an upsurge and is expected to be the new normal in the future as well.5.What are the future and business plans of TeacherOn?
So far, starting as a bootstrap company we have cherished our journey by overcoming every obstacle in our path. Being a cutting-edge platform to build connections between teacher and student, we are on a mission to make every teacher searchable for students, locally and globally. We also strive to link students with teachers within 24 hours of posting the criteria. We are creating a marketplace where teachers can sell their materials, such as question papers, PowerPoint presentations, and answer keys. In the times ahead, we will begin using cryptocurrency for accepting payments and for paying teachers all over the world. Furthermore, we are also planning to begin live tutoring, where a student can receive assistance from a live tutor anytime. Starting with Maths it will gradually expand to include other subjects. Additionally, we intend to expand our footprints in previously untapped global markets in the coming years. As CEO and Founder of TeacherOn, I believe in the concept of excellence, and for students to become pioneers in their professions, they must be imbibed with quality education, as well as easy access to information and instructors.6.Kindly brief us about your role at Edtech platform – TeacherOn and your journey in this highly promising sector.
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