Showing posts with label presentations. Show all posts
Showing posts with label presentations. Show all posts

Thursday, September 18, 2025

From Engaging Sales Videos To Entertaining Short Form Content With AI Video Suite

 

Credit to: arminhamidian

AI Video Suite is a comprehensive video generation suite that allows users to create professional videos for various purposes. Whether you are creating sales videos, presentations, short form videos for social media, or online ads, AI Video Suite has got you covered. Let’s dive into the details and see what this software has to offer.

AI Video Suite is not your typical AI-generated video or image tool. It is a focused and professional video generation suite that provides users with a wide range of templates and designs to create high-quality videos. With AI Video Suite, you can create sales videos, presentations, short form videos for platforms like TikTok and Instagram online ads, and much more. The software offers impressive done-for-you and vertical templates that are popular on social media apps.

The AI Video Suite offers several powerful tools to create stunning videos. The main tools include the AI video maker, AI Image Generator and AI voiceover. With the AI video maker, you can easily generate videos using various templates. The AI image generator allows you to create images that can be imported into your videos. The AI voiceover feature provides professional-sounding voiceovers using AI technology. You can also add text subtitles, overlays, and use stock photos to enhance your video content.

One of the impressive aspects of AI Video Suite is the wide variety of templates and designs it offers. These templates are designed to cater to different video styles and purposes. Whether you need a sales video presentation, a business ad, a squeeze page video, a product promo, an explainer tutorial video, or a simple informational video, AI Video Suite has the perfect template for you. The templates are customizable, allowing you to tailor them to your specific needs.

With AI Video Suite, you can create various types of videos to suit your needs. From engaging sales videos to entertaining short form content for platforms like TikTok and Instagram, the possibilities are endless. The software provides vertical editing tools that ensure your videos are perfectly aligned for social media platforms. Whether you are aiming for informative or entertaining content, AI Video Suite has the capabilities to bring your vision to life.

Once you have generated your initial video using AI Video Suite, you have the flexibility to further edit and customize it according to your preferences. The step-by-step time stamp and script editor make the editing process seamless and systematic. You can easily make changes to the text, script, and even import images from the internet. The software allows you to access a database of stock images, use the AI image generator, and take advantage of the voiceover artist to create a fully customized video.

AI Video Suite is a valuable tool for marketers looking to enhance their video marketing efforts. The ability to create professional videos quickly and easily can significantly improve engagement and conversion rates. By including strong calls to action and utilizing the software’s editing features, marketers can create compelling videos that drive traffic and generate sales. With AI Video Suite, marketers can tap into the power of AI technology to create impactful video content.

In conclusion, AI Video Suite is a comprehensive video generation suite that offers a range of features and tools for creating professional videos. Whether you are a marketer, content creator, or business owner, this software can help you create engaging and high-quality videos for various purposes.

With its impressive templates, advanced editing options, and innovative use of AI technology, AI Video Suite is a valuable asset in the video marketing landscape. Explore the possibilities of AI Video Suite and unlock the potential of video marketing….

See more details here…

Source: https://aivideosuite.com

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Source: AiVideoSuite

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Monday, July 21, 2025

AI Cuts Costs Adds 13 Hours For SMB Marketers

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According to a new survey from marketing technology firm Active Campaign, small-business marketers are getting back quite a bit of time from their investment in AI. AI-powered marketing systems are giving SMBs the equivalent of a full extra workday each week, and saving almost $5,000 a month in the process. But as of today, only one in four teams is applying AI throughout their full marketing processes……..Continue reading….

By Ron Schmelzer

Source: Forbes

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Critics:

Knowledge representation and knowledge engineering allow AI programs to answer questions intelligently and make deductions about real-world facts. Formal knowledge representations are used in content-based indexing and retrieval, scene interpretation, clinical decision support, knowledge discovery (mining “interesting” and actionable inferences from large databases), and other areas.

A knowledge base is a body of knowledge represented in a form that can be used by a program. An ontology is the set of objects, relations, concepts, and properties used by a particular domain of knowledge. Knowledge bases need to represent things such as objects, properties, categories, and relations between objects; situations, events, states, and time; causes and effects.

Knowledge about knowledge (what we know about what other people know); default reasoning (things that humans assume are true until they are told differently and will remain true even when other facts are changing); and many other aspects and domains of knowledge.

Among the most difficult problems in knowledge representation are the breadth of commonsense knowledge (the set of atomic facts that the average person knows is enormous); and the sub-symbolic form of most commonsense knowledge (much of what people know is not represented as “facts” or “statements” that they could express verbally). There is also the difficulty of knowledge acquisition, the problem of obtaining knowledge for AI applications.

An “agent” is anything that perceives and takes actions in the world. A rational agent has goals or preferences and takes actions to make them happen. In automated planning, the agent has a specific goal. In automated decision-making, the agent has preferences there are some situations it would prefer to be in, and some situations it is trying to avoid. The decision-making agent assigns a number to each situation (called the “utility”) that measures how much the agent prefers it.

For each possible action, it can calculate the “expected utility”: the utility of all possible outcomes of the action, weighted by the probability that the outcome will occur. It can then choose the action with the maximum expected utility. In classical planning, the agent knows exactly what the effect of any action will be. In most real-world problems, however, the agent may not be certain about the situation they are in

(it is “unknown” or “unobservable”) and it may not know for certain what will happen after each possible action (it is not “deterministic”). It must choose an action by making a probabilistic guess and then reassess the situation to see if the action worked. In some problems, the agent’s preferences may be uncertain, especially if there are other agents or humans involved. These can be learned (e.g., with inverse reinforcement learning), or the agent can seek information to improve its preferences. 

Information value theory can be used to weigh the value of exploratory or experimental actions. The space of possible future actions and situations is typically intractably large, so the agents must take actions and evaluate situations while being uncertain of what the outcome will be. Machine learning is the study of programs that can improve their performance on a given task automatically. It has been a part of AI from the beginning. There are several kinds of machine learning.

Unsupervised learning analyzes a stream of data and finds patterns and makes predictions without any other guidance. Supervised learning requires labeling the training data with the expected answers, and comes in two main varieties: classification (where the program must learn to predict what category the input belongs in) and regression (where the program must deduce a numeric function based on numeric input).

In reinforcement learning, the agent is rewarded for good responses and punished for bad ones. The agent learns to choose responses that are classified as “good”. Transfer learning is when the knowledge gained from one problem is applied to a new problem. Deep learning is a type of machine learning that runs inputs through biologically inspired artificial neural networks for all of these types of learning. Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization.

Affective computing is an interdisciplinary umbrella that comprises systems that recognize, interpret, process, or simulate human feeling, emotion, and mood.For example, some virtual assistants are programmed to speak conversationally or even to banter humorously; it makes them appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human–computer interaction.

However, this tends to give naïve users an unrealistic conception of the intelligence of existing computer agents. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal sentiment analysis, wherein AI classifies the effects displayed by a videotaped subject.

AI can solve many problems by intelligently searching through many possible solutions. There are two very different kinds of search used in AI: state space search and local search. State space search searches through a tree of possible states to try to find a goal state. For example, planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.

Simple exhaustive searches are rarely sufficient for most real-world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes. “Heuristics” or “rules of thumb” can help prioritize choices that are more likely to reach a goal.Adversarial search is used for game-playing programs, such as chess or Go. It searches through a tree of possible moves and countermoves, looking for a winning position.

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