Category robots in business

With industrial robots’ hourly operating rate of $2500, the financial impact of their failure can escalate quickly. Thus, it is crucial to avoid such technical disruptions in operations, whether it involves one singular robotic arm or several hundred industrial robots.

Engineers have developed a new system for full-body motion capture -- and it doesn't require specialized rooms, expensive equipment, bulky cameras or an array of sensors. Instead, it requires only a smartphone, smartwatch or earbuds.

Researchers explore how the domesticated flightless silkworm moth (Bombyx mori), a prominent insect model in olfactory research, uses wing flapping to manipulate airflow, enhancing their ability to detect distant pheromones. These findings highlight how moths guide pheromones to their odor sensors in antennae, and suggest potential applications for designing advanced robotic systems for odor source localization. This could inspire future innovations in drones and provide design guidelines for robots to locate odor sources.

Researchers have unveiled a vision system inspired by feline eyes to enhance object detection in various lighting conditions. Featuring a unique shape and reflective surface, the system reduces glare in bright environments and boosts sensitivity in low-light scenarios. By filtering unnecessary details, this technology significantly improves the performance of single-lens cameras, representing a notable advancement in robotic vision capabilities.

Penn Engineers have developed a new algorithm that allows robots to react to complex physical contact in real time, making it possible for autonomous robots to succeed at previously impossible tasks, like controlling the motion of a sliding object.

The silkworm moth (Bombyx mori) is an insect that no longer flies due to domestication. The males use their antennae to detect pheromones emitted by females and respond very acutely, and have been used as model insects for the study of their odor source localization.

How Much does Artificial Intelligence Cost?

Artificial Intelligence Cost

Today, the development of technology is rising at an exponential rate, bringing with it many possibilities and possibilities. Artificial Intelligence is one of the most buzzed words in companies in 2020 with a plethora of AI-based solutions that change business processes in several industries.
The benefits promised by Artificial Intelligence bring life-changing consequences. Time-consuming tasks in the past could be quickly completed with AI capabilities. With machine learning, AI continues to increase the accuracy and quality of outcomes.

AI can develop efficient business models, test abstract models while reducing the cost of labor.
AI systems have already designed for several sectors in the economic world. These AI solutions have been used to streamline business processes, expand employee productivity, optimize communication channels, manage customer contacts, and remove wastage.

The potential of AIs has encouraged large enterprises to consolidate their business to take advantage of these opportunities. On the other hand, small business owners have been stifled due to the expenses involved.

Related Links:

• Top Benefits And Risks Of Artificial Intelligence

• The Significance Of AI Technology By Industry

Future of Artificial Intelligence

AI technology is becoming widespread in our lives as many companies in different industries widely accept it. By providing creative insights and automating the daily routine works, every sector from manufacturing and logistics to healthcare and retail is reaping the benefits of Artificial Intelligence.

E-commerce platforms leverage AI algorithms to simplify the buying process and personalise their offers based on customer behaviour. with such a wide range of use cases all sectors, it is no wonder that Artificial Intelligence has an increasingly expanding market.

According to the market analysis reports of Scoop Junction, ‘the AI market across the globe will develop from 19.631 billion US dollars in 2017 to 176.547 billion US dollars in the year 2024.
If you are worried about how to make an artificial intelligence program and how much does artificial intelligence cost, be cool! You are on the right page.

The entire process of developing Artificial-Intelligence based solutions has various key features that determine the total cost.

Here is an Expert Guide On AI Business Consulting

Continue Reading!

To know the Factors Affecting the Cost of AI Solution Development

• Data Issues

AI-system development does not only depend on the coding skills of the trusted AI development team but also depends on the quality of data for the training algorithm model, which plays a vital role in the success of the project.

Because of this, a large amount of information is needed to detect hidden patterns between input data and output features. If businesses do not need enough data, it may be possible to obtain data from external services or collect more data which may take much time. Using data augmentation is also a better solution to raise the sample size.

However, it leads to the increasing cost of development. In addition, the data should be cleaned and stored in the appropriate warehouse to reduce the cost. Otherwise, we should take appropriate measures to clean up the data that can expand the cost of AI solutions.

It is easy to work with structured data, as the outcome is cheap. Therefore, before initiating the project, it is better to conduct a smart review on the internal databases of clients to find its quantity and quality.

In many cases, companies also need to be accountable for dealing with errors, missing data, defects, outliers, etc. In fact, most organizations typically capture and manage highly structured data (ex: video, audio, communication such as social media posts, chats, etc.) and needed more complex and sophisticated machine learning algorithms to utilize this type of data. This kind of project usually costs more to develop AI solutions.

• AI Algorithm’s Performance

Adequate algorithm performance is one of the major cost-effective factors because a high-quality algorithm requires a round of tuning sessions. This eventually raises the overall cost of the project.

The Algorithm’s performance rate varies according to the client’s business objective and the cost of incorrect AI predictions machines.

The mediator takes advantage of a system that generates 55% correct estimates because it already determines profitability. However, a 90.9% system planned at diagnosing a disease, treating false-positive patients is fatal, which is not satisfactory so far.

Many people got a doubt that why should we care about algorithm performance and data processing speed to understand how costs to develop an AI project?

The answer is quite simple: all AI applications rely on data and use it for the knowledge gaining process. So, it will take much time if the date is processed slowly.

The development cost of Artificial Intelligence (AI) may depend on various crucial factors that comprise the scope and size of the AI project. So, it is great to have an idea of the phases of the AI project.

Let us have a look at the following four essential phases

• Discovery and Analysis Phase

The phase aims to research the project requirements, goals and the feasibility of the project. In the initial stage, it is most important to both technology partners and customer companies to find whether Artificial Intelligence solution is required for your business.

This starts with conducting in-depth research on client’s operation processes, data assets, and business metrics as well as finding out exactly how an organization can address their key business issues using Artificial Intelligence.

If AI technology is required for your business, you can then develop a project by predicting the scope of work required for further process, i.e. prototype development. If all the necessary data, business metrics and client’s processes are in the proper format, this phase will just take 5 to 7 days.

• Prototype Implementation and Evaluation Phase

A prototype is a method of creating a business model to examine the proof of concept and feasibility as well. This could be a limited, drawing or text-based mock-up or advanced code-based prototype.

This form will depend on the project tools and complexity and tools (application simulation programs, screen generators, or design tools) used to develop it. Prototypes are shown to and discussed with the client.

It is completely based on project complexity and tools like application simulation programs, screen generators, design tools that used to develop it.

Prototyping is an awesome technique that enables IT professionals to validate design choices and requirements. Prototypes are cheap to produce, and easy to adjust. The prize and threats associated with software implementation are significantly reduced, as the requirements are discussed before the development process begins.

USM Business System – ranked in the list of best mobile app development companies in the USA, is trying hard to make this phase as budget-friendly as possible for their clients.

• Minimum Viable Product (MVP)

Based on the prototype results, we develop a real MVP product in this phase. MVP is based on the client’s data and exposes it to a small group of real customers as a simplified version of the end product solution.

Feedback is very similar because at this stage, it is not that much expensive to revise the system than it was fully developed. Average MVP costs vary depending on project complexity and size.

• Product Release

In the final stage, a product with predefined features is developed and then introduced into the market. Previous steps place a high emphasis on requirements validation and elimination. Therefore, the final product is made with fewer errors. The cost of this final phase is usually predicted in previous steps.

Types of AI Use Cases

AI development can vary significantly depending on the type of system being created. Here are common AI applications:

• Chatbots and Virtual Assistants: Basic conversational tools or advanced assistants like ChatGPT.
• Predictive Analytics: Used for forecasting trends in industries like finance or supply chain.
• Image Recognition: For applications such as facial recognition, medical imaging, or security.
• Natural Language Processing (NLP): Powers text understanding, translations, and content generation.
• Recommendation Systems: Found in e-commerce platforms and streaming services.
• Autonomous Vehicles: Combines AI with sensors and IoT for self-driving technology.
• Fraud Detection: Applied in banking and finance to detect anomalies and prevent fraud.
• Robotics: For automation in industries like manufacturing, healthcare, or retail.
• Personalized Marketing: Tailors advertisements or content based on user behavior.
• Speech Recognition: Converts spoken words into text, as seen in virtual assistants.

Top 5 Factors Behind AI Costs

1. Type of Software: Simple chatbots are affordable, while custom AI models with deep learning cost significantly more.
2. Level of Intelligence: Basic AI vs. advanced systems requiring fine-tuned and context-aware models.
3. Data Quality and Quantity: The cost of collecting, cleaning, and managing data impacts the project budget.
4. Algorithm Accuracy: Higher precision algorithms demand more computational power and testing.
5. Solution Complexity: Integration of multiple components, APIs, or advanced functionality increases costs.

So, How Much Does AI Cost?

In today’s world, with the more availability of various frameworks and tools for developing Artificial Intelligence solutions, AI technologies are becoming more accessible to businesses.

A few years ago, only the multinational tech powerhouse companies like Microsoft, Google, and Amazon could afford their AI solutions for businesses. Now, most of the companies like USM Business Systems can leverage the power of machine learning technology at a very feasible price.

Depending on the needs, goals, and size of your company, AI solutions may have a higher cost, which offers enormous benefits. In the very soon, rather than later, your company can beat your competitors and take advantage of Artificial Intelligence, like by using dynamic pricing to increase revenue.

With the above-provided information, I hope you well understood how will artificial intelligence affect our lives and got an idea about how to make an artificial intelligence program.

AI Project Cost Breakdown

• Simple AI Models: Starting at $5,000 for basic implementations.
• Complex AI Solutions: Ranges from $50,000 to $500,000+, often involving deep learning and advanced algorithms.
• Industry-Specific AI Applications:
  • Healthcare AI: $20,000–$50,000.
  • Fintech AI: $50,000–$150,000, due to regulatory and security needs.

Additional Costs

• Hardware: High-speed servers and GPUs may cost upwards of $10,000.
• Software: Specialized tools or frameworks for AI development.
• Development Team: Rates depend on location, ranging from $25/hour (in some regions) to $150/hour (in the US/Western Europe).

I truly believe in the below words of David Gasparyan, a President of Phonexa.
“No company is going to survive in the future without implementing, or at least gaining an understanding of, artificial intelligence and how it can be used to better grasp data they collect.” 
If you believe the same and would you like to have an AI solution for your business, Please reach us
We are happy to assist you!

In collaboration with robotics expert, TM Robotics, its South American partner Questt has developed an innovative vial packaging solution that addresses the limitations of manual processes and elevates productivity and performance for life sciences production lines.

The development of affordable and highly performing sensors can have crucial implications for robotics research, as it could improve perception to help boost robot manipulation and navigation. In recent years, engineers have introduced a wide range of advanced touch sensor devices, which can improve the ability of robots to detect tactile signals, using the information they gather to guide their actions.

A patient recovering from a limb amputation won't use a prostheses that isn't comfortable. A person rehabbing from a stroke won't use a robotic exoskeleton if the mobility it grants doesn't allow them to perform everyday activities. And a diabetes patient won't use an insulin pump that doesn't deliver the appropriate dosage of medicine to control their blood sugar.

Featuring new advanced welding solutions for multi-axis, coordinated welding. New cobot capabilities will also be showcased in extended reach and portability applications, and in tasks such as laser restoration, finishing, and press brake tending.

Ready for a robot to take on those menial tasks—and much more?

A three-armed robot trained to mimic a human orchestra conductor has made its debut in the German city of Dresden, directing music composed specially to complement the device's skills.

Red Flag: Google’s CoHosted-Podcast Maker Not Always Accurate

Google’s new NotebookLM — which has gone viral with its ability to auto-script and auto-produce a co-hosted podcast in minutes — is unfortunately also very good at making things up.

The new AI research tool — which uses two, extremely natural-sounding robot voices to discuss text, audio or video that you input into NotebookLM — is currently wowing the Web.

But reviewer Matt Derron has found that like all generative AI tools, the two robot voices can occasionally get it wrong.

“In trying to make the hosts sound natural with their back-and-forth banter, it sometimes misses the original intent of the source material,” Derron says.

Of course, that flaw is no reason to toss NotebookLM on the digital trash-heap: The AI tool’s ability to auto-generate an extremely lively, co-hosted podcast using numerous forms of media is still truly remarkable.

But to be on the safe side, you may want to do a little editing on any co-hosted podcast that’s auto-generated by NotebookLM before publishing it live — or suffer the consequences.

For an excellent, in-depth look and critique of how NotebookLM auto-produces co-hosted podcasts, check-out Derron’s in-depth video.

In other news and analysis on AI writing:

*In-Depth Video Guide: ChatGPT’s New Onboard Editor, ‘Canvas:’ If you’re looking for a crystal clear, extremely informative demo on ChatGPT’s new onboard editor, Canvas, this video is the ticket.

Produced by the ‘Productive Dude,’ channel, the 12-minute, video guide offers great insights into the editor, whose coolest feature is its ability to highlight and quickly change portions of a text in ChatGPT, on-the-fly.

Canvas is already available to paying users of ChatGPT Plus and ChatGPT Team, is currently being rolled-out to ChatGPT Enterprise and ChatGPT Education users.

It also may be offered to free users at a later date.

Other key tools included by the Canvas editor — which are operated with a click — include the ability to:

~adjust the word length of a document
~adjust the reading level of a document
~solicit editing suggestions for a document
~add final polish to a document’s wording
~add emojis to a document

Plus, while you’re using Canvas, you can also work with the document the ‘old fashioned’ way by using prompts to alter the document’s text.

*Gmail’s Upgraded Auto-Replies: For When “K” Isn’t Enough: Gmail aided by Google’s Gemini AI is now able to offer auto-replies to emails that are much more in-depth.

Observes writer Mike Moore: “After selecting to reply to a message, users will see several response options at the bottom of their screen, which now analyzes the full content of the email thread to provide more detailed, richer responses.

“Users can hover over each response to get a quick preview of the text, then select the one that feels right for the situation.

“You will be able to edit the pre-written message if needed, or send immediately.”

*When Less is More: New Frase Upgrade Cuts Clutter, Keeps Magic: Frase — an AI writer that specializes in auto-producing search engine optimized (SEO) copy — is out with an easier-to-use version.

Observes Matt Hurley, co-founder, Frase: “We removed the functionality that caused clutter and confusion and focused on building more of what truly mattered to our users.

“The result? A simpler yet more powerful tool that ensures you don’t need an army of specialists or endless training to create content that drives results.”

*Microsoft’s Upgraded Copilot: Part Assistant, Part Thinker, Always on: Microsoft Copilot — a key competitor to ChatGPT — now offers enhanced functionality, including:

~An audio-driven, daily news summary
~Natural voice interaction
~The ability to act as a companion when you browse the Web
~’Think Deeper,’ available via the Copilot Lab module, which enables Copilot’s AI to ruminate carefully before responding to a user question

*Google’s Podcast Creator: Instant Banter, Now With Audio and YouTube Inputs: Google’s NotebookLM — an AI research assistant that
auto-creates co-hosted podcasts from text — can now also ingest audio and YouTube videos to auto-create podcasts.

Observes Raiza Martin, a Google product manager: “Today, you can now add public YouTube URLs and audio files directly into your notebook, alongside PDFs, Google Docs, Slides, Web sites and more.”

In practice, this means you can add a bit of text to NotebookLM, a few links to some YouTube videos, a few more links to some audio podcasts — and the tool will auto-create a co-hosted podcast for you based on those inputs.

*Automated Blogging: Who Needs Quality When You Can Have Quantity?: Marketers and others using AI to auto-generate endless posts for their blog could be playing with fire, according to writer Sandra Dawson.

Specifically, Dawson says such automated blogging can lead to:

~Misinformation and low quality content

~Auto keyword stuffing

~Generic-sounding posts

~A slew of other downsides

*Using ChatGPT? Congrats, You’ve Mastered Most AI Writers Already: While there are hundreds of AI writers, just a few companies — including ChatGPT’s maker Open AI, Anthropic and Meta — actually power those auto-writers, according to Ryan Doser.

The reason: Most AI writers are simply software interfaces that sit atop the powerful AI engines that actually do the real work of auto-generating writing, according to this 12-minute video by Doser.

Plus, the few AI titans who own those AI engines currently all use the same technology: Generative AI.

A key takeaway: This is why it makes sense to stay well-acquainted with ChatGPT, whose AI engine — and underlying technology –serves as the foundation for many other AI writers.

Essentially: If you know how to use ChatGPT, you already know — in a general way — how to use all those other AI writers that are powered by ChatGPT or powered by other generative AI.

*New ChatGPT Challenger: Free, Open — and Ready to Rumble: ChatGPT has another challenger lurching for its throne: A new AI engine just released by Nvidia.

Interestingly, the new AI engine is open source, meaning anyone can download its software, tinker with it and/or build applications atop it, free-of-charge.

The reason why this particular AI engine is so notable: Most of today’s generative AI is powered by Nvidia chips, which heavily dominate the world as the go-to hardware for powering AI.

Plus, Nvidia also has extremely deep pockets to continue competing with ChatGPT: It’s currently one of the top five companies in the world and worth about $3.4 trillion.

*AI Big Picture: AI Engine Building: For People Who Use Moons as Paperweights: The power to build AI engines — the underlying software that powers today’s AI writers and similar apps — is being concentrated in fewer and fewer hands.

The reason: It takes enormous amounts of capital to build such engines — also known as Large Language Models.

Case in point: Character.AI, an AI startup, just abandoned its efforts to enhance its own AI engine, given that such building has become incredibly expensive, according to writer Sage Lazzaro.

Share a Link:  Please consider sharing a link to https://RobotWritersAI.com from your blog, social media post, publication or emails. More links leading to RobotWritersAI.com helps everyone interested in AI-generated writing.

–Joe Dysart is editor of RobotWritersAI.com and a tech journalist with 20+ years experience. His work has appeared in 150+ publications, including The New York Times and the Financial Times of London.

The post Dream a Little Dream for Me appeared first on Robot Writers AI.

Red Flag: Google’s CoHosted-Podcast Maker Not Always Accurate

Google’s new NotebookLM — which has gone viral with its ability to auto-script and auto-produce a co-hosted podcast in minutes — is unfortunately also very good at making things up.

The new AI research tool — which uses two, extremely natural-sounding robot voices to discuss text, audio or video that you input into NotebookLM — is currently wowing the Web.

But reviewer Matt Derron has found that like all generative AI tools, the two robot voices can occasionally get it wrong.

“In trying to make the hosts sound natural with their back-and-forth banter, it sometimes misses the original intent of the source material,” Derron says.

Of course, that flaw is no reason to toss NotebookLM on the digital trash-heap: The AI tool’s ability to auto-generate an extremely lively, co-hosted podcast using numerous forms of media is still truly remarkable.

But to be on the safe side, you may want to do a little editing on any co-hosted podcast that’s auto-generated by NotebookLM before publishing it live — or suffer the consequences.

For an excellent, in-depth look and critique of how NotebookLM auto-produces co-hosted podcasts, check-out Derron’s in-depth video.

In other news and analysis on AI writing:

*In-Depth Video Guide: ChatGPT’s New Onboard Editor, ‘Canvas:’ If you’re looking for a crystal clear, extremely informative demo on ChatGPT’s new onboard editor, Canvas, this video is the ticket.

Produced by the ‘Productive Dude,’ channel, the 12-minute, video guide offers great insights into the editor, whose coolest feature is its ability to highlight and quickly change portions of a text in ChatGPT, on-the-fly.

Canvas is already available to paying users of ChatGPT Plus and ChatGPT Team, is currently being rolled-out to ChatGPT Enterprise and ChatGPT Education users.

It also may be offered to free users at a later date.

Other key tools included by the Canvas editor — which are operated with a click — include the ability to:

~adjust the word length of a document
~adjust the reading level of a document
~solicit editing suggestions for a document
~add final polish to a document’s wording
~add emojis to a document

Plus, while you’re using Canvas, you can also work with the document the ‘old fashioned’ way by using prompts to alter the document’s text.

*Gmail’s Upgraded Auto-Replies: For When “K” Isn’t Enough: Gmail aided by Google’s Gemini AI is now able to offer auto-replies to emails that are much more in-depth.

Observes writer Mike Moore: “After selecting to reply to a message, users will see several response options at the bottom of their screen, which now analyzes the full content of the email thread to provide more detailed, richer responses.

“Users can hover over each response to get a quick preview of the text, then select the one that feels right for the situation.

“You will be able to edit the pre-written message if needed, or send immediately.”

*When Less is More: New Frase Upgrade Cuts Clutter, Keeps Magic: Frase — an AI writer that specializes in auto-producing search engine optimized (SEO) copy — is out with an easier-to-use version.

Observes Matt Hurley, co-founder, Frase: “We removed the functionality that caused clutter and confusion and focused on building more of what truly mattered to our users.

“The result? A simpler yet more powerful tool that ensures you don’t need an army of specialists or endless training to create content that drives results.”

*Microsoft’s Upgraded Copilot: Part Assistant, Part Thinker, Always on: Microsoft Copilot — a key competitor to ChatGPT — now offers enhanced functionality, including:

~An audio-driven, daily news summary
~Natural voice interaction
~The ability to act as a companion when you browse the Web
~’Think Deeper,’ available via the Copilot Lab module, which enables Copilot’s AI to ruminate carefully before responding to a user question

*Google’s Podcast Creator: Instant Banter, Now With Audio and YouTube Inputs: Google’s NotebookLM — an AI research assistant that
auto-creates co-hosted podcasts from text — can now also ingest audio and YouTube videos to auto-create podcasts.

Observes Raiza Martin, a Google product manager: “Today, you can now add public YouTube URLs and audio files directly into your notebook, alongside PDFs, Google Docs, Slides, Web sites and more.”

In practice, this means you can add a bit of text to NotebookLM, a few links to some YouTube videos, a few more links to some audio podcasts — and the tool will auto-create a co-hosted podcast for you based on those inputs.

*Automated Blogging: Who Needs Quality When You Can Have Quantity?: Marketers and others using AI to auto-generate endless posts for their blog could be playing with fire, according to writer Sandra Dawson.

Specifically, Dawson says such automated blogging can lead to:

~Misinformation and low quality content

~Auto keyword stuffing

~Generic-sounding posts

~A slew of other downsides

*Using ChatGPT? Congrats, You’ve Mastered Most AI Writers Already: While there are hundreds of AI writers, just a few companies — including ChatGPT’s maker Open AI, Anthropic and Meta — actually power those auto-writers, according to Ryan Doser.

The reason: Most AI writers are simply software interfaces that sit atop the powerful AI engines that actually do the real work of auto-generating writing, according to this 12-minute video by Doser.

Plus, the few AI titans who own those AI engines currently all use the same technology: Generative AI.

A key takeaway: This is why it makes sense to stay well-acquainted with ChatGPT, whose AI engine — and underlying technology –serves as the foundation for many other AI writers.

Essentially: If you know how to use ChatGPT, you already know — in a general way — how to use all those other AI writers that are powered by ChatGPT or powered by other generative AI.

*New ChatGPT Challenger: Free, Open — and Ready to Rumble: ChatGPT has another challenger lurching for its throne: A new AI engine just released by Nvidia.

Interestingly, the new AI engine is open source, meaning anyone can download its software, tinker with it and/or build applications atop it, free-of-charge.

The reason why this particular AI engine is so notable: Most of today’s generative AI is powered by Nvidia chips, which heavily dominate the world as the go-to hardware for powering AI.

Plus, Nvidia also has extremely deep pockets to continue competing with ChatGPT: It’s currently one of the top five companies in the world and worth about $3.4 trillion.

*AI Big Picture: AI Engine Building: For People Who Use Moons as Paperweights: The power to build AI engines — the underlying software that powers today’s AI writers and similar apps — is being concentrated in fewer and fewer hands.

The reason: It takes enormous amounts of capital to build such engines — also known as Large Language Models.

Case in point: Character.AI, an AI startup, just abandoned its efforts to enhance its own AI engine, given that such building has become incredibly expensive, according to writer Sage Lazzaro.

Share a Link:  Please consider sharing a link to https://RobotWritersAI.com from your blog, social media post, publication or emails. More links leading to RobotWritersAI.com helps everyone interested in AI-generated writing.

–Joe Dysart is editor of RobotWritersAI.com and a tech journalist with 20+ years experience. His work has appeared in 150+ publications, including The New York Times and the Financial Times of London.

The post Dream a Little Dream for Me appeared first on Robot Writers AI.