Why Choosing the Right AI Development Company in the USA Matters for Your Business Growth
Artificial Intelligence is no longer a futuristic concept; it's the driving force behind smarter operations, personalized customer experiences, and faster decision-making. As businesses across industries race toward digital transformation, choosing the right AI development company in the USA becomes a critical decision. Whether you're looking to streamline workflows, automate tasks, enhance service delivery, or build forward-thinking applications, the right partner can help you harness AI in meaningful, measurable ways.
Today, organizations need more than just generic tech solutions. They need strategic partners capable of delivering scalable and customized artificial intelligence software development tailored to their goals. This is where selecting an experienced and innovative team becomes essential.
The Growing Importance of AI for Modern Businesses
AI adoption is growing rapidly as companies realize the value it brings. From small businesses to global enterprises, AI is being used to predict customer behavior, automate manual tasks, optimize operations, and create personalized experiences at scale.
Here’s why AI is becoming a necessity:
1. Smarter Decision-Making
AI-driven insights help leaders understand trends, analyze user behavior, and make data-backed decisions that improve performance and profitability.
2. Increased Efficiency and Automation
Repetitive and time-consuming tasks can be automated, reducing human error and freeing teams to focus on strategic initiatives.
3. Enhanced Customer Experience
AI-powered chatbots, personalization engines, recommendation systems, and voice assistants transform how brands interact with customers.
4. Competitive Edge in a Growing Market
Companies that invest early in AI technology position themselves ahead of competitors who rely only on traditional systems.
5. Scalable Business Growth
AI supports expansion by predicting demand, optimizing resources, and enabling better management of operations.
What an AI App Development Company Can Do for You
Working with a certified and experienced AI app development company ensures you access cutting-edge technologies, expert engineering, and end-to-end development support. Here’s what such a partner typically offers:
✔ Custom AI Software Development
From idea to deployment, they design solutions tailored to your industry and business needs.
✔ Machine Learning Model Development
Professionals build ML models that learn from data to improve accuracy and efficiency over time.
✔ AI-Powered Mobile and Web Apps
Features like real-time analytics, automation, smart recommendations, and voice interactions enhance user experience.
✔ Computer Vision & NLP Solutions
Businesses benefit from AI that can detect objects, analyze images, understand speech, and process natural language.
✔ Integration With Existing Systems
AI systems are seamlessly integrated into your current workflows, CRM platforms, ERP systems, and more.
✔ Ongoing Optimization & Support
Deployment is just the beginning; continuous training, scaling, and improvement are essential to long-term success.
Why Choose an AI Software Development Company in the USA?
Partnering with an AI software development company in the USA comes with several advantages:
Developers stay updated with the latest frameworks, models, and innovations in the AI landscape.
3. Strategic Consulting Approach
They don’t just develop software, they guide your AI transformation journey.
4. Clear Communication & On-Time Delivery
You benefit from structured processes, transparency, and well-defined project timelines.
The Power of Tailored AI Software Development Solutions
Every business requires unique strategies and tools. That’s why personalized AI software development solutions matter. Companies benefit most when solutions are designed to support their goals whether that’s automating operations, improving service quality, or enabling smarter decision-making.
A high-quality AI software development agency ensures your AI system is:
Scalable as your business grows
Secure with robust protection measures
Flexible to integrate with new tools and technologies
Cost-efficient with optimized performance
Partnering With a Trusted AI Development Company
A reliable partner not only understands technology but also understands your business. Bitontree stands out as a forward-thinking team dedicated to delivering intelligent, scalable, and high-impact AI solutions that empower companies to innovate and grow.
When you work with a skilled AI development services company, you gain access to expert engineers, data scientists, and strategists who ensure your AI product is built with precision and long-term value.
Choosing the Right AI Development Agency in the USA: Key Factors
When selecting your AI partner, consider the following:
1. Expertise Across AI Technologies
Machine learning, NLP, computer vision, predictive analytics, and automation.
2. Proven Portfolio
Strong case studies and diverse industry experience.
3. Transparent Process
Regular communication, clear milestones, and collaborative development.
4. Customized Approach
No “one-size-fits-all” solutions tailored to your business goals.
5. Long-Term Support
AI systems need training, optimization, and ongoing improvements.
FAQs
1. What should I look for in an AI development company?
Look for experience across AI technologies, strong case studies, transparent processes, and the ability to deliver customized solutions aligned with your business goals.
2. How long does AI software development take?
Timelines vary based on complexity. Simple AI integrations may take 4–8 weeks, while larger, fully custom AI solutions can take several months.
3. Can AI be integrated into my existing systems?
Yes. AI can be integrated with CRMs, ERPs, mobile apps, websites, automation tools, and internal software to enhance efficiency and intelligence.
4. What industries can benefit from AI?
Healthcare, finance, retail, logistics, education, manufacturing, real estate, and nearly every industry can benefit from AI automation, analytics, and intelligent applications.
Hire AI/ML Engineers to Transform Your Business with Intelligent Solutions
In today’s fast-evolving digital landscape, businesses are rapidly adopting Artificial Intelligence (AI) and Machine Learning (ML) to stay competitive. From automation to predictive analytics, AI is driving innovation across industries.
To successfully implement these technologies, companies need skilled professionals. That’s why many organizations are choosing to hire AI/ML engineers who can build scalable, data-driven solutions.
Why Businesses Need to Hire AI/ML Engineers
AI is no longer optional—it’s a necessity for growth and innovation.
🚀 Accelerate Digital Transformation
AI helps automate workflows, improve decision-making, and enhance operational efficiency.
📊 Data-Driven Insights
Businesses generate massive data daily. Skilled engineers turn this data into valuable insights.
💡 Competitive Advantage
Companies leveraging AI outperform competitors with smarter strategies and faster execution.
If you're planning to integrate AI into your operations, working with AI developers for hire can give you a strong competitive edge.
Key Services Offered by AI/ML Engineers
When you hire AI engineers in India, you gain access to a wide range of services:
✔ Machine Learning Development
Custom models tailored to your business needs.
✔ Natural Language Processing (NLP)
Chatbots, sentiment analysis, and conversational AI.
✔ Computer Vision
Image recognition and video analytics solutions.
✔ Predictive Analytics
Forecast trends, demand, and customer behavior.
✔ AI Automation
Streamline operations and reduce manual work.
A reliable machine learning development company ensures these solutions are implemented effectively.
Benefits of Hiring Dedicated AI/ML Engineers
💰 Cost Efficiency
Reduce hiring, infrastructure, and training costs.
⚡ Faster Time-to-Market
Quick onboarding ensures faster project delivery.
🔄 Scalability
Easily scale your team as your business grows.
🧠 Access to Top Talent
Work with experts skilled in modern AI frameworks.
Businesses often prefer dedicated AI developers to ensure consistent performance and focus.
Industries Leveraging AI/ML Solutions
AI is transforming multiple industries:
🏥 Healthcare – diagnostics and patient analytics
🚚 Logistics – route optimization and forecasting
🏭 Manufacturing – predictive maintenance
💳 Finance – fraud detection and risk analysis
💻 SaaS – intelligent applications and automation
Partnering with AI outsourcing services helps businesses implement these solutions faster.
Real-World Use Cases of AI/ML
AI/ML engineers help build impactful solutions such as:
🤖 AI chatbots for customer support
📊 Predictive analytics for forecasting
🔍 Fraud detection systems
🎯 Recommendation engines
📦 Supply chain optimization
To implement these use cases effectively, many companies choose to hire AI developers remotely for flexibility and scalability.
How to Hire AI/ML Engineers: Step-by-Step
Step 1: Define Your Requirements
Outline your goals, budget, and technical needs.
Step 2: Choose the Right Model
Dedicated, contract-based, or full-time hiring.
Step 3: Evaluate Expertise
Check technical skills and past projects.
Step 4: Onboard Quickly
Start development without delays.
Step 5: Scale Your Team
Expand resources as your project grows.
Working with AI consulting services can simplify this entire process.
Why Choose AquSag Technologies?
AquSag Technologies provides a reliable solution for businesses looking to scale with AI.
✅ Pre-Vetted Engineers
Access skilled professionals with proven experience.
✅ Flexible Hiring Models
Choose what fits your business needs.
✅ Fast Onboarding
Start your project quickly.
✅ End-to-End Support
From development to deployment.
Whether you need NLP, automation, or predictive analytics, hiring NLP developers for hire ensures high-quality AI solutions.
Challenges in Hiring AI Talent
❌ Talent Shortage
AI experts are in high demand globally.
❌ High Costs
Full-time hiring can be expensive.
❌ Long Hiring Cycles
Traditional recruitment takes time.
This is why businesses are turning to AI automation services and external hiring models.
Future of AI/ML in Business
The future is driven by:
Generative AI
Real-time analytics
Autonomous systems
Intelligent automation
Companies that invest in AI talent today will lead tomorrow’s market.
Conclusion
AI is transforming how businesses operate, innovate, and grow. To stay ahead, companies must adopt intelligent solutions powered by skilled professionals.
The best way to achieve this is to hire AI/ML engineers who can deliver scalable and impactful results.
In the world of artificial intelligence, machine learning is a crucial component that enables computers to learn from data and improve their performance over time. However, the math behind machine learning is often shrouded in mystery, even for those who work with it every day. Anil Ananthaswami, author of the book "Why Machines Learn," sheds light on the elegant mathematics that underlies modern AI, and his journey is a fascinating one.
Ananthaswami's interest in machine learning began when he started writing about it as a science journalist. His software engineering background sparked a desire to understand the technology from the ground up, leading him to teach himself coding and build simple machine learning systems. This exploration eventually led him to appreciate the mathematical principles that underlie modern AI. As Ananthaswami notes, "I was amazed by the beauty and elegance of the math behind machine learning."
Ananthaswami highlights the elegance of machine learning mathematics, which goes beyond the commonly known subfields of calculus, linear algebra, probability, and statistics. He points to specific theorems and proofs, such as the 1959 proof related to artificial neural networks, as examples of the beauty and elegance of machine learning mathematics. For instance, the concept of gradient descent, a fundamental algorithm used in machine learning, is a powerful example of how math can be used to optimize model parameters.
Ananthaswami emphasizes the need for a broader understanding of machine learning among non-experts, including science communicators, journalists, policymakers, and users of the technology. He believes that only when we understand the math behind machine learning can we critically evaluate its capabilities and limitations. This is crucial in today's world, where AI is increasingly being used in various applications, from healthcare to finance.
A deeper understanding of machine learning mathematics has significant implications for society. It can help us to evaluate AI systems more effectively, develop more transparent and explainable AI systems, and address AI bias and ensure fairness in decision-making. As Ananthaswami notes, "The math behind machine learning is not just a tool, but a way of thinking that can help us create more intelligent and more human-like machines."
The Elegant Math Behind Machine Learning (Machine Learning Street Talk, November 2024)
Matrices are used to organize and process complex data, such as images, text, and user interactions, making them a cornerstone in applications like Deep Learning (e.g., neural networks), Computer Vision (e.g., image recognition), Natural Language Processing (e.g., language translation), and Recommendation Systems (e.g., personalized suggestions). To leverage matrices effectively, AI relies on key mathematical concepts like Matrix Factorization (for dimension reduction), Eigendecomposition (for stability analysis), Orthogonality (for efficient transformations), and Sparse Matrices (for optimized computation).
The Applications of Matrices - What I wish my teachers told me way earlier (Zach Star, October 2019)
Transformers are a type of neural network architecture introduced in 2017 by Vaswani et al. in the paper “Attention Is All You Need”. They revolutionized the field of NLP by outperforming traditional recurrent neural network (RNN) and convolutional neural network (CNN) architectures in sequence-to-sequence tasks. The primary innovation of transformers is the self-attention mechanism, which allows the model to weigh the importance of different words in the input data irrespective of their positions in the sentence. This is particularly useful for capturing long-range dependencies in text, which was a challenge for RNNs due to vanishing gradients. Transformers have become the standard for machine translation tasks, offering state-of-the-art results in translating between languages. They are used for both abstractive and extractive summarization, generating concise summaries of long documents. Transformers help in understanding the context of questions and identifying relevant answers from a given text. By analyzing the context and nuances of language, transformers can accurately determine the sentiment behind text. While initially designed for sequential data, variants of transformers (e.g., Vision Transformers, ViT) have been successfully applied to image recognition tasks, treating images as sequences of patches. Transformers are used to improve the accuracy of speech-to-text systems by better modeling the sequential nature of audio data. The self-attention mechanism can be beneficial for understanding patterns in time series data, leading to more accurate forecasts.
Attention is all you need (Umar Hamil, May 2023)
Geometric deep learning is a subfield of deep learning that focuses on the study of geometric structures and their representation in data. This field has gained significant attention in recent years.
Michael Bronstein: Geometric Deep Learning (MLSS Kraków, December 2023)
Traditional Geometric Deep Learning, while powerful, often relies on the assumption of smooth geometric structures. However, real-world data frequently resides in non-manifold spaces where such assumptions are violated. Topology, with its focus on the preservation of proximity and connectivity, offers a more robust framework for analyzing these complex spaces. The inherent robustness of topological properties against noise further solidifies the rationale for integrating topology into deep learning paradigms.
Cristian Bodnar: Topological Message Passing (Michael Bronstein, August 2022)
Mastering MATLAB: Solving Challenging University Assignments
Welcome to another installment of our MATLAB assignment series! Today, we're diving into a challenging topic often encountered in university-level coursework: image processing. MATLAB's versatility makes it an invaluable tool for analyzing and manipulating images, offering a wide array of functions and capabilities to explore. In this blog, we'll tackle a complex problem commonly found in assignments, providing both a comprehensive explanation of the underlying concepts and a step-by-step guide to solving a sample question. So, let's roll up our sleeves and get ready to do your MATLAB assignment!
Understanding the Concept: Image processing in MATLAB involves manipulating digital images to extract useful information or enhance visual quality. One common task is image segmentation, which involves partitioning an image into meaningful regions or objects. This process plays a crucial role in various applications, including medical imaging, object recognition, and computer vision.
Sample Question: Consider an assignment task where you're given a grayscale image containing cells under a microscope. Your objective is to segment the image to distinguish individual cells from the background. This task can be challenging due to variations in cell appearance, noise, and lighting conditions.
Step-by-Step Guide:
1. Import the Image: Begin by importing the grayscale image into MATLAB using the 'imread' function.
image = imread('cells.jpg');
2. Preprocess the Image: To enhance the quality of the image and reduce noise, apply preprocessing techniques such as filtering or morphological operations.
filtered_image = medfilt2(image, [3 3]); % Apply median filtering
3. Thresholding: Thresholding is a fundamental technique for image segmentation. It involves binarizing the image based on a certain threshold value.
6. Analyze Results: Finally, analyze the labeled image to extract relevant information about the segmented objects, such as their properties or spatial distribution.
Navigating through complex MATLAB assignments, especially in challenging topics like image processing, can be daunting for students. At matlabassignmentexperts.com, we understand the struggles students face and offer expert assistance to ensure they excel in their coursework. If you need someone to do your MATLAB assignment, we are here to help. Our team of experienced MATLAB tutors is dedicated to providing comprehensive guidance, from explaining fundamental concepts to assisting with assignment solutions. With our personalized approach and timely support, students can tackle even the most demanding assignments with confidence.
Conclusion:
In conclusion, mastering MATLAB for image processing assignments requires a solid understanding of fundamental concepts and proficiency in utilizing various functions and techniques. By following the step-by-step guide provided in this blog, you'll be well-equipped to tackle complex tasks and excel in your university assignments. Remember, at matlabassignmentexperts.com, we're here to support you every step of the way. So, go ahead and dive into your MATLAB assignment with confidence!
Thelio Massive at the Lab: An interview with Luca Della Santina
Every now and then we like to check in on our customers to find out about what coolness they’re up to. This week, we sat down with Luca Della Santina, an assistant professor at UCSF in the Department of Ophthalmology, to see what he and his Thelio Massives are discovering at the lab.
What kind of work goes on in the Department of Ophthalmology?
Everything we do is focused on the eye and on vision. I am also part of the Bakar Institute, which is a computational institute specializing in machine learning and deep learning applied to health sciences. The lab that I run is divided between working on computational approaches, mainly image analysis.
What projects are you working on right now?
One major current project is detecting an infection of the eye called trachoma. Trachoma is an infection that affects the inside of the eyelid. It usually occurs in countries below the tropics, and it’s a major cause of blindness for people across the world—except for in wealthy countries like the US where it’s very rare. Eliminating it elsewhere is a major goal of the World Health Organization. Africa, South America, Asia and Oceania still have many cases, so we’re taking photographs of the afflicted eyelid to look at the sites where bacteria has infected the eye. Then we use deep learning to detect it automatically to help public health experts decide which communities will require antibiotic treatment.
We’re also taking images of neurons in the eyes and map the connection between them, called synapses, to study how degenerative diseases of the eye such as glaucoma can alter the wires between neurons. Knowing which neurons are the most susceptible to disease will shine a light on new and more sensitive tests to catch these blinding diseases before they can actually cause major vision loss. This type of research generates really large data sets, in which each image is large many gigabytes and for which the analysis is very computationally intensive, both for the GPU and the CPU.
How long have you been using System76 workstations for your projects?
We started to use System76 systems two years ago, give or take. It was part of setting up my computational lab. One of the goals was to have a completely open a stack, and your workstations were an integral part of this strategy.
What is the computational stack you’re using?
We have the Thelio Massives configured for deep learning and for processing large image data. One of the systems has NVIDIA Quadro RTX 8000 GPUs for training larger models than we usually do. In the other system, we have it configured with dual CPUs and dual NVIDIA GeForce RTX 2080 Tis. The reason for that is that some of the computational work is being developed with parallel computing, both on GPUs and CPUs. The more cores and the more CPUs we get this on, the better.
How do you balance workloads between the CPUs and GPUs?
Strictly for the projects I’m on, they’re each about as important. All of the machine learning runs off the GPU right now, but all of the basic image analysis and parallel computing actually works off the CPU. The reason for the latter is there’s no significant advantage to push that work onto a GPU. There are a few algorithms that we cannot parallelize on the GPU because of the way they are designed, and one of these is actually pretty fundamental in the way we segment images, so if we put it on the GPU there is not much increase in speed because we cannot push it onto every core of the GPU. For most of it, we need the raw power of the CPU.
What were the determining factors when you decided to go with System76 and our Thelio Massives?
A few things. We wanted a system that was designed to run Linux from its foundations. There are not a lot of systems like yours, so that was a major factor in our choice. We also wanted a system that we could expand easily in the future, and we found out that the Thelio Massive has has great expandability.
The most important factor for me was being able to double or triple the RAM somewhere down the line, and maybe have another couple of GPUs in the system. Having storage options is useful for us because we may generate a dataset and on a single 4TB hard drive, so the ability to just pop out and pop in hard drives is very easy. It’s actually huge for us. I ended up buying a bunch of 5TB drives and just packed them in. Most of the small stuff we just run off of the NVMe drive, and that’s much better than the rest of the storage we have.
I really enjoy how quiet these machines are! I can testify that we’re sharing the same room with another computer from a different vendor with similar components, and it’s about 10 times louder than the Thelio Massives.
What operating system do you use?
So far we’ve been keeping both Thelio Massives on Pop!_OS. The other workstation we have in the lab is either Ubuntu or Windows.
How has Pop!_OS been for you?
The software pipeline we use runs out of the box pretty well on Pop!_OS, so that’s not been an issue so far. I appreciate that you guys have full disk encryption out of the box.
We’ve also heard you’re thinking about buying a Lemur Pro. What made you consider that machine?
I need something that’s light that I can bring around with me. It’s also got a good number of ports, which lately has been hard to find on a laptop, which frees me up from having to carry dongles on my trips. I can also configure it up to 40GB of RAM, and I need at least 32GB, so that’s perfect for me.
Would you like to share how System76 has improved workflow for you and your organization? Contact [email protected] to set up an interview!
Adam Haar Horowitz is the first to admit that whispering to strangers as they fall asleep “seems a little creepy.” He’d been mulling over the idea with fellow MIT master’s student Ishaan Grover a few years ago while thinking about ways to influence the dreamlike visions people see at sleep onset, a state known as hypnagogia. The pair wondered if quietly saying words or phrases to people in hypnagogia might influence the content of their thoughts and visions, thereby serving both as a tool to investigate human cognition and, ultimately, as a means to help people wield control over their dreaming brains.
Haar Horowitz didn’t end up whispering into strangers’ ears, but he, Grover, and other collaborators did find a way to execute the basic concept, using a more practical solution: a device that fits into a person’s hand to monitor changes in heart rate, muscle tone, and skin conductance—all of which help researchers determine the moment at which someone dozes off—paired with a computer or smartphone app that automatically plays audio prompts and records people’s spoken responses. The app “would speak to people when it guessed that they were at the end of hypnagogia before they go into something deeper,” Haar Horowitz says. After reporting what they were thinking about right at that moment, people would be allowed to nod off again, and the whole process could be repeated.
In experiments detailed in Haar Horowitz’s master’s thesis and a scientific paper published earlier this year, people interacted easily with the setup, known as Dormio. They chatted with it, albeit somewhat nonsensically, about what they could see and feel as they slipped in and out of wakefulness. One volunteer, prompted by Dormio to think about a fork, described dreaming about a family that was “happy to see the fork. And they’re putting it in a pumpkin,” according to Haar Horowitz’s thesis. Another participant, told to think about a tree, described “a tree from my childhood, from my backyard. It never asked for anything.”
Dream researchers who spoke with The Scientist say Dormio marks an exciting step for a field traditionally limited by scientists’ inability to interact with their study participants. Many experts define dreams broadly as any subjective experiences people have while asleep, although most projects rely on dream reports collected specifically from people woken up from rapid-eye movement (REM) sleep, the stage of sleep at which people are most likely to experience emotional, narrative dreams (though not the only sleep stage in which dreams can occur; see “The Stages of Dreaming” below). This time-consuming approach has been something of an obstacle to researchers interested in manipulating dreams—an important aspect of dream research, says Haar Horowitz, now a research assistant in the Fluid Interfaces research group at MIT’s Media Lab. After all, he says, “you can’t do controlled experimentation on dreams without an ability to control dreams.”
Haar Horowitz is one of a small but growing group of researchers who call themselves dream engineers and are exploring various methods to influence people’s thoughts at sleep onset and during sleep itself. Some tools, like Dormio, use aural stimuli, while others harness sights or smells, or employ more-complex technologies such as noninvasive brain stimulation. With a recent wave of studies demonstrating the promise of such approaches, neuroscientists and psychologists may be able to learn more not only about how and why dreams are generated, but about possible health and cognitive applications of dream control.
Such techniques could help give dream researchers the control they’re after, says Harvard Medical School dream researcher Robert Stickgold, Haar Horowitz’s mentor and collaborator. With these approaches, “we can use the scientific method.”
Lucidity and other dream experiences
Attempts to influence dreaming are by no means new. People dating as far back as the ancient Egyptians have been known to fast to induce vivid dreams, while scientists, philosophers, and artists have been experimenting for centuries with hashish, opium, and other drugs to conjure dreamy visions in and out of sleep. Many cultures continue to hold beliefs about the dream-altering effects of certain foods—folklore in many Western societies holds that cheese can induce vivid dreams, although there’s little scientific research on the topic. And demand for lucid dreaming classes designed to help people take control of their in-dream environments has taken off in the last few years, helped along by Christopher Nolan’s dream-twisting 2010 blockbuster, Inception.
Dream engineers are interested in finding reliable, researcher-controlled ways to induce lucidity—where a dreamer becomes aware of being in a dream and may be able to exert control over their actions and their environment, as well as other sensations such as flying, to investigate how those sensations are generated and whether they’re associated with any benefits for the person experiencing them. One method that’s received significant interest as a way to manipulate dream sensations is noninvasive brain stimulation, which uses a magnetic coil or scalp electrodes to influence electrical activity in the dreamer’s brain.
In 2013, a small study applied 10 minutes of transcranial direct current stimulation (tDCS) to people in REM sleep, and concluded, based on reports people made after being woken up from REM sleep, that the procedure increased lucidity in dreams when compared to a sham procedure. A similar study the following year that applied either a sham procedure or bursts of transcranial alternating current stimulation (tACS)—which is thought to be better than tDCS at influencing brain oscillations—concluded that 40 Hz currents during REM could also promote dreamers to become more self-aware. However, in both studies, the effect was weak, and in the 2013 study it was only observed among people who said they already frequently experienced lucid dreams. That group is unlikely to be representative of the general population, for which researchers estimate that up to 50 percent may never have experienced dream lucidity.
The University of Montreal’s Tore Nielsen, who directs the Dream and Nightmare Laboratory at the Center for Advanced Research in Sleep Medicine, is unconvinced that noninvasive brain stimulation works as a lucidity inducer. Like many dream researchers, Nielsen says he has experienced his fair share of lucid and otherwise extraordinary dreams. He and his colleagues recently carried out their own study of tACS using stringent study conditions: for researchers to confirm a participant’s report of lucid dreaming, that person had to give a signal on becoming lucid—flicking their eyes from left to right under their closed eyelids three times—and that had to happen during REM sleep, as determined by electroencephalography (EEG) analyses of their brain activity. “Much to our chagrin, we failed to replicate” the earlier findings, Nielsen says. Although some participants did the eye-flick signal during REM sleep and subsequently described vivid dreams, people were no more likely to have lucid dreams after receiving tACS than they were if they’d had the sham procedure.
The Stages of Dreaming
Neuroscientists used to think that dreaming took place almost exclusively during rapid-eye movement (REM) sleep, a stage of slumber that is often accompanied by complex emotional, narrative-heavy dreams that can involve sensations such as flying or other movements. But in the last few decades, research has shown that people can also have subjective dream-like experiences in non-REM sleep, albeit less frequently and of a different nature. For example, a person thinking about a cat as they doze off into the first stage of sleep—a hallucinatory state known as hypnagogia—may see strange cat visions and experience sensations such as falling. Dreams experienced later in non-REM sleep tend to be more mundane and may involve people or objects that are familiar to the dreamer. Once in very deep sleep, people are more likely to have conceptual thoughts than to experience emotional narratives, if they have any memorable dreams at all.
Noninvasive brain stimulation may have other uses in dream manipulation, particularly for studying the relative roles of different brain regions in generating common dream experiences. Queen Mary University of London’s Valdas Noreika and colleagues, for example, recently used 10-minute sessions of tDCS to disrupt activity in the sensorimotor cortex of 10 volunteers while they were in REM sleep. The researchers woke people from REM sleep shortly after each session, and asked them to fill out questionnaires on what they’d been dreaming about—and specifically, whether they’d been engaged in movements such as lifting objects or walking. The results showed that people who had received tDCS reported experiencing less movement in their dreams than people receiving a sham procedure, suggesting that normal sensorimotor cortex activity is required for those dream sensations, Noreika says. Specifically, “we found that this sensorimotor cortex is responsible for repetitive actions of the dream self . . . such as walking, running, swimming.”
Simpler technologies likely also have their place in the manipulation of dream experiences. Michelle Carr, who did her PhD in Nielsen’s lab and is now a postdoc at the University of Rochester, has been experimenting for the last couple of years with techniques to induce self-awareness in dreamers in a lab setting. She’s found that using behavioral training to get people to associate sensory stimuli such as lights or sounds with a sense of heightened awareness seems to be an effective way to trigger lucid dreaming.
In a recent study, for example, Carr and her colleagues trained awake volunteers to try to become particularly aware of their surroundings whenever researchers presented them with alternating cues of flashing red LEDs and a beeping noise. Participants subsequently were allowed 90 minutes to doze off for a nap in the lab, while researchers monitored their sleep stages using several techniques including EEG and measurements of electrical activity in the muscles. When a participant entered REM sleep, experimenters triggered the LED and the audio cues in the same alternating pattern they’d played during training. By monitoring eye movements for the agreed-upon eye-flicking signal, collecting dream reports from people woken up by researchers for brief periods mid-nap, and administering questionnaires after the 90 minutes was up, the team found that around 50 percent of the treatment group experienced lucid dreams, compared with just 17 percent in a control group of participants who’d completed the training but hadn’t had the lights and sounds played to them during their naps. “It was really cool—some people did [the cue-signal response] up to eight times,” Carr says. “Some people who had never before had one had their first lucid dream in the lab.”
While the research is still in early stages, Carr says she hopes the findings will encourage further studies intended to trigger certain sensations in dreams, with an eye toward the possible benefits. She and her colleagues recently analyzed dream and mood diaries kept by 20 people over the course of a week and found that higher lucidity correlated with elevated waking mood the following day. The researchers plan to use their lucidity-inducing techniques to investigate whether the relationship is to some degree causal, Carr says, and whether inducing lucidity has other applications, such as helping people suffering from recurrent nightmares—a common symptom of many mental health conditions including anxiety disorder and post-traumatic stress disorder.
“If we can get [these dreams] to be induced reliably,” Carr says, “then we can use them for beneficial purposes.”
Incubation of specific dream content
For some dream researchers, it’s not just the overall dream experience that’s worth manipulating, but also a dream’s specific content. Many ancient human civilizations experimented with this idea too, and documented attempts to promote in-dream encounters with various deities, for example. But for Harvard’s Stickgold, it was a family trip to Vermont in the 1990s that made him start thinking about the idea.
Falling asleep one evening after a hike up Camel’s Hump in the Green Mountains, Stickgold was surprised to feel as though he were scrambling up the side of the mountain, just as he had earlier that day, with the distinct sensation of rocky ground under his hands. Waking up and then dozing off again, he found that he was able to regain this sensation several times before falling into a deeper sleep. Intrigued by the experience, Stickgold says, he wondered about how to try to capture it in an experiment.
There was a stumbling block, however: he’d be unlikely to obtain ethical and administrative approval to lead a gaggle of undergrads on a rock-climbing expedition just to see if they’d go on to dream about the experience. It was only a few years later that an alternative presented itself. “I was in a meeting with a bunch of students one day, pissing and moaning about what a great experiment this would be, but how I would never be able to do it,” Stickgold says. “One of the students sitting there just said, ‘What about Tetris?’ They proceeded to tell me that this happens when you start playing Tetris: you see [the pieces] floating down before your eyes.”
That conversation was the seed for what would become a famous study in dream research. Stickgold and colleagues recruited 27 people—10 Tetris experts, 12 Tetris novices, and five patients with memory loss from brain damage—to play seven hours of the computer game over the course of three days. For an hour at the beginning of each night, participants were prompted by an experimenter or by a digitized voice recording to say what they were thinking about into a microcassette recorder or to an experimenter as they fell in and out of sleep. Almost two-thirds of the participants reported dream-like visions of Tetris during sleep onset, and three of the five amnesiacs also reported seeing Tetris-inspired imagery, despite having no conscious memory of the game. One described “thinking about little squares coming down on a screen and trying to put them in place,” while another said they’d seen “images that are turned on their side. I don’t know what they are from, I wish I could remember, but they are like blocks.” Several Tetris experts reported thinking not only of the Tetris they’d been playing during the experiment, but also of older versions of the game they’d played previously.
Nudging the brain to incorporate specific content—a trick known as dream incubation—has proven to be surprisingly practical using computer and virtual reality games. Erin Wamsley, previously a postdoc with Stickgold’s group who now runs a lab at Furman University in South Carolina, says that many researchers previously assumed dreams would be most influenced by more-intense experiences. “You can show someone horrible graphic images or very disturbing films with very high emotional content that participants would agree is disturbing or emotional,” says Wamsley. “But [that’s] not something that triggers people to dream directly about that experience, necessarily. On the other hand, we’ve had a lot of success causing participants to incorporate new learning experiences into their dreams.” Wamsley’s now looking into what determines whether a particular experience will be incorporated into a dream.
The dream engineer’s tool box
Researchers use a variety of technologies to monitor (teal) and attempt to modulate (purple) people’s dream experiences. While many protocols include pre-sleep training—to encourage people to become more aware of their dreaming selves, for example, or to incubate specific ideas using virtual reality or computer games—a number of dream-influencing approaches can be applied during sleep. Scientists also monitor participants during sleep and collect dream reports as soon as they awake.
In 2010, Stickgold, Wamsley, and colleagues got 43 volunteers to play an arcade skiing game called Alpine Racer. Around a third of the dream reports collected from subjects woken up from non-REM sleep over the following nights were related to the game. The nature of the dream content changed as people fell into deeper sleep, however, going from typical comments such as “I get like flashes of that . . . game in my head, virtual reality skiing game,” to oblique skiing references such as, “I was picturing stacking wood this time. . . . I felt like I was doing it at . . . a ski resort that I had been to before, like five years ago maybe.”
In the last couple of years, the same researchers have also used a simple maze navigation task that participants carry out on a computer to explore how content is incorporated into dreams that occur during different sleep stages. A dream report from someone just falling asleep contained thoughts of swimming above the maze, for example, while one participant woken from REM sleep reported dreaming about walking through it. A typical report from later stages of non-REM sleep involved the dreamer just standing in the middle of a maze waiting for a friend to find them.
This and many other studies have also reported an association between the incorporation of task content into dreams and task performance post-sleep—a finding that adds weight to the prevalent view among sleep researchers that sleep, and perhaps dreaming specifically, plays an important role in memory consolidation. On the basis of current evidence, it’s not clear whether dreaming helps drive that consolidation, or is perhaps instead a reflection or byproduct of the process. Stickgold, who explores theories of dreaming with coauthor Antonio Zadra in a new book, When Brains Dream, slated for publication in January, hypothesizes that REM sleep plays an active role in consolidating emotional memories and extracting patterns from recent experiences, and that perhaps the dreamlike visions of hypnagogia are the brain’s way of tagging relevant content for processing later on in the sleep cycle. Other researchers posit that dreams serve different functions—Noreika is one of several scientists who think they offer simulation of potential threats and social interactions that the dreamer might encounter in waking life—or perhaps no function at all.
See “Dreaming of Possibilities”
Exploring potential functions of dreams and dream content is a key purpose of dream-influencing technologies such as Dormio, notes Haar Horowitz, who says that the device’s ability to interact with dreamers in real time offers the possibility of collecting data more easily compared to traditional dream research, even if hypnagogia and REM sleep aren’t exactly equivalent. He’s recently launched a number of collaborations, not only with sleep scientists curious about how changing dream content could alter memory or learning, but also with artists and philosophers interested in how dream incubation might boost their creativity.
Making dream engineering mainstream
Carr, Haar Horowitz, and others organized a workshop at MIT last year for engineers and dream researchers to discuss technologies available to the field, and the group put together a special issue of scientific papers on dream engineering for the journal Consciousness and Cognition this summer. “A lot of collaborations developed from that workshop,” says Carr, who was managing guest editor for the issue. “I think it’s the start of something new.”
With this momentum, dream researchers are hopeful that their field will overcome a lingering image problem in sleep science. Even now, “a lot of people view dreaming as a fringe topic, kind of like studying ESP [extrasensory perception] or out-of-body experiences,” says Wamsley. “Of course, in my opinion, it’s nothing like that at all. In our research on dreaming, we treat studying dreams as another way to understand what the mind and brain are doing during sleep.”
Nevertheless, the subjectivity of self-reported dreams remains an issue, she acknowledges. While neuroscientists’ attempts to objectively predict what people are dreaming about on the basis of brain imaging techniques such as functional MRI have made strides in the last few years, they’re a long way from matching the detail in dreamers’ own descriptions, she says. Aware of this obstacle, several groups working on dream engineering seek to demonstrate the value and feasibility of collecting dream reports as part of regular sleep studies.
In a recent study from Björn Rasch’s lab at the University of Fribourg in Switzerland, for example, researchers trained people on a word-picture association task, and then subsequently woke them up for dream reports during the night. The team found that people’s memory of the task the following morning didn’t seem to be affected by the awakenings themselves. The researchers also reported that there was a positive relationship between dreaming of the task during non-REM sleep and memory performance the following morning, but they found no such association when it came to dreams of the task during REM sleep—a clue about sleep’s role in memory that would have been overlooked had dream reports not been gathered.
Dream researchers are also looking toward some of the extraordinary implications of manipulating the minds of sleeping people. With the prospect of devices such as Dormio allowing people to interface with their own or other people’s dreams, ethical considerations “are paramount here,” notes computer scientist Pattie Maes, the head of the Fluid Interfaces group at MIT’s Media Lab and a coauthor of a review of the field in Consciousness and Cognition.
Stickgold agrees, noting that even after having done it for decades, there’s something unique, and even unsettling, about interacting with the minds of people in the not-quite-conscious, not-quite-unconscious world of dreams. “It has an edge of scariness,” he says. “We’re tapping into an aspect of people’s minds that we don’t have much control over and they don’t have much control over when they’re sleeping. We’re almost voyeurs, watching their minds do what they decide to do.”
Online Courses in Artificial Intelligence | Discover a career in Artificial Intelligence | IBM
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Annotation - The Best Performance Of AI In The Automotive Industry
There have been many approaches for image annotation in digital images, especially in its application to the automotive industry. Nowadays, as digital photography is becoming a common technology for capturing, people can easily archive images thanks to the help of well-equipped digital cameras and memory storage, the use of image annotation is easier than ever before.
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Image Annotation
As it is named, image annotation is the bridge between objects in reality and its appearance on machine learning. The other definition is - the process of making the given objects detectable and recognisable to machines, which mostly using bounding boxes to annotate. As being told as the helpful boost for AI (Artificial Intelligent), image annotation is valuable in all types of AI models namely self-driving cars, security cameras, robots, auto flying gadgets, etc which are relying on data created by bounding box annotation to power computer vision models with high-quality image data.
Specifically, one technical term like automatic image annotation is the process when a computer system assigns metadata to a digital image in an automatic way for both caption or keywords forms. This is one of the most important applications of computer vision techniques which is used in organising and locating images from a database.
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Annotation Application
By making such objects detectable through computer vision technology, its application can be listed down including the following: detect objects of interests, classify various objects given and segmentise objects into single classes.
For instance, with the help of annotation, we as humans can easily train our autonomous vehicles, drones, and any computer vision solutions to interpret images and videos. The whole process can be done completely by using high-quality annotated images. In developing annotation projects, annotation experts will focus on the execution of work, including tagging key objects in images using annotation methods based on the requirement of clients. Image annotation team will ensure the progress from sourcing, preparing, analysing the images with the metadata in the given file formats, and annotating.
How it works
The procedure in which annotation works is using the technology of AI to mark the correlation between machine learning and objects given in image features, especially shape, color, and texture. The aim is to provide automatic correct annotation objects in images that provide an alternative to the time-consuming work of manual image annotation for machines to recognise, thus increasing the flexibility of work afterward.
With the increase of digital applications, annotating a specific image becomes an essential factor in data labelling companies. It has been creating a completed model which is capable of assigning terms to an image in order to describe its content within. Thus, clients can get the expected outcomes.
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