Technology Archives

Generative AI Comparison: Best AI Models Available in 2024

Posted on September 5, 2024September 5, 2024 by mkoftielieva

AI is a revolutionary technology, and its rapid growth is why you need some generative AI comparison sources right now. This tech has spread and evolved so fast that it’s hard to understand exactly what the solutions available on the market are capable of. Despite having some similar functionality, generative AI tools differ quite a bit. So, read on to learn the best time to use each top AI model.

Who Needs This Generative AI Comparison Guide?

If you use the Internet today, you will benefit from reading this simple guide on AI model comparison. This technology is quickly spreading to different areas of our daily and, most of all, professional lives. Therefore, knowing which AI tool to use and when is key to staying ahead.

There are numerous areas of business where you can implement AI, so you will definitely find ways to use this technology to boost your outcomes.

Today, the market provides a variety of large language models (LLMs). Each of them has different tools and capabilities. Some are best used for coding only, while others perform exceptionally well in creative tasks. As a result, it’s pretty confusing and complicated to pick the right AI tools for your purposes. That’s why Devtorium R&D experts prepared this short guide on four of the most effective LLMs and their best use cases.

Comparison of Generative AI Tools: Benefits and Uses

ChatGPT

ChatGPT stands for “Chat Generative Pretrained Transformer.” OpenAI developed this LLM and currently offers three models: GPT-3.5, GPT-4, and GPT-4o.

Chat GPT-3.5 is a free version that anyone can access. However, it has many limitations, like no image input or complex task processing.
Chat GPT-4 is a $20/month subscription version designed for professional use. The model has excellent contextual understanding and creative reasoning. Among its drawbacks is slower task processing speed due to model complexity.
Chat GPT-4o (or ChatGPT-4 Turbo) is a brand-new version of ChatGPT-4 that offers similar capabilities but is cost-efficient and speed-optimized. This tool has free and paid plans with varying limits. Also, among its inputs can be text, images, audio, and video. Even though GPT-4o has a bit worse context retention than Chat GPT-4, this model still balances exceptional outputs with processing speed.

Best ChatGPT applications:

Cost-effective solution
For budget-conscious projects, models like ChatGPT-4o offer a balance between performance and affordability.
Hard prompts
Advanced versions like ChatGPT-4o would be effective if complex or nuanced responses are necessary. Moreover, according to the LMSYS Chatbot Arena Leaderboard, the best hard prompt performance out of 126 AI models shows ChatGPT-4o.
Longer queries
ChatGPT excels at understanding context and coherence across extended conversations, making it ideal for in-depth discussions or multi-step tasks.
Versatile applications
From creative writing to code generation, ChatGPT developed its available functions evenly.

Claude

Not a common name in most AI comparison guides, Claude is a family of AI language models developed by Anthropic. These LLMs focus on providing safe AI interactions. Claude 3 Haiku, Claude 3 Opus, and Claude 3.5 Sonnet are among the models currently available to general users.

Claude 3 Haiku has the highest response time of all Anthropic models. It’s ideal for concise prompts and fast tasks. It’s also more affordable compared to others. However, it has limited creative capabilities and contextual understanding. It’s best suited for mobile application chatbots and instant messaging.
Claude 3 Opus is a mid-range AI tool with moderately fast latency. It balances creativity and accuracy, offering strong contextual retention and versatility.
Claude 3.5 Sonnet is the first release in the forthcoming Claude 3.5 model family. It’s one of the most advanced Claude models at the moment. This model is outperforming competitors in different spheres. However, it meets the same problem as Chat GPT-4: slower workflow speed due to more complex processing for richer output. Claude 3.5 Sonnet is now free on Claude.ai, while Claude Pro and Team plan subscribers can access it with significantly higher rate limits.

Best cases to use Claude:

Code generation
Claude 3.5 Sonnet generates optimal, almost bug-free code across 20+ languages, optimizing for project-specific needs and best practices. Also, according to the LMSYS Leaderboard, Claude 3.5 Sonnet is the best coding and math task-solving AI today.
Visuals analysis
Claude 3.5 Sonnet can analyze images, documents, and PDFs, extracting essential information for diverse tasks. It’s free with basic features, but paid plans offer enhanced capabilities and higher usage limits.
Ethical AI applications
Every Anthropic’s model is built on nuanced AI principles, prioritizing safety. It also means that all responses Claude provides must adhere to them. Claude is forthright about its limitations and potential biases, promoting responsible AI use.
Complex decision-making
Claude can handle intricate scenarios with multiple variables. Moreover, it is ideal for tasks that require deep contextual awareness.

Meta LLaMA

LLaMA (Large Language Model Meta AI) is an open-source LLM developed by Meta. Its main feature is its small resource intensity, which enables researchers and developers to meet complex requests on smaller hardware. At the moment, Meta offers three models of LLaMA: LLaMA 2, LLaMA 3, and LLaMA 3.1.

LLaMA 2 is a free-to-use OSS model of AI. It is the first openly available LLM instruction-tuned for text. It’s also great for commercial use if you struggle with huge budgets. However, this model is a bit outdated, so you can find inexpensive alternatives that provide higher performance.
LLaMA 3 is the next generation with some significantly upgraded features. This model is multilingual and has high prompt understanding. Unfortunately, it delivers bad performance in reasoning and math.
LLaMA 3.1 is a recent model built on LLaMA 3. It has improved reasoning and coding capabilities. Also, LLaMA 3.1 is the largest openly available model right now. So, if you want the best free-to-use AI model, this one will be a top hit according to our AI comparison.

When to use LLaMA:

Commercial applications
This AI model is ideal for many business applications without additional costs.
Meta integration
LLaMA can be easily integrated into Meta AI, Facebook, Instagram, and WhatsApp, providing advanced AI capabilities for content generation, customer interaction, and personalized user experiences.
Multimodal tasks
The model offers robust support for diverse languages and media formats, making it a versatile tool for global and cross-platform applications.

Comparison of generative AI models on the market

Gemini

Gemini is an AI model developed by Google DeepMind. It is positioning itself as a competitor to advanced LLMs like GPT-4. Four Gemini models made it to our I comparison guide: Gemini Ultra, Gemini Pro, Gemini Flash, and Gemini Nano.

Gemini 1.0 Ultra is Google’s largest model, designed for complex AI tasks. It offers maximum computational power for enterprise-level solutions and advanced AI research. This AI tool is great for advanced app integration.
According to ratings, Gemini 1.5 Pro is the best Google AI model. It excels in general performance across a wide range of tasks. Gemini Pro can process hard prompts and follow instructions almost perfectly, making it suitable for professional-grade tools and large-scale applications.
Gemini 1.5 Flash is a lightweight model of Gemini Pro designed for fast data analysis.
Gemini 1.0 Nano is the most powerful on-device model available. It is ideal for mobile apps, IoT devices, and edge computing with minimal resource usage.

Top Gemini use cases:

Overall best app
Currently, Gemini 1.5 Pro has the best results, outperforming all listed competitors.
Factual accuracy
Google’s AI relies on enormous databases and searches, ensuring its output is reliable and trustworthy.
Gmail integration
Using Gemini, you can enhance email management by providing smart reply suggestions, drafting assistance, and content generation directly within the platform.

Bottom Line: Which Model Is Best in AI Tools Comparison?

To sum it up, the current tech landscape offers a diverse range of AI solutions tailored to various business needs. From the advanced capabilities of ChatGPT and Gemini to the specialized performance of Claude and LLaMA models, each of these tools can help you.

Therefore, the best model for your specific case is the one that has the most advanced capabilities in the niche your business requires. If you want to benefit from AI integration, contact our experts for a free consultation today. We’ll help you choose a suitable AI model and develop the best implementation to enhance your business. If you want to learn more about our strengths, be sure to check our Devtorium’s case studies and verified Clutch reviews from our customers.

Will Small Business Be Affected by the AI Bubble Burst?

Posted on August 14, 2024August 13, 2024 by mkoftielieva

With the AI bubble burst and the stock market crash on everyone’s mind, it’s no surprise that many people are getting anxious. They do have due cause because even leading market strategists and analysts aren’t sure exactly how this situation will end.

Will there be a recession? Almost definitely. Will AI drop back into the obscurity of specialized tech? Certainly not!

Therefore, anyone using AI for small businesses in any capacity should not worry about any tech issues with these solutions. In fact, the global economic recession should provide more incentive to develop and implement AI solutions. In this kind of volatile market, a small business needs every advantage to actually stay in business.

Benefits of using AI for small business today.

When Will the AI Bubble Burst?

Since the COVID-19 pandemic, there have been many discussions about the global economic crisis. However, the stock market crash came as a shock anyway, and many leading experts and market strategists commented on it. The good news is that if we look at authoritative sources like Bloomberg, the expert prognosis is not favorable, but it’s not panic-inducing either.

Yes, economists are concerned, and there is a chance this is just the beginning of a massive recession. However, some leading experts, like Diana Iovanel, a senior markets economist at Capital Economics, say that instead of the AI bubble burst, we should expect its strengthening after this shakeup. The level of investment in AI technology by leading companies, such as Microsoft and Apple, continues to grow. Moreover, the technology itself evolves and attracts more users every day. Therefore, while a global recession might be an issue for the world economy, AI will remain one of the leading market forces for years to come.

How to Use AI for Small Business to Stay Safe in the Volatile Market?

The truth of the matter is that as a small business owner, you will be affected regardless of whether the AI bubble burst comes to pass. Events like stock market crashes are indicators of volatile global economic processes. This volatility alone is a major threat to the livelihood of small business owners worldwide. The best way to cope with this danger is by managing risks, and AI is a handy helper for this specifically.

Using AI for small business can help you achieve crucial outcomes, such as:

Cost reduction
Using AI tools and chatbots, you can automate processes and even replace some outsourced services, like customer support.
Innovation and competitive advantage
By implementing innovation, you can increase your value proposition for your customers. For many startups, innovation in the process itself becomes a business. For example, check out our case study of an insurance platform with a widget that can remake the entire process of buying a policy online.
Scalability and enhanced efficiency
Using AI for small business gives you some freedom to scale up or down as needed with minimal disruptions to the overall business processes. Moreover, automating some of the routine tasks reduces human error and frees up time for your qualified employees. They can use this time to work on solutions that will help increase your business resilience.
Better decision-making
AI goes hand in hand with data analytics services, which can unlock your access to invaluable insights. Making decisions based on concrete data will enable you to achieve the best possible results.

Learn more about practical implementations of this tech in our article How to Use AI in Small Business.

Bottom Line: Reduce Business Risks and Increase Resilience with AI

We don’t know when or if the AI bubble burst will occur. However, we know for sure that the practical value of implementing AI solutions in business processes will only grow. As the markets grow more competitive, the one who has an edge has the best chance of survival. Therefore, whether you launch a chatbot to enhance customer service or supplement your security with AI, you are moving in the right direction.

Business owners need to be proactive to stay ahead. In these times, this means using cutting-edge tech to its maximum benefit.

Are you interested in learning more about the topic? Check out more of our articles about AI and ideas on how to implement it for various businesses here.

If you are ready to start implementing AI solutions in your own company, set up a free consultation! Our experts will make a detailed analysis of your business and ideas. Then, we’ll give you a proposal on how to achieve the best results with the project.

Data Science Uses in Business, Healthcare, Finance & Engineering

Posted on July 24, 2024July 24, 2024 by mkoftielieva

Do you know how many valuable insights a company’s data hides? Data science uses are innumerable, and your business can’t afford to miss out on these opportunities. This complex study applies various practices from mathematics, statistics, programming, and artificial intelligence (AI) to analyze vast volumes of data. Data scientists use analytics to explain past causality and predict the future. Some things you can quickly improve with data science services include operational efficiency, decision-making, planning, and many more.

Unsurprisingly, data science has become one of the fastest-growing fields in every industry. According to the LinkedIn Emerging Jobs Report, data scientists have seen 37% annual growth in demand, and it keeps rising. Businesses use data science to gain an advantage over competition and achieve maximum efficiency. Devtorium data science experts utilize the power of data to optimize our clients’ performance and help develop AI-powered solutions. In this post, we will dive into diverse data science applications, focusing on their uses in business, finance, and healthcare.

Data Science Uses Across Industries

Some time ago, we posted a blog explaining what data science services are. Summing up that post, you can divide the general data science uses in any industry into three categories:

Predictive Analytics
Predictive analytics uses specific historical data to analyze captured patterns and forecast the future. These forecasts are in high demand across various industries today. With their help, you can anticipate market trends or customer behaviors. For example, retailers can predict inventory needs based on seasonal trends, while manufacturers can forecast demand to optimize production schedules.
Risk Management
Data science helps organizations identify and mitigate risks. This crucial function can prevent significant losses or disruptions by analyzing rash decision consequences. In addition, fraud detection is one of the excellent data science uses. You can analyze transaction patterns to identify anomalies indicative of fraudulent activities.
Process Optimization
Another way to implement data science is to analyze operational data to identify weaknesses and inefficiencies and optimize processes. The absence of good process optimization causes huge money waste. For instance, logistics companies need data science to optimize delivery routes, reduce costs, or improve service levels.

Benefits of using data science in business.

Data Science in Business Analytics

The benefits of data science in business come primarily from the fact that it allows you to understand your performance and market trends much better. As a result, you are able to make data-driven decisions and have a greater chance of success. Considering this, the best practical data science uses in business would be:

Strategic Planning and Decision-Making
By analyzing market trends, competitive landscape, and internal performance data, your company can drive growth and innovation while avoiding potential pitfalls.
Supply Chain Management
Data science benefits supply chain management through forecasting, inventory management, and logistics planning. When companies use data-driven insights to manage their supply chains effectively, they can reduce costs and improve service delivery.
Marketing Strategies
Marketing teams leverage data science to analyze customer data and optimize campaigns. Techniques like customer segmentation and sentiment analysis enable targeted marketing efforts, which increase conversion rates.

Data Science Uses in Finance

The financial sector is the main beneficiary of the many data science uses. Some key applications in this area include:

Algorithmic Trading
Algorithmic trading is a techniques that uses complex algorithms to execute trades at high volumes fast. Data science enables the development of these algorithms. Therefore, the users can analyze market data and execute trades based on predefined criteria. The result is increased efficiency and profitability of the business.
Credit Scoring and Risk Assessment
Financial institutions, such as banks, use data science to assess credit risks by analyzing many data points. These include credit history, transaction patterns, and social media activity. The results of such analyses lead to more accurate credit scoring and better risk management.
Customer Segmentation and Personalization
Financial institutions use data science to segment customers based on their behaviors and preferences. This application provides personalized financial products and services, enhancing customer satisfaction and loyalty.

Uses of Data Science in Healthcare

The number and diversity of uses of data science in healthcare seem to be growing by the day. From personalized marketing of healthcare services to analysis of X-rays, data science services help reduce mistakes and make us healthier. This field is developing rapidly, but for now, key areas of application in healthcare include:

Personalized Medicine
Data science enables personalized medicine by analyzing genetic data and medical histories to tailor treatments to individual patients. This approach increases the effectiveness of treatments and reduces adverse reactions. Moreover, healthcare providers use predictive analytics to forecast patient outcomes based on historical data. It can help in the early detection of diseases and timely intervention, improving patient prognosis.
Operational Efficiency
Hospitals and clinics use data science to optimize operations, such as patient flow management, staff scheduling, and inventory control. This approach leads to cost savings and improved patient care.
Drug Discovery and Development
Pharmaceutical companies use data science to accelerate drug discovery and development. By analyzing large datasets, they can identify potential drug candidates faster and more accurately, bringing new lifesaving drugs to market.

Data science uses in various industries.

Applications in Engineering

Engineering fields leverage data science to drive innovation, improve quality, and enhance efficiency. For instance, an automotive manufacturer can use data science to optimize its production line, increase productivity, and reduce production costs. Other notable applications include:

Predictive Maintenance
Data science helps predict equipment failures before they occur. This can be achieved by analyzing sensor data and maintenance records. This feature reduces downtime and maintenance costs, improving operational efficiency.
Quality Control and Defect Detection
Manufacturers use data science to enhance quality control by analyzing production data to detect defects early in the process. It can lead to higher product quality and reduced waste.
Design and Simulation
Engineers use data science to improve design processes through simulations and modeling. This application allows for testing and optimization of designs before building physical prototypes, saving time and resources.

In Conclusion

In our data-driven world, data science is a superpower that can transform businesses across various industries. By leveraging data-driven insights, companies can make better decisions, optimize operations, and drive innovation.

Are you ready to harness the power of data science for your business? Contact us today and let Devtorium’s data science experts help you unlock the full potential of your business.

If you’re interested in learning more about our other services, check out more articles:

How to Use AI in Small Business: Ideas and Practical Applications

Posted on July 3, 2024July 3, 2024 by mkoftielieva

At this point, using AI in small business has become a mandatory requirement. There is just no other way to gain a competitive advantage. The level of competition in every industry is skyrocketing, so you must cut your costs and optimize every possible process. This is precisely what AI can do for small businesses, and we’ll tell you how today.

How many companies consider using AI in small business

Using AI in Small Business: Practical Tips from Professionals

Leveraging AI tools can be troublesome for many SMBs because they need help figuring out where to start. Devtorium offers a range of AI software development services, and our developers have expertise in implementing AI into various systems. In this post, they will demystify AI for small businesses, providing insights and examples of successful AI usage.

Bear in mind, that the majority of businesses are either already using or consider implementing AI solutions already. Take a look at stats in the graphs to see what position your company matches currently.

Let’s start with a few general tips to consider when using AI in small business:

Start small
First, you should begin with only targeted AI applications that align with your business goals and resources. For example, if you have an e-commerce site, add a chatbot to enhance customer experience.
Data quality matters
For AI to work accurately, you must be sure that training data is clean and relevant. It’s crucial for minimizing the risk of bias in AI outcomes.
Monitor your ROI
Your BA must analyze all AI projects’ return on investment (ROI) to ensure they deliver value to your business.
Collaborate with experts
Partner with AI specialists or consultants to navigate complex implementation challenges.

Real-World Implementations of AI in Small Business

Improving Customer Experience

Adding an AI chatbot to your website or service is the best AI innovation to start with. This virtual assistant can solve many tasks, from answering users’ frequently asked questions to assisting with product recommendations based on their behavior. Moreover, chatbots can learn and adapt over time. Therefore, their accuracy and efficiency in handling customer queries will improve.

Virtual assistants can cut the workload of your human support staff. In turn, they will have time to focus on more complex tasks. Besides, these AI solutions can gather valuable data on customer interactions. Use them as an analytics tool to learn about your audience and make better-targeted business decisions. For example, you can identify trends, understand customer needs, and improve your services overall.

To start using such a chatbot, small businesses can try services like Dialogflow by that uses Google Al. You can also try to make your own chatbot with LangChain. If you want to go to the next level, check out our case of what a voice bot can do.

Supply Chain Management

Some fantastic ways of using AI in small business that deals with logistics include:

Optimizing delivery routes optimization
Reducing transportation costs
Cutting down on delivery times
Increasing logistics efficiency

Also, you can use AI to evaluate supplier performance and manage relationships, ensuring the best terms and reliability. AI-driven supply chain management systems leverage advanced algorithms to analyze vast amounts of data and make real-time decisions that enhance operational efficiency.

AI can also enhance predictive maintenance in logistics. This is done by using data from IoT sensors to anticipate equipment failures and schedule proactive repairs. With this tech, you can minimize downtime and improve your business’s overall reliability.

Moreover, AI-powered demand forecasting models help anticipate customer needs more accurately. Therefore, businesses can adjust production schedules and inventory levels accordingly. By optimizing these processes, AI contributes to cost reduction and enhances the agility of supply chain operations.

Predictive Analytics

One of the best ways to use AI in small business is implementing predictive analytics to analyze your past data and identify patterns. These tools enable you to forecast sales trends, which can make a crucial difference in achieving success.

Efficient use of predictive analytics can help you come up with effective strategies and prevent rash decisions. Furthermore, predictive analytics can optimize inventory management by forecasting demand and caution about overstock or stockouts.

In addition, these AI systems can analyze sales data to identify seasonal trends and customer behavior patterns. You can utilize tools like Salesforce Einstein Analytics to anticipate market shifts.

Visual content

You should consider using AI in small business marketing, especially if you don’t have a dedicated marketing team. Creating and optimizing visual content can be easy using tools like Midjourney or Photoshop’s integrated AI, creating and optimizing visual content can be easy.

For example, some tools today can personalize marketing materials based on user data, creating tailored experiences that appeal more deeply to the target audience. In addition, using generative AI can save time, money, and effort.

Integrating AI into visual content strategies helps businesses stay ahead of the competition by consistently delivering visually compelling content. These tools can streamline the design process by providing instant enhancements and creative ideas, allowing teams to focus on more strategic tasks.

Cybersecurity

One area where small businesses aren’t using enough AI is cybersecurity. You should definitely make this your priority, as data breaches are a major threat today. Implement AI-powered cybersecurity solutions to detect and mitigate potential threats in real-time. For instance, predictive threat intelligence enables AI to analyze patterns and trends in cyber attack data, forecasting where and how future attacks might occur. Using AI for small business in this particular sphere enables them to strengthen their defenses preemptively.

On other levels, AI enhances email security by detecting phishing attempts and malware-laden messages. This will significantly reduce the risk of successful social engineering attacks. Additionally, AI assists in post-incident analysis, providing valuable insights to understand the nature and scope of breaches, and informing future prevention strategies. This cycle of learning and making changes based on new data ensures that AI-powered cybersecurity solutions remain effective against an ever-changing threat landscape.

Conclusion: how to best start using AI in small business.

Bottom Line: How to Use AI in Small Business to Get Top Value for Money

To sum it up, small businesses can and should use AI systems to automate processes, make data-driven decisions, and achieve desirable growth. As AI continues to evolve and become more accessible, embracing this technology will be essential for staying ahead of the curve. However, it is challenging to adopt AI for your needs without technical expertise and business analysis. If you want to get maximum benefit from any AI tools or even customize some of them to fit your business needs, set up a free consultation with our experts today.

Use of AI in Cybersecurity: Modern Way to Enhance Security Systems

Posted on April 18, 2024April 18, 2024 by mkoftielieva

Now is definitely the time to use AI in cybersecurity. In fact, those who don’t do this put their businesses at great risk. While some reports aren’t highly detailed, according to cybercrime statistics, the damage caused by it amounts to $12.5 billion. This number is growing every year because complex systems are inherently vulnerable, and criminals use every tech innovation they can. So, no one can afford not to use AI’s help to build the best possible security system while hackers use this technology to exploit your weaknesses.

Modern threats need up-to-date solutions. Devtorium offers a wide range of cybersecurity services, and our security and R&D specialists are working to identify the best AI applications in this area. At the current level of technology, AI can automate tasks, improve threat detection, predict future attacks, and more. In this blog, you will learn about the benefits and drawbacks of using AI in cybersecurity systems.

Use of AI in cybersecurity: areas of implementation.

Use of AI in Cybersecurity: Applications in Various Systems

Network Security

This type of security protects a computer network from unauthorized access, misuse, or attacks. It involves developing a secure infrastructure for devices, users, and applications to work safely. The tools used in network security include firewalls, VPNs, and data loss prevention (DLP), as well as intrusion detection and prevention systems.

AI applications in network security:

Anomaly detection.
AI can analyze network traffic patterns to identify unusual activity that might indicate a cyberattack.
Automated threat mitigation.
AI systems can automatically take steps to isolate threats, such as blocking malicious IP addresses or quarantining infected devices.

Information Security

This system protects digital information, such as data stored in databases, files, or other repositories. It includes data encryption, access controls, and data backup and recovery. Devtorium is an ISO/IEC 27001:2013-certified company and our specialists are able to ensure complete security of your systems both with and without AI.

AI use in cybersecurity of information systems:

Data Loss Prevention (DLP).
AI can analyze data content to identify sensitive information and prevent unauthorized data exfiltration.
Threat intelligence analytics.
AI can collect and analyze threat data from various sources to predict future attacks and improve security planning.

Application Security

This security system aims to secure software applications from being stolen or hacked. Application security can reveal weaknesses at the application level, helping to prevent attacks. AI implementation in application security can include secure coding practices, vulnerability scanning, and penetration testing.

Static application security testing (SAST).
AI can analyze code to find potential vulnerabilities before application deployment.
Runtime application self-protection (RASP).
AI-based RASP systems can monitor applications in real-time and detect suspicious behavior.

Cloud Security

Cloud security protects cloud-based assets and services by keeping data private and safe across online infrastructure, applications, and platforms. It is a shared responsibility between the organization and the service provider.

Uses of AI in cybersecurity of the cloud:

User and entity behavior analytics (UEBA).
AI can analyze user activity in the cloud to identify potential threats or compromised accounts.
Cloud workload protection platforms (CWPP).
AI can continuously monitor and secure cloud workloads from evolving threats.

Identity and Access Management (IAM)

This security system manages user access to IT resources. IAM systems ensure that only authorized users can access specific resources and that their access is logged and monitored.

Possible AI applications:

Risk-based authentication.
AI can analyze user behavior and context in order to determine the appropriate level of authentication required for access.
User behavior anomaly detection.
AI can detect unusual user login attempts that might indicate a compromised account.

Internet of Things (IoT) Security

This security system is the practice of securing devices connected to the internet, for example these are smart home hubs, wearables, and industrial control systems. IoT security is a growing concern as there are over 17 billion connected devices, and each of them is vulnerable.

Examples of AI uses for IoT:

AI can analyze data from IoT devices to identify suspicious activity that might indicate a cyberattack.
Predictive maintenance: AI can predict potential device failures and help prevent security breaches caused by vulnerabilities in IoT devices.

Benefits and Drawbacks of Using AI in Cybersecurity

There can be no doubt that AI can enhance security systems in many ways, for example:

Detecting threats.
Analyzing vast amounts of data to identify subtle pattern changes that might indicate malicious activity.
Automation.
Automating repetitive tasks such as log analysis. This will increase overall efficiency by allowing the security personnel to focus on other strategic tasks.
Faster incident response.
By automating threat detection and mitigation, AI responds faster to security incidents.
Scalability and adaptability.
AI systems can accommodate growing networks and data volumes. Additionally, AI can adapt to new threats and security landscapes, ensuring continuous protection.

However, you shouldn’t forget that AI itself is still vulnerable. Moreover, using it can introduce additional weaknesses to your system, such as:

False Positives and Negatives.
AI systems can generate false positives (flagging harmless activity as threats) and false negatives (missing actual threats).
Data bias.
AI algorithms are only as good as their trained data. Biased data can lead to corrupted AI models that miss certain threats or unfairly target specific users.
Insufficient transparency.
AI decision-making processes are quite complex and difficult to understand. Therefore, it might be challenging for us, as users, to debug errors and trust the system’s recommendations.
Security risks of AI itself.
AI systems can be vulnerable to attacks. Malicious actors could potentially exploit vulnerabilities in AI models to launch targeted attacks.
The lack of legal regulation.
The lack of clear regulations around AI use in cybersecurity can create uncertainty regarding liability in case of security breaches or misuse of AI systems.

Bottom Line: How to Implement AI in Cybersecurity?

Eventually, as the technological landscape continues to evolve, new cyber threats will appear, too. However, specialized professionals are always ready to secure your services. Contact our team for a free consultation on how to best implement AI power in your cybersecurity systems!

To learn more about the Devtorium Security Team and the multiple capabilities of AI, check out our other articles:

Prompt Engineering Basics: How to Talk to AI

Posted on April 4, 2024April 4, 2024 by mkoftielieva

Being proficient with prompt engineering basics has become an essential skill nowadays. Many of us talk to AI almost daily. Sometimes, it’s even without our knowledge as the number of voice chatbots increases. However, today, we’ll talk specifically about how to talk to generative AI.

Generative AI prompt engineering can be a bit tricky because you aren’t just ‘venting’ to a machine or going through some customer service routines. The goal here is to word your command in such a way that you get the most accurate result. You can use the knowledge of AI prompt engineering to complete a great variety of tasks, from generating an image to developing and programming an AI voice bot.

Moreover, all of these tasks are becoming more relevant with every passing day. According to the CompTIA IT Industry Outlook 2024 report, 22% of companies insist on AI integration in the workflow. The percentage of using AI in daily work by usual employees is even higher. However, only a few know how to interact with AI most efficiently.

Our highly qualified specialists maintain that prompt engineering is the main thing that most GenAI users need to improve. So, with their help, you’ll be able to learn the basics of prompt engineering.

Prompt Engineering Basics: What Is Prompt Engineering?

Prompt engineering is creating inputs as specific instructions for large language models (LLMs, more on that here).

Generative AI models generate specific outputs based on the quality of provided inputs. We call these inputs prompts, and the practice of writing them is called prompt engineering.

Prompt engineering helps LLMs better process the incoming tasks to produce desired outputs.

How knowing AI prompt engineering basics benefits you.

Where You Can Apply Prompt Engineering Basics

AI software development

Prompt engineering now plays an active role in software development. You can save a great deal of time time by giving the AI model a clear prompt describing the desired functionality. It suggests code snippets or even complete entire functions. That is very helpful, especially for repetitive tasks. Trained on developer prompts, AI can also analyze existing code and identify potential bugs. If you want to read the opinion of Devtorium`s developers on AI code generation tools, check out this post.

Chatbot development

Prompt engineering allows chatbots to respond more naturally and informatively. You provide them with clear instructions and context for understanding customer inquiries. A better understanding of customer questions leads to improved chatbot responses, which means happier customers and shorter wait times. If you want to create your chatbot, read our blog about Assistant API.

Cybersecurity services

Cybersecurity is another field where understanding prompt engineering basics can help you. Security analysts can leverage prompt engineering to guide AI systems in analyzing network activity and logs. AI can efficiently scan vast amounts of data and flag potential security threats when given prompts with specific indicators of compromise (IOCs) or suspicious behavior patterns. Prompt engineering in cybersecurity empowers security professionals by harnessing AI’s analytical power to identify threats, uncover vulnerabilities, and respond to incidents.

Creative content generation

Prompt engineering allows writers to enhance their efficiency. You can give an AI model a starting point and direction for generating creative text formats like blogs, posts, scripts, or even musical pieces. This frees up the writer to focus on refining and polishing the ideas. The same goes for any kind of content, be it visuals, text, or even music.

Essential Tips on Prompt Engineering Basics

Prompt engineers do not only design and develop prompts. They also operate a wide range of skills and techniques that improve the interaction and development of LLMs. Their work encompasses the following:

Zero-shot prompting – instructing LLM without relying on any examples.
Few-shot prompting – giving the model a few examples before instructing.
Chain-of-thought prompting (CoT) – asking the model to explain its steps every time it performs the instruction.

Here are a few tips that will help you communicate with an AI as a prompt engineer on the basic level:

Use clear instructions and ask direct questions. Make the sentences as concise as possible.
Provide LLM with context. Use any relevant data for it.
Give examples in prompts.
Specify the desired output format and length.
Align prompt instructions with the task’s end goal.
Provide the desired output with styles such as bullet points, tables, numbered lists, inline/block code, quotes, hyperlinks, etc.
Let the LLM answer “I don`t know” if needed.
Break the complex tasks into subtasks.
Use a clear separator like “###” to split the instruction and context.
Experiment a lot to see what prompts work best.

Bottom Line: Are Prompt Engineering Basics Enough to Talk to an AI?

So, to sum up, everyone who uses GenAI can learn the easiest prompts to get desired but simple outputs. However, to get more complex results, you will need to have a really good understanding of programming and mathematics. Therefore, if you need to use AI in your project as more than a simple user, contact our team for a free consultation on how to best implement its power for you!

AI Chatbot Development: How Does a Voice Bot Work?

Posted on March 25, 2024March 25, 2024 by mkoftielieva

AI chatbot development is in high demand. Only about 20% of businesses use one, but over 60% think about adopting this technology. At Devtorium, we often work with chatbots because they are of interest to our clients. Today, we’d like to share one of our current cases, where we created an AI chatbot for a client that runs an innovative marketing solution.

The product is a CMS that gathers data through quizzes. The AI chatbot capabilities are required to collect and verify the collected data. In addition, we are expanding the product with an AI-powered voice chatbot that can schedule calls and generally substitute call center services.

Devtorium has a dedicated Research and Development Team that works with different types of AI services. In this case, our developers did all the work, creating the CMS, integrating a messenger chatbot, and developing a voice bot for calls.

How we used AI chatbot development for a case of a marketing CMS.

AI Chatbot Development for a Marketing Solution: Base Product Outline

In this project, the product is a quiz-based CMS that creates a variety of questionnaires based on user-set parameters. The quizzes are flexible so every user can custom-tailor the questionnaire for their business. The CMS makes collecting, processing, and visualizing data easy to help users understand their target audience.

The project aims to create an effective solution that will help businesses generate qualified leads. This product’s text chatbot collects data. The next phase, the voice bot, can schedule calls, initiate APIs, and send messages. Most importantly, the AI can use advanced ML models to understand what the customer says and proceed according to the information received in real-time.

During this project, our software development team used a variety of technologies:

Back-end:

Node.js

Front-end:

React

Databases:

MongoDB
PostgreSQL

DevOps:

AWS
Jenkins
GitHub

AI (phone service):

Vonage

ML models:

OpenAI GPT-3.5(GPT-4)
OpenAI speech-to-text (Whisper)
OpenAI text-to-speech

It’s essential to note that Devtorium always uses a combination of technologies and frameworks to achieve the best results. We discuss the client’s ideas and goals in depth to build a product that can achieve them while staying within budget.

Voice AI chatbot development; step-by-step AI tools involved.

Voice AI Chatbot Development Services Overview

In this article, we wanted to focus specifically on the voice bot designed for this project. Below, we will detail exactly how such a solution works and what it can achieve within the current level of technology. However, we’d also like to remind you that this tech is evolving fast. Almost anyone today can build an AI chatbot using LangChain or similar frameworks. You don’t need a coding degree for that. In fact, some platforms are so user-friendly that they can help you build a basic bot with limited capabilities with no tech knowledge.

However, if your business wants to invest in a solution that will give you a competitive advantage, you’ll need a professional-grade tool. The Devtorium’s lead AI specialist, Oleksii Makarov, outlines how to create an AI chatbot that can talk to your clients.

Voice AI chatbot development starts with VoIP

First of all, when creating a voice bot, we need to use a phone service. VoIP technology is quite advanced today, so this won’t be an issue. We chose to use Vonage because it is currently the best option regarding both quality and service versatility.

Speech-to-text: an essential part of AI chatbot development services

Speech-to-text technology is crucial for building an AI voice bot because it enables the machine to process audible information. We use Assebly.ai in our projects because it currently delivers the highest level of accuracy. Most importantly, it’s able to process information effectively in real-time. Therefore, it helps create an illusion that the user is talking to a person instead of a machine.

While working on this project, we noticed that lag is the biggest issue with these chatbots. Simply put, processing data takes time, so the pauses in their responses are too long. Assembly’s processing capabilities help us reduce this time to manageable levels.

Machine Learning models do the powerlifting in data processing

Devtorium uses the GPT engine versions 3.5 and 4 to build the most efficient chatbots for every application. This technology is the leading AI power behind any voice bot because the solution uses it to process data. Basically, it’s your bot’s ‘thinking power’.

We actively use advanced prompt engineering techniques while designing instructions for the bot’s conversations. The main task is creating instructions that enable the bot to learn and grow. The critical task is to make the conversation sound as natural as possible to a human.

Going back through text-to-speech

Now that the data has been processed and the machine has created the response, we use the GPT-provided text-to-speech tool. It works pretty well for the current technology level. However, we are excited because there are some announcements for more advanced features. In addition, we expect to see more voices and ‘emotions’ options quite soon.

It’s great to see this technology developing and including the emotional aspect of conversations. This truly bridges the gap between machines and people. Most importantly, we are sure this will boost the bot’s ability to deliver higher-quality customer services fast.

Back to the phone service

AI chatbot development is a complex process that includes many steps. However, at the final stage, it returns to where it started. In our case, the Vonage phone service is where the bot talks to the customer.

Voice AI Chatbot Development Benefits

The extraordinary thing about using voice bots is that they do not only cut down the cost of outsourcing call center services. Even with the current technology level, we can create a bot that extracts data from spoken conversations in JSON format. In addition, it’s able to send out a call to third-party APIs.

In simple terms, the bot can trigger an application to run in response to your customer’s query. It will also automatically process all data from the conversation and show it to you in the way you choose. This offers limitless opportunities for studying your customers’ preferences, reactions, and interests. Therefore, a voice bot can become your single most valuable tool for interacting with and researching your target audience. It can also initiate various programs or connect the client to a human operator if the machine cannot process the query.

If you want to see how it could work in practice, set up a free consultation with the Devtorium AI team!

Build an AI Chatbot with OpenAI Assistant API and LangChain

Posted on January 12, 2024January 12, 2024 by mkoftielieva

Technology has progressed so far that you can build an AI chatbot with minimum effort today. The world now really looks like some sci-fi stories come to life. OpenAI is one of the businesses standing at the forefront of this technological revolution.

We talked to Devtorium developers about how OpenAI’s Assistant API is reshaping how we develop digital assistants. Have you ever wondered what the development of chatbots looks like? Devtorium’s developers explore this superb AI-powered tool and share a tutorial on how to build a chatbot with Assistant API and LangChain.

Why Choose Assistant API to Build an AI Chatbot

Developers might have vastly different opinions on whether or not AI code-generation tools are helpful. However, everyone with experience working with OpenAI Assistant API agrees this is a magnificent tool.

Assistants API is an NLP(natural language process) API currently available as a beta version. You can already use Assistants API for question answering, language translation, and code generation. However, its primary function is to assist developers in building chatbots within their apps. To start using Assistants API, you must have an OpenAI API account.

Assistants API uses OpenAI-hosted models, access files, persistent threads, and call tools to respond to user queries. According to Devtorium developers, its most prominent and valuable features are:

Code interpreter and retrieval: Access and execute code from various sources.
Function calling mechanism: Call functions from other APIs.
Knowledge base: Store and access information from a variety of sources.
Easy conversation management with threads: Keep track of the context of a conversation.
Support for different models: Choose the best model for your specific task.
Customizable instructions: Control how your assistants respond to user requests and perform tasks.
Easy deployment: Deploy to a variety of platforms.

How to Build an AI Chatbot with Assistant API and LangChain

The range of AI software development services offered by Devtorium is vast. Therefore, we explored multiple AI solutions and technologies available today. One of our developers’ favorites is LangChain, a framework built around LLMs (large language models) and designed to simplify the creation of complex apps. This tool connects components: prompt templates, LLMs, agents, and memory to create a chain, hence the name LangChain.

In order for LangChain to work correctly with Assistants API, make sure you download version 0.0.331rc2 or higher. The latest RC version of LangChain can support Assistant API using an experimental package. The only class you need is OpenAIAssistantRunnable, which makes code much cleaner.

Now, let’s get on with the guide on how to build an AI chatbot using these tools. Using the tips below, you can make an MnA assistant that will answer queries using a retrieval tool. No Chunking, no embeddings, and no vector database are required.

The steps to build an AI chatbot using this approach include:

Create an Assistant API account and get an API key.
Create an Assistant in the API by defining its custom instructions and picking a model.
Install LangChain and create a LangChain project.
Write a script that uses the Assistant API to send and receive messages from users and access and store information from LangChain.
Deploy your chatbot to a web server, messaging platform, or mobile app.

Technical Instructions on Working with Assistant API and LangChain

1. Set up Assistant API

Sign up for an OpenAI account
Get API key

import openai

     openai.api_key = "YOUR_API_KEY"

2. Create an Assistant

Define instructions for the scope of your chatbot, tools it can access, etc.
Pick a model (code recommendation, text embedding, etc.)

assistant = openai.Assistant("assistant name", model="Davinci")

3. Install LangChain

Install LangChain and LangChain-experimental package
Import OpenAIAssistantRunnable

from langchain.llms import OpenAIAssistantRunnable

4. Handle user input and get assistant response

Take user message as input
Process with OpenAIAssistantRunnable to get assistant response

user_message = input("User: ")

     assistant_response = OpenAIAssistantRunnable(assistant).run(prompt=user_message)["response"]

5. Connect LangChain memory

Store data to use across conversations
Access external APIs through LangChain agents

memory = {"context": {}} 

     agent = ExampleAgent()

     assistant_response = OpenAIAssistantRunnable(assistant, memory=memory, agent=agent).run(prompt=user_message)["response"]

6. Deploy chatbot

Wrap in a web app/API, connect to a messaging platform, etc.

Bottom Line: Who Can Build an AI Chatbot with Assistant API and LangChain

As you can see from the post above, anyone with minimal coding knowledge can build an AI chatbot using tools like LangChain and Assistant API. Of course, an average person with no software development background won’t be able to do this unless they learn extensively.

However, the essential factor is that any small business can now access all the benefits of using a chatbot with minimal investment. All you have to do is contact our team and set up a free consultation. Our experts will discuss your ideas and requirements and come up with a plan that will fit your budget.

No reason for any business to not benefit from a chatbot today exists. So, contact us and take the next step in your tech growth!

If you want to learn more about how Devtorium developers work with AI, check out the following articles:

How to Use Photoshop AI Generative Fill

Posted on December 21, 2023December 21, 2023 by mkoftielieva

Photoshop AI Generative Fill, recently introduced as a beta version by Adobe, is truly an incredible tool. Even as limited as it is now, it makes the work of designers and photographers much easier. Moreover, Generative Fill has a high potential to change the image editing industry. In this post, Devtorium`s designers will explain the features of Photoshop AI and show off some of its applications in graphic design services.

What is Photoshop AI Generative Fill?

Generative Fill is an AI solution that can generate or remove parts of an image with minimal effort from the user. In simple terms, it works like this, you select an area of an image and AI can produce realistic textures, patterns, and details that perfectly integrate into the rest of the picture.

The step-by-step process goes like this:

First, you select an area within your image where you want to add content.
Next, you provide text prompts (commands).
After this, Photoshop AI analyzes your prompts and the surrounding image content.
Following the analysis, Generative Fill generates new pixels that match the style, lighting, texture, and details based on your input.
Finally, you receive several updated image preview options and can choose the best one.

Note that Photoshop AI beta is available to all paid Adobe Photoshop subscribers. All you need to do as a registered user is to download and install the solution from the official website.

Our graphic designer, Khrystyna Byelova, has already tried using Photoshop AI Generative Fill when working on photo editing for our social media posts. Take a look at the amazing results it can produce. You can see the prompts she used in the image below.

Photoshop AI Generative Fill use case with prompts

How Generative Fill Benefits Designers

The main benefit of using Photoshop AI is that it saves designers a lot of time, which is usually spent on image editing. AI assistance offers some additional benefits as well:

Contextual image generation that matches the original (objects and backgrounds)
Multiple style options, including textures, patterns, lighting, and details
Layers support for non-destructive editing
Customization: you control the adjustment settings, like Color Adaptation, Rotation Adaptation, and Scale
Faster iteration by generating dozens of variations

Bottom Line: How to Implement Benefits of Photoshop AI for Your Business

If you are a graphic designer or a photographer, you can greatly speed up your work using Generative Fill. However, it’s not only professionals who work with visuals who can benefit from it. Content generated with Photoshop AI can be used across industries ranging from eCommerce to advertising.

The technology is still relatively new, but it continues to improve at a rapid pace. The potential use cases seem endless, whether filling gaps in product images or generating graphical assets for ad campaign concepts. If you want to know how to boost your own business using this tool, contact us for a free consultation. To learn more about the use of AI in design, check out this article about prompt engineering for Midjourney.

Introduction to AI Part 1: Basic AI Terminology Cheat Sheet

Posted on November 16, 2023November 16, 2023 by mkoftielieva

Without a doubt, artificial intelligence technology started a new round of modern progress. Soon, understanding it, at least on a basic level, will become crucial, so you must start learning AI terminology fast. Today, we will begin our series of articles that provide a basic guide to AI by explaining some of the terms essential for understanding this tech. In future articles, we will explain how artificial intelligence works and AI software development services in greater detail.

You can already see how big and small businesses, governments, and ordinary people use AI in everyday activities. When we discover new technology, we try to make it a part of our daily lives. For example, with the invention of near-field communication (NFC), humankind came up with the idea of using it as a way of payment. Now, no one is surprised to see others paying with just a phone or hear NFC in regular conversation.

The same thing is happening to AI. First of all, AI is a set of techniques that imitates human behavior and completes the tasks that would usually require human intelligence. According to Forbes, the AI market is expected to reach $407 billion by 2027. Thus, providing yourself with AI techs is a win-win investment in your business success.

AI Terminology Cheat Sheet (in Alphabetical Order)

Chatbot

A chatbot is an app made to simulate human conversations. It uses NLP to process inputs and generative AI to automate responses. A chatbot can perform multiple functions. For example, it can assist businesses from within or to engage customers.

Computer Vision

Computer vision is a set of AI technologies that allows machines to analyze and interpret visual content. Driven by deep learning models, it can recognize patterns, objects, and even emotions in images. In addition, computer vision can process dynamic content and perform gesture recognition and motion analysis. Today, computer vision can analyze images at extreme speeds and identify objects with 99% accuracy. This technology is applied in various industries, from security to healthcare and self-driving vehicles.

Datasets

Datasets – are large collections of various types of digital data. They are a crucial element of any ML algorithm and are used to train AI systems to complete assigned tasks. The most popular public datasets are Kaggle, UCI, ImageNet, and Quandl.

Deep Learning (DL)

It is a subset of machine learning that relies on neural networks trained on massive amounts of data. In this context, “deep” refers to the use of multiple layers in the network. As AI networks become more complex, the importance of deep learning in the scope of AI terminology increases.

Generative AI

Generative AI is an AI algorithm whose primary function is to generate new output from the training dataset. Whereas traditional AI models mostly follow predefined rules to respond to inputs, generative AI can produce variable content such as images, video, text, and code. Also, it can create outputs either in the same prompted medium, like text-to-text, or in a different one, like text-to-image or image-to-video. A great example of generative AI is Midjourney, with its multiple features.

Large Language Models (LLMs)

LLMs are machine learning models trained on vast amounts of textual data. The most well-known example of an LLM is ChatGPT. A part of Devtorium AI software development services includes creating and training LLMs to deliver the output you need automatically.

Machine Learning (ML)

ML is a subset of AI development that enables machines to self-learn when dealing with specific tasks without preset coding. It’s one of the most basic terms in AI terminology today. There are many machine learning types and methods. Most of them use conditions (ifs), cycles, and internal variables to train the system`s algorithm. The developer needs to train the model using training datasets but does not change the code itself. Then, the model can be saved, loaded, or used to process new data.

Natural Language Processing (NLP)

The ability of AI-driven systems to analyze, comprehend, and generate human language is called NLP. The three main parts of any NLP are computer science, human language, and AI. Applications that require NLP include chatbots, text generators, translation tools, and autocorrect solutions. Chatbots, like Amazon’s Alexa and Apple’s Siri, utilize NLP to process user queries and find answers.

Neural Networks

Neural networks are models of teaching machines to recognize underlying patterns. The name refers to neurons in the human brain. It’s because the way this network operates looks pretty similar to them. This kind of structure enables neural networks to handle more complicated challenges than traditional programming.

Prompt Engineering

Prompt engineering is the process of creating specific instructions for LLMs to generate desired outputs. The instruction usually contains information on the way and form of outputting content. It’s a handy skill in the modern world because it allows users to interact with AI more efficiently.

What’s the Next Step After Learning Basic AI Terminology?

This little AI terminology cheat sheet should help you understand essential AI-related content. In order to explain the matter more deeply, we will continue expanding this AI terms glossary and explain how artificial intelligence is built and trained in our upcoming articles.

However, if you want to learn how exactly AI can benefit your business today, contact our experts to set up a free consultation.

For those of you who want to continue learning about artificial intelligence technologies, check out our older posts: