Category Robotics Classification

Over the past few years, engineers have developed increasingly advanced robotic systems already introduced in some public spaces and could soon be deployed in home environments. Many of these robots are humanoids, meaning that their body structure and physical features resemble those of humans.

From mountain goats that run up near-vertical rock faces to armadillos that roll into a protective ball, animals have evolved to adapt effortlessly to changes in their environment. In contrast, when an autonomous robot is programmed to reach a goal, each variation in its pre-determined path presents a significant physical and computational challenge.

Springtails, small bugs often found crawling through leaf litter and garden soil, are expert jumpers. Inspired by these hopping hexapods, roboticists have made a walking, jumping robot that pushes the boundaries of what small robots can do. The research glimpses a future where nimble microrobots can crawl through tiny spaces, skitter across dangerous ground, and sense their environments without human intervention.

A bioinspired robot can change shape to alter its own physical properties in response to its environment, resulting in a robust and efficient autonomous vehicle as well as a fresh approach to robotic locomotion.

A research team demonstrated the 'world's smallest shooting game,' a unique nanoscale game inspired by classic arcade games. This achievement was made possible by real-time control of the force fields between nanoparticles using focused electron beams. This research has practical applications, as the manipulation of nanoscale objects could revolutionize biomedical engineering and nanotechnology.

A low-energy challenger to the quantum computer also works at room temperature. The researchers have shown that information can be transmitted using magnetic wave motion in complex networks.

A team of scientists has now created a computer model that can represent and generate human-like goals by learning from how people create games. The work could lead to AI systems that better understand human intentions and more faithfully model and align with our goals. It may also lead to AI systems that can help us design more human-like games.

Springtails, small bugs often found crawling through leaf litter and garden soil, are expert jumpers. Inspired by these hopping hexapods, roboticists at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have made a walking, jumping robot that pushes the boundaries of what small robots can do.

How Much Does Stock Trading App Development Cost?

Fintech has revolutionized the way people trade their stocks. The stocks can now be traded even more easily than before. There are many apps created for trading stocks and options in the stock market. Investors can now trade, monitor trends in the stock market, or manage their portfolios directly from their cell phones. Where there is demand, however, development of stock trading apps would be a great strategy. In this blog, we will discuss the development cost of a stock trading application, current trends that arise, and some of the essential features and technologies involved in the development process.

Current Top Trends in Stock Trading App Development

1. AI and Machine Learning Rise

The role of Artificial Intelligence (AI) and machine learning is becoming increasingly significant in the stock trading apps. These technologies help to analyze vast data providing personalized investment advice, predictive analytics, and automated trading options.

Stock trading apps are becoming prominent in AI and machine learning. These technologies will allow analysis of large volumes of data and much more to contribute to the realm of investment advice, analytics, and automated trading capabilities.

2. Social Trading Features

Social trading has grown within the platform because one can simply follow and copy the trades of other good investors. These systems tend to be community-based and encourage novices to start trading with the platform.

3. Integration with Cryptocurrencies

As cryptocurrencies become more mainstream, trading applications are jumping into the fray by including digital assets. In addition to traditional trading of stocks, giving a better user base to apps to use them for cryptocurrencies would also be good.

4. Enhanced Security Features

With growing data breaches, they have been developing features more related to security enhancement, like two-factor authentication (2FA), biometric login, and end-to-end encryption.

5. Robo-Advisory Services

Robo-advisors offer automated, algorithm-driven financial planning services with minimal human intervention. They can be used for better portfolio diversification by risk tolerance levels and investment purposes.

Essential Features of Stock Trading Apps

While developing a stock trading application, there should be features that bring comfort and functionality to its use. These are some essential features:

1. User Registration and Profile Management

The onboarding process of the stock trading app has to be as smooth as possible. It should make it easy for users to create an account, manage profiles, and set customizable preferences.

2. Real-Time Market Data

Financial news that keeps a user connected with actual time stock prices and market trends is very important for traders. It can keep users informed through the integration of APIs providing real-time data feeds.

3. Order Execution

The heart of any trading app is that it lets users place trades. This includes such functions as: Trades, opportunities, and being able to see order history.

4. Portfolio Management

Users should be able to follow and manage their investment portfolio. This includes performance tracking, tracking profits and losses, and obtaining diversification insights.

5. Charts and Analytics Tools

Advanced charting and analytics implementation enables users to make trading decisions. Technical indicators, bar charts, and historical data are some of the features that are involved in this implementation.

6. Notifications and Alerts

There is push notification for changes in prices, news from the stock market, and order executions, which keep the user noticed and engaged with his investments.

7. Payment Integration

Deposits and withdrawals should always be assured to pass through secure payment gateways. Various payment options such as credit/debit card, bank transfer, and even digital wallets make it easier for users.

8. Customer Support

In-app customer support through AI chatbots or live agents helps in answering the questions of users promptly and increases user satisfaction in general.

9. Educational Resources

Educational content, such as articles, videos, and webinars, can prove useful in helping users gain the maximum out of their trading capabilities and knowledge.

Technologies Used in Stock Trading App Development

The making of a stock trading app consists of all the technologies that have to be amalgamated in order to provide functionality, security, and user engagement. These include the following primary technologies that will be mainly used during the course,

1. Frontend Development

The user interface is fundamental to the engagement of the end-user. Following are the quite commonly used technologies for frontend development:

• React Native: Used for cross-platform app building.
• Flutter: This is another framework for making natively compiled applications for mobile, web, and desktop in a single codebase.
• HTML/CSS/JavaScript: For developing web applications.

2. Backend Development

Back-end takes care of processing data and executing trades. The most used are:

• Node.js: For developing scalable network applications.
  • Python: Becoming very popular because of simplicity and libraries, especially in finance.
  • Java: It is a reliable and scalable technology at the enterprise level.

3. Database Management

User data, transaction history, and real-time market data need a very high-performance database to be stored in. There are several, but among the most common are:

• Mongo DB: NoSQL database. Flexible.
• MySQL: Traditional relational database. Strong data integrity

4. APIs (Application Program Interface)

There are various APIs that should be included in your application to give functionalities like real-time data feeds, payment processing, and trading capabilities. Some of the most extremely common ones are:

• Alpha Vantage: Stock and crypto data.
  • Plaid: User bank account, safe end.
  •Stripe or PayPal: To process payments.

5. Security Protocols

The safety of user’s details and transactions is essential. The following technologies should be in use:

• OAuth: This is authorization with safety.
• SSL/TLS: This encrypts information being sent over the internet.
• Blockchain: This is implemented to add security in transactions, as well as the integrity of data.

Stock Trading App Development Cost

The cost of developing a stock trading app is quite variable, based on several factors, such as the features developed, platform, complexity, and geolocation of the app development company. So here is a list of the factors on which the cost influences the development process:

1. App Complexity

• Basic Apps: These might include basic abilities, including user registration, minimal trading functions, and portfolio management capabilities. Prices may be in between $ 30,000 – $ 50,000.
• Mid-Level Apps: Features installed will comprise real-time data, analytics, and payments. Development cost range from $50,000 to $150,000.
• Advanced Apps: This comprises features such as social trading, AI analytics, and robo-advisory services. Costs can reach up to $150,000 to $300,000 or more.

2. App Development Platform Choice

• iOS vs. Android: Creating your app for both platforms will triple the cost. However, creating it using cross-platform frameworks such as React Native or Flutter helps save some of the costs involved.

3. Location of the Development Team

The location of your development team can significantly affect the cost. Let’s do a rough estimate here:

• North America: $150 to $250 per hour.
• Western Europe: $100 to $200 per hour.
• Eastern Europe: $30 to $100 per hour.
• India and Asia: $20 to $80 an hour.

4. Maintenance and Updates

There will be an ongoing cost after launch for maintenance. Allocate 15-20% of the initial development budget as annual updates and support.

Conclusion

The demand for stock trading apps continues to grow as technology and consumer expectations evolve. Understanding the trends, essential features, and technologies involved in development will help businesses create successful applications. The cost of development of stock apps can vary widely based on complexity, platform, and location, so careful planning and budgeting are essential. By investing in a well-designed, feature-rich trading app, you can tap into the lucrative fintech market and provide users with a valuable tool for managing their investments.

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AI security is no longer optional. By 2026, organizations that fail to infuse transparency, trust, and security into their AI initiatives could see a 50% decline in model adoption, business goal attainment, and user acceptance – falling behind those that do.

At the same time, AI leaders are grappling with another challenge: rising costs.

They’re left asking: “Are we investing in alignment with our goals—or just spending more?”

With the right strategy, AI technology investments shift from a cost center to a business enabler — protecting investments and driving real business value.

The financial fallout of AI failures

AI security goes beyond protecting data. It safeguards your company’s reputation, ensures that your AI operates accurately and ethically, and helps maintain compliance with evolving regulations.

Managing AI without oversight is like flying without navigation. Small deviations can go unnoticed until they require major course corrections or lead to outright failure.

Here’s how security gaps translate into financial risks:

Reputational damage

When AI systems fail, the fallout extends beyond technical issues. Non-compliance, security breaches, and misleading AI claims can lead to lawsuits, erode customer trust, and require costly damage control.

• Regulatory fines and legal exposure. Non-compliance with AI-related regulations, such as the EU AI Act or the FTC’s guidelines, can result in multimillion-dollar penalties.
  
  Data breaches in 2024 cost companies an average of $4.88 million, with lost business and post-breach response costs contributing significantly to the total.
  
  
• Investor lawsuits over misleading AI claims. In 2024, several companies faced lawsuits for “AI washing” lawsuits, where they overstated their AI capabilities and were sued for misleading investors.
  
  
• Crisis management efforts for PR and legal teams. AI failures demand extensive PR and legal resources, increasing operational costs and pulling executives into crisis response instead of strategic initiatives.
  
  
• Erosion of customer and partner trust. Examples like the SafeRent case highlight how biased models can alienate users, spark backlash, and drive customers and partners away.
  

Weak security and governance can turn isolated failures into enterprise-wide financial risks.

Shadow AI

Shadow AI occurs when teams deploy AI solutions independently of IT or security oversight, often during informal experiments. 

These are often point tools purchased by individual business units that have generative AI or agents built-in, or internal teams using open-source tools to quickly build something ad hoc.

These unmanaged solutions may seem harmless, but they introduce serious risks that become costly to fix later, including:

• Security vulnerabilities. Untracked AI solutions can process sensitive data without proper safeguards, increasing the risk of breaches and regulatory violations.
  
  
• Technical debt. Rogue AI solutions bypass security and performance checks, leading to inconsistencies, system failures, and higher maintenance costs
  

As shadow AI proliferates, tracking and managing risks becomes more difficult, forcing organizations to invest in expensive remediation efforts and compliance retrofits.

Expertise gaps

AI governance and security in the era of generative AI requires specialized expertise that many teams don’t have.

With AI evolving rapidly across generative AI, agents, and agentic flows, teams need security strategies that risk-proof AI solutions against threats without slowing innovation.

When security responsibilities fall on data scientists, it pulls them away from value-generating work, leading to inefficiencies, delays, and unnecessary costs, including:

• Slower AI development. Data scientists are spending a lot of time figuring out which shields, guards are best to prevent AI from misbehaving and ensuring compliance, and managing access instead of developing new AI use-cases.
  
  In fact, 69% of organizations struggle with AI security skills gaps, leading to data science teams being pulled into security tasks that slow AI progress.
  
  
• Higher costs. Without in-house expertise, organizations either pull data scientists into security work — delaying AI progress — or pay a premium for external consultants to fill the gaps.
  

This misalignment diverts focus from value-generating work, reducing the overall impact of AI initiatives.

Complex tooling

Securing AI often requires a mix of tools for:

• Model scanning and validation
• Data encryption
• Continuous monitoring
• Compliance auditing
• Real-time intervention and moderation
• Specialized AI guards and shields 
• Hypergranular RBAC, with generative RBAC for accessing the AI application, not just building it

While these tools are essential, they add layers of complexity, including:

• Integration challenges that complicate workflows and increase IT and data science team demands.
  
  
• Ongoing maintenance that consumes time and resources.
  
  
• Redundant solutions that inflate software budgets without improving outcomes.
  

Beyond security gaps, fragmented tools lead to uncontrolled costs, from redundant licensing fees to excessive infrastructure overhead.

What makes AI security and governance difficult to validate?

Traditional IT security wasn’t built for AI. Unlike static systems, AI systems continuously adapt to new data and user interactions, introducing evolving risks that are harder to detect, control, and mitigate in real time. 

From adversarial attacks to model drift, AI security gaps don’t just expose vulnerabilities — they threaten business outcomes.

New attack surfaces that traditional security miss

Generative AI solutions and agentic systems introduce unique vulnerabilities that don’t exist in conventional software, demanding security approaches beyond what conventional cybersecurity measures can address, such as

• Prompt injection attacks: Malicious inputs can manipulate model outputs, potentially spreading misinformation or exposing sensitive data.
  
  
• Jailbreaking attacks: Circumventing guards and shields put in place to manipulate outputs of any existing generative solutions.
  
  
• Data poisoning: Attackers compromise model integrity by corrupting training data, leading to biased or unreliable predictions.
  

These subtle threats often go undetected until damage occurs.

Governance gaps that undermine security

When governance isn’t airtight, AI security isn’t just harder to enforce — it’s harder to verify.

Without standardized policies and enforcement, organizations struggle to prove compliance, validate security measures, and ensure accountability for regulators, auditors, and stakeholders.

• Inconsistent security enforcement: Gaps in governance lead to uneven application of AI security policies, exposing different AI tools and deployments to varying levels of risk.
  
  One study found that 60% of Governance, Risk, and Compliance (GRC) users manage compliance manually, increasing the likelihood of inconsistent policy enforcement across AI systems.
  
  
• Regulatory blind spots: As AI regulations evolve, organizations lacking structured oversight struggle to track compliance, increasing legal exposure and audit risks.
  
  A recent analysis revealed that approximately 27% of Fortune 500 companies cited AI regulation as a significant risk factor in their annual reports, highlighting concerns over compliance costs and potential delays in AI adoption.
  
  
• Opaque decision-making: Insufficient governance makes it difficult to trace how AI solutions reach conclusions, complicating bias detection, error correction, and audits.
  
  For example, one UK exam regulator implemented an AI algorithm to adjust A-level results during the COVID-19 pandemic, but it disproportionately downgraded students from lower-income backgrounds while favoring those from private schools. The resulting public backlash led to policy reversals and raised serious concerns about AI transparency in high-stakes decision-making.
  

With fragmented governance, AI security risks persist, leaving organizations vulnerable.

Lack of visibility into AI solutions

AI security breaks down when teams lack a shared view. Without centralized oversight, blind spots grow, risks escalate, and critical vulnerabilities go unnoticed.

• Lack of traceability: When AI models lack robust traceability — covering deployed versions, training data, and input sources — organizations face security gaps, compliance breaches, and inaccurate outputs. Without clear AI blueprints, enforcing security policies, detecting unauthorized changes, and ensuring models rely on trusted data becomes significantly harder.
  
  
• Unknown models in production: Inadequate oversight creates blind spots that allow generative AI tools or agentic flows to enter production without proper security checks. These gaps in governance expose organizations to compliance failures, inaccurate outputs, and security vulnerabilities — often going unnoticed until they cause real damage.
  
  
• Undetected drift: Even well-governed AI solutions degrade over time as real-world data shifts. If drift goes unmonitored, AI accuracy declines, increasing compliance risks and security vulnerabilities.
  

Centralized AI observability with real-time intervention and moderation mitigate risks instantly and proactively.

Why AI keeps running into the same dead ends

AI leaders face a frustrating dilemma: rely on hyperscaler solutions that don’t fully meet their needs or attempt to build a security framework from scratch. Neither is sustainable.

Using hyperscalers for AI security

Although hyperscalers may offer AI security features, they often fall short when it comes to cross-platform governance, cost-efficiency, and scalability. AI leaders often face challenges such as:

• Gaps in cross-environment security: Hyperscaler security tools are designed primarily for their own ecosystems, making it difficult to enforce policies across multi-cloud, hybrid environments, and external AI services.
  
  
• Vendor lock-in risks: Relying on a single hyperscaler limits flexibility, increases long-term costs, especially as AI teams scale and diversify their infrastructure, and limits essential guards and security measures.
  
  
• Escalating costs: According to a DataRobot and CIO.com survey, 43% of AI leaders are concerned about the cost of managing hyperscaler AI tools, as organizations often require additional solutions to close security gaps. 
  

While hyperscalers play a role in AI development they aren’t built for full-scale AI governance and observability. Many AI leaders find themselves layering additional tools to compensate for blind spots, leading to rising costs and operational complexity.

Building AI security from scratch 

The idea of building a custom security framework promises flexibility; however, in practice, it introduces hidden challenges:

• Fragmented architecture: Disconnected security tools are like locking the front door but leaving the windows open — threats still find a way in.
  
  
• Ongoing upkeep: Managing updates, ensuring compatibility, and maintaining real-time monitoring requires continuous effort, pulling resources away from strategic projects.
  
  
• Resource drain: Instead of driving AI innovation, teams spend time managing security gaps, reducing their business impact.
  

While a custom AI security framework offers control, it often results in unpredictable costs, operational inefficiencies, and security gaps that reduce performance and diminish ROI.

How AI governance and observability drive better ROI

So, what’s the alternative to disconnected security solutions and costly DIY frameworks?

Sustainable AI governance and AI observability. 

With robust AI governance and observability, you’re not just ensuring AI resilience, you’re optimizing security to keep AI projects on track.

Here’s how:

Centralized oversight

A unified governance framework eliminates blind spots, facilitating efficient management of AI security, compliance, and performance without the complexity of disconnected tools. 

With end-to-end observability, AI teams gain:

• Comprehensive monitoring to detect performance shifts, anomalies, and emerging risks across development and production.
  
  
• AI lineage, traceability, and tracking to ensure AI integrity by tracking prompts, vector databases, model versions, applied safeguards, and policy enforcement, providing full visibility into how AI systems operate and comply with security standards.
  
  
• Automated compliance enforcement to proactively address security gaps, reducing the need for last-minute audits and costly interventions, such as manual investigations or regulatory fines.
  

By consolidating all AI governance, observability and monitoring into one unified dashboard, leaders gain a single source of truth for real-time visibility into AI behavior, security vulnerabilities, and compliance risks—enabling them to prevent costly errors before they escalate.

Automated safeguards 

Automated safeguards, such as PII detection, toxicity filters, and anomaly detection, proactively catch risks before they become business liabilities.

With automation, AI leaders can:

• Free up high-value talent by eliminating repetitive manual checks, enabling teams to focus on strategic initiatives.
  
  
• Achieve consistent, real-time coverage for potential threats and compliance issues, minimizing human error in critical review processes.
  
  
• Scale AI fast and safely by ensuring that as models grow in complexity, risks are mitigated at speed.
  

Simplified audits

Strong AI governance simplifies audits through:

• End-to-end documentation of models, data usage, and security measures, creating a verifiable record for auditors, reducing manual effort and the risk of compliance violations.
  
  
• Built-in compliance tracking that minimizes the need for last-minute reviews.
  
  
• Clear audit trails that make regulatory reporting faster and easier.
  

Beyond cutting audit costs and minimizing compliance risks, you’ll gain the confidence to fully explore and leverage the transformative potential of AI.

Reduced tool sprawl

Uncontrolled AI tool adoption leads to overlapping capabilities, integration challenges, and unnecessary spending. 

A unified governance strategy helps by:

• Strengthening security coverage with end-to-end governance that applies consistent policies across AI systems, reducing blind spots and unmanaged risks.
  
  
• Eliminating redundant AI governance expenses by consolidating overlapping tools, lower licensing costs, and lowering maintenance overhead.
  
  
• Accelerating AI security response by centralizing monitoring and altering tools to enable faster threat detection and mitigation. 
  

Instead of juggling multiple tools for monitoring, observability, and compliance, organizations can manage everything through a single platform, improving efficiency and cost savings.

Secure AI isn’t a cost — it’s a competitive advantage

AI security isn’t just about protecting data; it’s about risk-proofing your business against reputational damage, compliance failures, and financial losses.

With the right governance and observability, AI leaders can:

• Confidently scale and implement new AI initiatives such as agentic flows without security gaps slowing or derailing progress.
  
  
• Elevate team efficiency by reducing manual oversight, consolidating tools, and avoiding costly security fixes.
  
  
• Strengthen AI’s revenue impact by ensuring systems are reliable, compliant, and driving measurable results.
  

For practical strategies on scaling AI securely and cost-effectively, watch our on-demand webinar.

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This brand-new regional show promises to connect industry leaders, innovators and decision-makers while showcasing cutting-edge technologies that will shape the future of manufacturing across more than 40 vertical markets.

Many industrial processes and household tasks currently completed by humans entail the manipulation of textiles, including clothes, sheets, towels, cloths and other fabric-based objects. Most robotic systems developed so far do not reliably manipulate all types of textiles, due to challenges associated with predicting how these objects will deform when grasped and handled.

Indoor search and rescue operations are some of the most dangerous tasks that law enforcement and first responders must face, but drone technology has revolutionized how they approach these intense situations, according to graduate students in Penn State's Autonomous Robotics Competition Club (ARCC). Drones can be used to locate people in claustrophobic, dark and GPS-limited environments like collapsed buildings, but developing these drones is difficult and expensive.

Gemini 2.0 Flash-Lite is now generally available in the Gemini API for production use in Google AI Studio and for enterprise customers on Vertex AI

How does a tennis player like Carlos Alcaraz decide where to run to return Novak Djokovic's ball by just looking at the ball's initial position? These behaviours, so common in elite athletes, are difficult to explain with current computational models, which assume that the players must continuously follow the ball with their eyes. Now, researchers have developed a model that, by combining optical variables with environmental factors such as gravity, accurately predicts how a person will move to catch a moving object just from an initial glance. These results could have potential applications in fields such as robotics, sports training or even space exploration.