SwissCognitive Guest Blogger: Zachary Amos – “How AI Reduces Physician Burnout”

Burnout rates among health care providers have been on the rise since the pandemic, with severe implications for broader public health. Advanced artificial intelligence systems can play a critical role in addressing this challenge, improving work-life balance among physicians for better patient outcomes. Explore the different ways to implement AI and machine learning (ML) to help mitigate physician burnout.

1. Automating Administrative Tasks

In a recent survey of 1,000 medical professionals, 93% said they felt burned out regularly. Nearly 50% considered quitting their jobs or stopping patient consultations altogether because of the pressures associated with the job.

One of the biggest causes of this work fatigue is excessive administrative tasks, such as scheduling, billing and documentation. According to Statista, 62% of U.S. physicians in the United States found these bureaucratic functions increasingly cumbersome, contributing immensely to burnout.

AI can automate routine administrative duties, allowing doctors and nurses to focus more on patient care. For example, AI-powered natural language processing (NLP) tools can transcribe physician-patient conversations into electronic health records, reducing the time spent on data entry. Physicians must carefully review notes before uploading them, but the time saved is still significant and can go a long way in reducing work stress.

2. Addressing Staff Shortages

The health care sector has been coping with a sustained worker deficiency since the pandemic — and things appear to be worsening. Experts estimate the U.S. will face a shortfall of 124,000 physicians and nearly 200,000 nurses by 2030.

As a result, medical staff spend more hours at work, increasing job dissatisfaction. Around 37% of burnout cases stem from this issue, highlighting the need for innovative solutions.

AI tools can effectively bridge gaps in health care worker shortages by automating routine monitoring tasks, allowing doctors to focus on more complex patient needs. For instance, these systems can continuously track vital signs and health metrics, alerting providers only when intervention is necessary. These real-time insights enhance decision-making capabilities for existing staff, enabling them to manage larger patient loads more efficiently.

3. Predictive Analytics for Patient Management

Health care analytics can contribute to stress and burnout by overwhelming doctors with excessive data and complex reports requiring time-consuming analysis. The pressure to quickly interpret vast amounts of information can lead to cognitive overload, reducing job satisfaction.

Additionally, analytics tools that are poorly integrated into workflows can create inefficiencies, forcing physicians to spend more time on non-patient tasks. This imbalance between data demands and actual clinical work can exacerbate feelings of frustration, ultimately leading to increased burnout.

Advanced ML systems can identify patterns and anomalies in patient data much quicker than humans, allowing for proactive care management. For example, AI predictive analytics can enhance early disease detection with up to 95% accuracy, alleviating the workload burden on physicians. These insights can also help forecast which patients are at risk of hospital readmission, enabling early intervention.

4. Bridging Training Gaps

Inadequate training or resources can significantly contribute to burnout. Physicians become frustrated when unprepared for work challenges, whether due to rapid advancements in medical technology, evolving treatment protocols or complex patient cases.

ML-driven platforms can assess individual knowledge gaps and learning preferences to create tailored training programs. These systems can work directly with AI-based virtual reality and augmented reality simulations, allowing health care professionals to practice procedures in a risk-free environment.

This targeted training helps physicians feel more prepared, improving decision-making and reducing anxiety during patient care. Additionally, increasing competence and procedure familiarity can shorten the learning curve, allowing doctors to manage their workloads more effectively and ultimately decreasing burnout.

AI Is Vital to Physician Fulfillment

Integrating AI in health care presents a promising avenue for reducing stress and improving overall career fulfillment. AI and ML systems can improve work-life balance by automating administrative tasks, addressing skilled labor shortages and streamlining clinical processes. As these technologies evolve, their potential impact on physician well-being will likely grow, contributing to improved job satisfaction and patient care quality.

About the Author:

Zachary Amos is the Features Editor at ReHack, where he writes about artificial intelligence, cybersecurity and other technology-related topics.

Der Beitrag How AI Reduces Physician Burnout erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: iccwbo.org – “Overarching Narrative on Artificial Intelligence”

Artificial intelligence is revolutionising global industries by augmenting human abilities in areas such as language processing, generating creative content, predictive analytics and analytical reasoning, as well as learning and decision-making.

As AI continues to shape economies and societies, a robust governance model becomes essential to harness its benefits while mitigating risks.

ICC outlines the four pillars of global AI governance from the perspective of global business:

1. Principles and codes of conduct
2. Regulation
3. Technical standards
4. Industry self-regulation

Each pillar plays a crucial role in fostering trustworthy, responsible and ethical AI development.

We show, how by adhering to these frameworks, businesses drive innovation, ensure compliance, and build trust, contributing to sustainable and equitable growth.

What is artificial intelligence?

Artificial intelligence is a technology that enables the simulation or extension of human intelligence in machines, allowing them to perform tasks commonly associated with human intelligence, such as speech recognition, content creation, problem-solving, learning, and decision-making, with the potential to boost productivity and augment creativity.

What are the four pillars of global artificial intelligence governance?

As AI continues to evolve, it is essential to strike a balance between realising its full potential for socioeconomic development, while ensuring that it aligns with globally shared values and principles that foster

• equality,
• transparency,
• accountability,
• fairness,
• reliability,
• privacy
• and a human-centric approach.

Over the past decade, this has created an increasingly complex, multi-layered policy environment and a proliferation of policy and regulatory approaches which are sometimes duplicative. These different approaches are gaining rapid momentum, as the technology continues to speed ahead.

The current global governance model for AI is based on four pillars:[…]

Read more: www.iccwbo.org

Der Beitrag Overarching Narrative on Artificial Intelligence erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: technologyreview.com – “Readying Business For The Age Of AI”

Rapid advancements in AI technology offer unprecedented opportunities to enhance business operations, customer and employee engagement, and decision-making. Executives are eager to see the potential of AI realized. Among 100 c-suite respondents polled in WNS Analytics’ “The Future of Enterprise Data & AI” report, 76% say they are already implementing or planning to implement generative AI solutions. Among those same leaders, however, 67% report struggling with data migration, and others cite grappling with data quality, talent shortages, and data democratization issues.

MIT Technology Review Insights recently had a conversation with Alex Sidgreaves, chief data officer at Zurich Insurance; Bogdan Szostek, chief data officer at Animal Friends Insurance; Shan Lodh, director of data platforms at Shawbrook Bank; and Gautam Singh, head of data, analytics, and AI at WNS Analytics, to discuss how enterprises can navigate the burgeoning era of AI.

AI across industries

There is no shortage of AI use cases across sectors. Retailers are tailoring shopping experiences to individual preferences by leveraging customer behavior data and advanced machine learning models. Traditional AI models can deliver personalized offerings. However, with generative AI, these personalized offerings are elevated by incorporating tailored communication that considers the customer’s persona, behavior, and past interactions. In insurance, by leveraging generative AI, companies can identify subrogation recovery opportunities that a manual handler might overlook, enhancing efficiency and maximizing recovery potential. Banking and financial services institutions are leveraging AI to bolster customer due diligence and enhance anti-money laundering efforts by leveraging AI-driven credit risk management practices. AI technologies are enhancing diagnostic accuracy through sophisticated image recognition in radiology, allowing for earlier and more precise detection of diseases while predictive analytics enable personalized treatment plans.[…]

Read more: www.technologyreview.com

Der Beitrag Readying Business For The Age Of AI erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: cio.com – “The Role Of AI In Operational Efficiency: Beyond The Silver Bullet”

AI has transformational power, but for most enterprises, focusing on operational efficiency rather than miracles is far more valuable.

Artificial Intelligence (AI) has earned a reputation as a silver bullet solution to a myriad of modern business challenges across industries. From improving diagnostic care, to revolutionizing the customer experience, there are many industries and organizations that have experienced the true transformational power of AI.

However, that’s not the case for the masses. And organizations that view AI as a fix-all are missing a huge opportunity—and are also likely to encounter significant challenges. When AI is applied in a way that overemphasizes its strengths and downplays its weaknesses, that’s when we run into problems.

While we tend to hear more about innovative, breakthrough AI use cases, the real value of AI lies in its ability to vastly improve operational efficiency. Is it less exciting than AI writing and producing its own songs or creating fine art in a matter of seconds? For sure. But for most businesses, a catchy tune or pretty picture aren’t going to move the needle.

The strengths of AI in modern business

AI’s ability to automate tasks, reduce errors, and make data-driven decisions at scale are its best lauded strengths. From predictive analytics to natural language processing (NLP), AI-powered applications enable faster and more accurate decision-making. In other words, the allure of AI lies in its ability to process vast amounts of data quickly, identify patterns that might be invisible to humans, and adapt to new information in real time.

These capabilities are undeniably valuable. In sectors like finance, healthcare, and manufacturing, AI-driven solutions have already proven their worth by optimizing supply chains, improving risk management, and enhancing customer service.[…]

Read more: www.cio.com

Der Beitrag The Role Of AI In Operational Efficiency: Beyond The Silver Bullet erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Dilshad Durani – “How AI-Generated Creative Assets For Ads Help B2B Marketers?”

Do you know that well-known platforms including Google and Linkedin are adapting to AI-generated creative assets for ads?

They are doing so as it helps them provide advanced tools that satisfy B2B marketers’ requirements for improving their advertising strategy. Read this blog to find out more about how AI-generated creative assets for ads help B2B marketers.

Power of AI: How AI-Generated Creative Assets are Proven Game Changers for B2B Marketing?

AI is used by marketers for one or other purposes. According to a Statista report, more than 90% of marketing professionals across 35 countries use AI to automate customer interactions, while 88% of them use it to personalize the customer journey across channels.

Source: Statista

AI-generated creative assets provide various benefits across various aspects of advertising strategies. AI technology usage can help marketers streamline business processes, improve efficiency, and scale campaigns.

Implementing AI optimizes ad spending after analyzing data to target audiences with personalized content which ultimately leads to improved ROI. Moreover, this modern technology ensures consistency in advertising. AI helps to improve productivity and implement innovative approaches to engage customers, helping B2B marketers to grow in today’s competitive market.

LinkedIn’s Innovations with AI in Advertising

LinkedIn has recently introduced several enhancements aimed at empowering B2B marketers through AI-generated creative assets. Wire Programs is one of them, it can be integrated in-stream video ads with premium publisher content. It expands B2B ads’ reach and also ensures the ads are placed in relevant contexts.

Additionally, LinkedIn’s Accelerate tool utilizes AI to streamline campaign management. It offers functionalities like Microsoft Designer integration for creative customization, enhanced targeting capabilities through exclusion lists, and an AI marketing assistant that provides valuable campaign guidance.

Google’s Approach to AI in Advertising

AI-generated creative assets for ads are also introduced by Google which delegates businesses to provide personalized experiences to their targeted audience. The transformation of artificial intelligence helps marketers redefine how the ad campaign is carried out to reap fruitful results.

With Google adopting this change, AI-driven ads are set to change the face of B2B marketing, opening possibilities of budding strong connections with their targeted audience on a deeper level via innovative approaches.

Generative AI use enables brands to automatically create assets that meet their branding guideline. This can prove to be beneficial for painting consistency across all the advertising channels. Besides it also ensures that your ads campaign helps you provide a personalized experience to your targeted audience.

The immersive ad format is also being introduced by Google, these include:

• Virtual try-ons
• 3D visualizations

The above-listed ad format uses AI to provide an improved experience to your targeted audience. This empowers brands to showcase their products and services in more improved and engaging ways which was not possible for them previously.

YouTube’s Innovative Ad Strategies: Spotlight Moments

YouTube pioneers unique AI-powered ad strategies with initiatives like Spotlight Moments. This advanced functionality helps to identify the trendy moments including award shows or sports events, facilitating advertisers to run ads across relevant streaming content. This helps B2B marketers ensure that their ads can create maximum engagement across all YouTube channels.

Two campaigns further import the effectiveness, these include:

• YouTube’s AI-driven Video Reach
• YouTube’s AI-driven Video View

The above-listed approaches have resulted in significant growth in reach. Compared to the traditional approach it results in a per-impression cost decrease. YouTube’s AI-driven ad delivery and targeting helps marketers improve their ad reach and deliver engaging messages.

Incorporating a streaming platform like YouTube into AI-powered advertising strategies allows brands to invest in platform engagement capabilities. This helps to boost the ad’s visibility and ensure that it is visible to viewers at the right time, resulting in rising business profits.

The video-sharing platforms remain at the top as they continuously adapt to change with AI-driven ad solutions like Spotlight Moments. This helps YouTube to provide unparalleled opportunities for marketers to reach their targeted ways in meaningful ways.

Disney’s Implementation of AI in Advertising

Disney has embraced AI in its advertising strategy, particularly in optimizing ad placement and creative development. Rita Frow, Disney’s president of Global Advertising, highlights the use of AI in their advertising technology stack.

AI algorithms are utilized to optimize yield across their tech stack, from ad placement to creative management. This approach allows Disney to enhance targeting precision and optimize ad performance based on real-time data insights.

Disney uses AI in different processes to efficiently manage creative content. It helps video streaming platforms to streamline their workflows and maintain a high standard of streaming content offering. AI integration helps to boost Disney’s ad campaign efficiency to resonate with audience expectations.

Benefits of AI-Generated Creative Assets for B2B Marketers

Don’t you agree that AI has introduced a new era of efficiency in B2B ad campaigns?

Of course, it has more in creative asset development. There are lots of perks waiting on the way for B2B marketers who are using or thinking of using AI-generated creative assets to improve their ads approach and overall business outcomes.

Cost-Effectiveness and Improved ROI

Do you know that AI-powered tools optimize advertising spending?

It analyzes a great amount of information to identify the most effective targeting parameters and content variations. This targeted approach reduces wasted ad spend and improves ROI by ensuring that marketing budgets are allocated towards high-performing strategies.

McKinsey reports that companies using AI for marketing and sales initiatives see an average increase in leads and appointments of more than 50%, and cost reductions of 40-60% in areas such as call center operations and predictive maintenance.

Personalization at Scale

Offering improved and better experience is the backbone of a B2B advertising campaign. AI helps to analyze data and deliver personalized experiences to their segmented audience. It helps them to deliver tailored content that meets their audience’s behaviors.

Real-Time Optimization and Insights

Real-time insight and AI-driven analytics help marketers track customer behavior and their viewing habits. This in the end facilitates marketers to optimize their ad campaigns, driving more engagement and profit for brands.

PwC artificial intelligence study shows that organizations utilizing AI in marketing are 2.6 times more likely to surpass their revenue targets compared to those brands that don’t invest in this modern tech.

Source: PwC

Consistency in Brand Messaging

It becomes essential for brands to maintain brand messages across different channels as it helps to build trust. AI-generated creative asserts brand voice, regardless of the targeted segment or platform brands use.

Future Outlook

AI’s future in B2B marketing is undoubtedly optimistic. Modern technology including NLP, ML, and predictive analytics improves the complexity of AI-generated creative assets. Additionally, AI integration with other technologies like AR and VR opens unexplored possibilities for marketing approaches.

AI-generated creative assets represent a paradigm shift in B2B marketing, providing unparalleled efficiency and personalization capabilities. AI technologies implementation helps B2B marketers streamline their operations and engage their targeted segments, resulting in sustainable growth in today’s competitive market.

About the Author:

Dilshad Durani is a seasoned Digital Marketer and Content Creator currently contributing her expertise to the dynamic team at Alphanso Technology, a leading company specializing in Soundcloud clone and open-source event ticketing system development. Her insatiable curiosity fuels a relentless pursuit of knowledge, driving her to unravel the intricacies of changing trends, evolving marketing approaches, and ethical business practices.

Der Beitrag How AI-Generated Creative Assets For Ads Help B2B Marketers? erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: forbes.com – “Getting Value From Predictive AI: How To Find High-ROI Opportunities”

Have you heard this from an executive in a meeting recently? “We really need to start using AI.”

With new AI innovations revealed every day, the pressure is on for companies to simply “use AI” to keep up and be competitive.

However, implementing AI solutions like predictive analytics without an intentional strategy can be disastrous. Without a thoughtful, ROI-focused approach underlying your AI strategy, your organization will waste resources and watch initiatives wither.

To select the right first steps toward using AI, I suggest applying a framework that assesses both the feasibility and value of potential predictive AI use cases. This approach will help you focus squarely on the places where you can drive measurable value in your highest-priority strategic areas.

There are three critical areas you’ll need to address using this approach. Let’s dive in.

Your Top Strategic Priorities

One of the most crucial aspects to consider is your company’s top strategic priorities this quarter or next quarter. These are the areas where your decisions and actions can make a significant impact.

You may be aware of some operational inefficiencies that need to be addressed. Perhaps there are key KPIs that support your company-wide strategy and goals. You may be in the loop about strategic initiatives that could have a major impact on business outcomes.

All of these could represent rich opportunities for predictive AI. Gather information about each of them.

The opportunities may all sound intriguing, but there’s a deeper question to answer before proceeding: If you could accurately predict future outcomes using AI, would that capability realistically enable you to execute these priorities better?[…]

Read more: www.forbes.com

Der Beitrag Getting Value From Predictive AI: How To Find High-ROI Opportunities erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Dilshad Durani – “How AI Drives Streaming AD Technology?”

Understanding Contextual Advertising

Traditional advertising methods rely on demographic targeting, which aims to reach targeted audiences in segments. However, contextual advertising takes a completely different approach.

Contextual ads use the context of the consumed content. In simple words, ads are tailored to match viewers’ moods, preferences, and themes, providing a more personalized ad experience.

How AI Drives Streaming Ad Technology for Disney?

Walt Disney is harnessing AI to power ad tools that help brands tailor their commercials to fit viewers’ screens within a TV series and movie.

Disney’s innovative approach with “Magic Words” exemplifies how AI is powering contextual advertising in streaming services. By analyzing scenes across its vast library using AI and machine learning, Disney can identify the mood, content, and brands featured in each scene.

Brands can use metadata (descriptive tags) to identify specific moods and personalize messages to match their tone. A OTT streaming script similar to Netflix and Disney are investing in artificial intelligence to provide personalized and improved experiences to viewers.

Disney has invested in streaming ad technology as it moves away from cable TV and broadcast, along with viewers. Hulu’s and Disney’s streaming giants services ad revenue fell almost 3% in the first quarter of 2024.

According to the eMarketer report, Netflix outpaces Disney’s ad revenue by $1.03 billion versus Disney’s $911.9 million.

Source: (eMarketer)

Disney relies on streaming ad technology that powers the linear TV business and Hulu, which helps the company to increase its ad revenues.

Personalized Messaging

AI-driven streaming ad technology enables brands to move beyond demographic targeting and embrace personalized messaging.

By leveraging metadata and AI insights, advertisers can craft tailored messages that resonate with individual viewers based on their viewing preferences and emotional cues. This shift towards audience-centric advertising promises higher engagement and conversion rates.

Beta Testing and Industry Adoption

Disney’s partnership with leading advertising companies like Omnicom, Dentsu, and GroupM underscores the industry’s growing interest in AI-driven streaming ad technology. Beta testing initiatives aim to refine and optimize these new advertising tools, paving the way for widespread adoption across streaming platforms.

Rise of Ad-Supported Streaming Services

As consumers increasingly gravitate towards ad-supported streaming options, platforms like Disney+ and Hulu are seizing the opportunity to monetize through targeted advertising. AI-powered ad technology enables these platforms to deliver relevant ads seamlessly integrated into the viewing experience, balancing user satisfaction with revenue generation.

Future Prospects and Innovations

Looking ahead, AI-driven streaming ad technology holds immense potential for further innovation and refinement. Advancements in computer vision, natural language processing (NLP), and predictive analytics will enable even deeper insights into viewer behavior and preferences, fueling the next wave of personalized advertising solutions.

Conclusion

AI is driving a paradigm shift in streaming ad technology, ushering in an era of contextual advertising and personalized messaging. From Disney’s Magic Words to Netflix’s sophisticated recommendation algorithms, AI-powered solutions transform how brands connect with consumers in the digital age. As the streaming landscape continues to transform, we expect artificial intelligence to remain at the forefront of innovation, shaping the future of advertising in the streaming era.

About the Author:

Dilshad Durani is a seasoned Digital Marketer and Content Creator currently contributing her expertise to the dynamic team at Alphanso Technology, a leading company specializing in Eventbrite clone and event management system in PHP development. She is curious to learn new things and is passionate about helping people understand market trends, changing marketing approaches, business ethics, and more with her writing.

Der Beitrag How AI Drives Streaming AD Technology? erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: ibm.com – “Conversational AI Use Cases For Enterprises”

Beyond the simplistic chat bubble of conversational AI lies a complex blend of technologies, with natural language processing (NLP) taking center stage. NLP translates the user’s words into machine actions, enabling machines to understand and respond to customer inquiries accurately. This sophisticated foundation propels conversational AI from a futuristic concept to a practical solution.

Several natural language subprocesses within NLP work collaboratively to create conversational AI. For example, natural language understanding (NLU) focuses on comprehension, enabling systems to grasp the context, sentiment and intent behind user messages. Enterprises can use NLU to offer personalized experiences for their users at scale and meet customer needs without human intervention.

Natural language generation (NLG) complements this by enabling AI to generate human-like responses. NLG allows conversational AI chatbots to provide relevant, engaging and natural-sounding answers. The emergence of NLG has dramatically improved the quality of automated customer service tools, making interactions more pleasant for users, and reducing reliance on human agents for routine inquiries.

Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. ML algorithms understand language in the NLU subprocesses and generate human language within the NLG subprocesses. In addition, ML techniques power tasks like speech recognition, text classification, sentiment analysis and entity recognition. These are crucial for enabling conversational AI systems to understand user queries and intents, and to generate appropriate responses.

DL, a subset of ML, excels at understanding context and generating human-like responses. DL models can improve over time through further training and exposure to more data. When a user sends a message, the system uses NLP to parse and understand the input, often by using DL models to grasp the nuances and intent.

Predictive analytics integrates with NLP, ML and DL to enhance decision-making capabilities, extract insights, and use historical data to forecast future behavior, preferences and trends. ML and DL lie at the core of predictive analytics, enabling models to learn from data, identify patterns and make predictions about future events.

These technologies enable systems to interact, learn from interactions, adapt and become more efficient. Organizations across industries increasingly benefit from sophisticated automation that better handles complex queries and predicts user needs. In conversational AI, this translates to organizations’ ability to make data-driven decisions aligning with customer expectations and the state of the market.[…]

Read more: www.ibm.com

Der Beitrag Conversational AI Use Cases For Enterprises erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Zachary Amos – “Will AI Reduce or Deepen the Digital Divide?”

The aviation sector is increasingly relying on internet-connected devices and interconnected digital systems, increasing the risk of cyberattacks and data breaches. Needless to say, an urgent solution is essential, which could be artificial intelligence (AI). AI is fast, versatile and adaptive, so it seems like the ideal tool. Can it permanently improve aviation cybersecurity?

Cybersecurity Concerns in the Aviation Industry

A ransomware attack is one of the most common cyberattacks. Globally, the aviation sector experiences one at least once per week on average. Threat actors know airlines are desperate to keep planes in the air, so they’re easy target.

Data breaches are another huge cybersecurity concern in aviation. Hackers know commercial airlines store passenger data — names, addresses and birth dates — which can be very valuable on the dark web. They covertly infiltrate systems to steal and leak information.

An insider threat is a threat that comes from inside an organization. Unfortunately, it’s relatively common. In 2023, 30% of chief information security officers felt it was one of the biggest dangers. More often than not, an employee’s mistake causes a cybersecurity incident.

Why Do Cybercriminals Target Aviation?

Threat actors and cybercriminals will target any sector as long as they can steal valuable data and secure ransom payments. However, aviation is a prominent target for these reasons.

Military Aviation

While the Air Force is advanced enough to stop many cyberattacks relatively easily, it deals with a tremendous number of them. Foreign countries, terror groups and digital attackers know the sensitive, top-secret data in military systems would be incredibly valuable on the dark web.

Commercial Aviation

Threat actors frequently concentrate their efforts on civil aviation. In 2020, 61% of aviation-related cyberattacks targeted commercial airlines. They’re valuable targets since they store a massive amount of personally identifiable data and get government funding.

Cyberattacks can force airlines to ground their planes until they resolve the situation, causing billions of dollars in lost revenue and refunds. Compliance-related fees and reputation damage can also have a massive financial impact on them.

Artificial Intelligence Alleviates Cybersecurity Concerns

AI can alleviate aviation’s main cybersecurity concerns and adapt to emerging threats.

• Updates and Patches

If software doesn’t receive updates, new vulnerabilities appear — and hackers exploit them. In response, aviation engineers use generative AI to expedite code base testing to guarantee patch installation, securing critical systems against cyberattacks faster.

• Incident Response

While cybersecurity professionals don’t work around the clock, cybercriminals do. Fortunately, AI can automatically respond to cybersecurity incidents during off-hours. It can either send critical alerts to prompt manual intervention or initiate a predetermined reaction.

• Autonomous Adaptation

Machine learning models can autonomously adapt as they receive new information. In other words, they don’t need any manual intervention to learn. They’ll get more accurate over time instead of becoming outdated like most other hardware. As a result, they know how to react to unique cybersecurity threats.

• Threat Detection

AI can learn from past cybersecurity incidents to understand what suspicious activity and anomalies look like. It enables predictive analytics, which essentially allows airlines to predict when and how cyberattacks will occur.

• Automatic Operation

Algorithms operate automatically, enabling them to work incredibly fast. According to Matthew Strohmeyer — an Air Force colonel — one of the military’s machine learning models can complete a task in 10 minutes that would take humans days. AI can work around the clock without manual intervention to detect, categorize and respond to threats.

Artificial Intelligence Strengthens Cybersecurity Efforts

Algorithms check all the boxes regarding cybersecurity. They’re fast, automatic, adaptable and affordable. Even commercial airlines — which have notoriously thin operating margins — can afford to leverage AI because it doesn’t need much power to run constantly.

As cyberattacks continue increasing in frequency, AI will likely become the go-to solution for aviation cybersecurity. It can protect against the most prominent concerns — ransomware, insider threats and data breaches — by minimizing human error, identifying threats faster than humans and securing systems against threat actors.

About the Author:

Zachary Amos is the Features Editor at ReHack, where he writes about artificial intelligence, cybersecurity and other technology-related topics.

Der Beitrag How Artificial Intelligence Improves Aviation Cybersecurity erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Anna Smith – “How To Get Involved With Artificial Intelligence As A Business, Investor, Or Creator”

Artificial Intelligence (AI) is revolutionizing industries worldwide, offering unparalleled opportunities for businesses, investors, and creators. Whether aiming to leverage AI technologies for business growth, invest in AI-driven ventures, or actively participate in developing AI solutions, there are various paths to take to get involved with artificial intelligence.

Getting Involved as a Business:

Identifying business needs involves a strategic assessment of how AI can specifically augment operational efficiency, elevate customer interactions, and foster innovation within your organization. This entails a systematic evaluation and automation across various
facets of your business to pinpoint potential areas for AI integration. By reviewing the processes, customers, and current innovation pipelines, will help to identify opportunities where AI
applications can optimize workflows, personalize customer experiences, and catalyze inventive solutions. As an entrepreneur, CEO, or C level executive a proactive approach ensures that AI is
not just a technological addition but a part of the businesses aligning with the core objectives and growth trajectories of your business. Integrating AI will help achieve business goals faster,
streamline business processes, and aid in system’s efficiency.

To develop a clear strategy for AI integration, automation, learn about artificial intelligence. There are some resources listed within this article. Learn about AI and how it functions. By learning about AI, I could potentially spot how AI could benefit your business. If I could become an AI expert it would increase value to society. Collaborate with AI experts or hire a team to implement AI solutions tailored to your business requirements. Crafting an AI integration and automation strategy requires a methodical approach focused on aligning AI solutions with your business objectives. Engaging with AI specialists, or assembling an in-house team proficient in AI implementation will also increase business value. Collaborating with experts facilitates the development of AI solutions tailored to your specific business needs. To ensure that the integration aligns seamlessly with your operational framework is crucial to know about AI. A clear strategy encompasses assessing technical capabilities, resource allocation, risk mitigation, and scalability, thereby ensuring a cohesive and effective assimilation of AI technologies into your business infrastructure.

Investing in learning, talent, and Infrastructure will facilitate smoother transitions and quicker adoption. Recruit AI specialists, data scientists, and engineers. Invest in robust computing infrastructure and data collection/storage capabilities necessary for AI development.Investing in talent and infrastructure for AI involves strategic recruitment of specialized professionals like AI specialists, data scientists, and engineers who possess the expertise to navigate AI development. A proficient workforce capable of conceptualizing, implementing, and optimizing AI solutions will aid in integration . Simultaneously, allocating resources towards robust computing infrastructure and advanced data collection/storage systems is pivotal. Machine learning lays the foundation for effective AI development by providing the computational power and data handling capabilities necessary for processing, analyzing, and deriving insights from vast datasets. Thereby fostering innovation and maximizing the potential of AI technologies within your organization.

Implement AI-driven tools and platforms such as predictive analytics, natural language processing, or machine learning algorithms to streamline operations and gain a competitive edge.Embracing AI solutions involves the proactive adoption of AI-driven tools and platforms, leveraging technologies like predictive analytics, natural language processing (NLP), and machine learning algorithms. By integrating these tools into your operational framework, you can streamline processes, optimize decision-making, and gain a competitive advantage. Predictive analytics aids in forecasting trends and patterns, empowering proactive strategies. NLP facilitates understanding and processing human language, enhancing communication and interaction with customers or internal data. Meanwhile, machine learning algorithms automate tasks, learn from data, and make informed decisions, boosting efficiency and innovation. Embracing these AI-driven solutions empowers businesses to leverage cutting-edge technologies for enhanced productivity, better decision-making, and staying ahead in the competitive landscape. Stay updated on AI trends and advancements. Encourage a culture of learning and experimentation within the organization to adapt to evolving AI technologies.

Getting Involved as an Investor:

This is not financial advice but a strategy to consider. Educate Yourself. Understand the landscape of AI technologies, market trends, and potential investment opportunities. Attend conferences, join forums, and read industry reports to stay informed.

Diversify Portfolio and consider investing in AI-focused startups, AI-based software companies, or venture capital firms specializing in AI technologies to diversify your investment portfolio. Research and Due Diligence. Conduct thorough research and due diligence before investing.

Evaluate the technology, leadership, market potential, and scalability of AI ventures to make informed investment decisions. Network and Collaborate. Build connections within the AI community. Engage with AI entrepreneurs, researchers, and industry experts to gain insights and identify promising investment prospects.

Long-Term Vision. Understand that AI is a rapidly evolving field. Invest with a long-term vision, acknowledging the potential for significant returns over time as AI technologies mature. I am a Venture Partner to 3x Capital, we are seeking investors for the fund, angel investing into Web 3, blockchain, Artificial Intelligence.

Getting Involved as a Creator:

Educational Foundation.Acquire knowledge in AI-related fields such as machine learning, data science, and programming languages like Python. Enroll in online courses, workshops, or pursue relevant degrees.

Hands-On Experience:Engage in practical projects or join AI-related hackathons to apply theoretical knowledge. Build AI models, work on datasets, and develop prototypes to enhance skills and gain experience. Contribute to open-source AI projects to collaborate with the AI community, showcase skills, and learn from others.

Join AI Communities:Participate in AI forums, meetups, and online communities. Network with like-minded individuals, share insights, and stay updated with the latest advancements in AI. Create Innovative Solutions: Identify problems that AI can solve and work on developing innovative AI-driven solutions.

Leverage creativity to address real-world challenges through AI technologies. There are resources to create content for your brand, business, and some of the generative AI could be inspired by creators. AI opens up a world of possibilities with endless opportunities to create!

Here are some AI Recommendations:

AI Communities:

GitHub: GitHub hosts various AI-related repositories, forums, and communities where developers collaborate on open-source AI projects, share code, and discuss AI-related topics.

AI Meetup Groups: Various local and global AI meetup groups, often organized through platforms like Meetup.com, bring together AI professionals, researchers, and enthusiasts for networking, knowledge sharing, and discussions on AI trends and advancements.

AI Associations and Organizations: Entities like the Association for the Advancement of Artificial Intelligence (AAAI), IEEE Computational Intelligence Society, and the AI Ethics Lab focus on AI research, ethics, and the societal implications of AI.

AI Tools for Investors:

AlphaSense: AI-powered search engine for financial analysis, enabling investors to access and analyze relevant information from transcripts, filings, and research.

Kavout: AI-driven investment platform utilizing machine learning
for stock analysis and forecasting.

Sentieo: Integrates AI for financial research, data analysis, and investment idea generation.

Wealthfront and Betterment: Robo-advisors using AI algorithms to manage portfolios and optimize investments based on risk tolerance and financial goals.

AI tools for content creators:

OpenAI’s GPT-3: Offers powerful language generation capabilities for content creation, article writing, and idea generation.

Canva: Utilizes AI for design suggestions, automated design resizing, and background removal.

Midjourney: Turn words into stunning AI-generated Art & Drawings instantly. Unlock your creativity. Discover The Magic Of AI-Generated Art.

AI Education:

DeepLearning AI:
Practical Deep Learning

Conclusion

Getting involved in Artificial Intelligence as a business, investor, or creator involves continuous learning, strategic decision-making, networking, imagination, entrepreneurship, and a passion for innovation. By embracing AI’s potential, individuals and entities can make significant contributions while reaping the benefits of this transformative technology in today’s dynamic landscape.

About the Author:

I’m Anna Smith, Vice President. I work in private equity, utilizing an artificial intelligence platform to buy businesses with EBITDA of up to $100M and partner to a hedge fund. I am a venture partner at 3x Capital, Scout to Expert Dojo, Deals Partner at Ganas VC. I’d love to connect with you.

Der Beitrag How To Get Involved With Artificial Intelligence As A Business, Investor Or Creator erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.