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Artificial intelligence (AI) has become increasingly popular in various aspects of business. According to a survey by Forbes Advisor, the top applications of AI in business include customer service, cybersecurity and fraud management, customer relationship management, digital personal assistants, inventory management, and content production. Additionally, AI is used for product recommendations, accounting, supply chain operations, recruitment and talent sourcing, and audience segmentation.

One variant of AI, generative AI, has gained significant attention and is predicted to reshape the competitive landscape in various industries. CEOs recognize the competitive advantage that organizations with advanced generative AI have. However, the adoption of AI is no longer limited to big businesses, as more accessible and dedicated solutions are developed to enhance different aspects of business.

Successfully harnessing the potential of AI requires a methodical approach. Its not enough to simply download a version of generative AI; organizations need to properly integrate AI models into operational systems. Selecting, governing, analyzing, and applying data across the enterprise are crucial for the effective use of AI. Companies should analyze their processes, customer interactions, and data assets to identify areas where AI can make the most impact.

There may be skepticism about the impact of AI, particularly in the finance function. Some concerns include unclear business objectives, poor data quality, lack of talent, and a lack of collaboration between teams. To introduce AI into the workflow effectively, organizations should assess their AI readiness, visualize specific use cases and desired outcomes, and address barriers to adoption.

When applying AI, companies should outline clear goals and prioritize experimental rollouts. Additionally, a comprehensive proof of technology is essential to determine compatibility and integration with existing environments. To build trust in AI, organizations should infuse AI into their culture and incentivize teams to create business value with AI. Data security and compliance should also be considered when deploying AI tools.

Scaling AI across the organization can be challenging, as observed in the experience with robotic process automation (RPA). Many companies struggle to deploy responsible AI algorithms and models at scale. While AI has reached its inflection point, only a small percentage of organizations have successfully scaled their AI initiatives across the enterprise.

Overall, the popular applications of AI in business are diverse, and organizations need to approach AI adoption strategically to fully leverage its potential.

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Most Popular Applications of Artificial Intelligence (AI) in Business - Fagen wasanni

As investors rush to find the next big AI deal, they should pay more attention to the potential AI has to reinvent venture capital.

While knowledge workers from lawyers to marketers are wondering how safe their jobs are, there is every chance that AI will disrupt the inner workings of the VC world itself. Most obviously, it can accelerate the speed at which investors find and make deals.

But the real power and potential of using tech in venture could be in using it to find hidden gems. Companies that might otherwise go under the radar and be overlooked by investors, who repeatedly source deals from the same networks.

AI can galvanise venture because it expands investors reach - exponentially.In the past 20 years, 514 unicorn companies have come from 65 cities across the continent according to Dealroom, but a small investment team cant hope to physically reach the 100+ places in Europe where exciting companies are emerging every day.

Right now, more than 50% of VC investment goes to just three countries (France, Germany and UK), according to Dealroom. Clearly, there is so much more to talent and potential to be discovered across Europe with a long tail of cities with strong tech talent, developing ideas in relative isolation. Using AI, weve unearthed startups of interest in Bratislava, Andorra and even in Bod in the Arctic Circle. Its leading us down less travelled roads and that has to be an advantage.

The second way that AI will change venture is because it can be used to eliminate bias. Investors naturally focus on the larger European tech hubs, finding founders similar to those they have worked with before - from the same schools and feeder companies. The end result is that the same types of companies and founders continue to get funding.

Extraordinary founders can come from anywhere. Using AI to find targets, we can look beyond easy and familiar founder metrics, instead of focusing on other signals that correlate with growth and potential. One example is bootstrapped companies. Too often, VCs treat these companies as unambitious but companies that choose to grow from revenue have remarkable qualities. This led us to Checkout.com - now one of Europes largest fintechs. As investors focus more on profitability than growth, being alert to these signals will become only more important.

Investors who are prepared to go to less obvious places can convince founders that they will go above and beyond - even when those companies arent actively looking for investment. Road trips for a cup of coffee in St Gallen, Switzerland, or a three-hour drive to Andorra, have led us to deals that were on no ones radar.

The third way that VC will change, is that AI can make up for investors human failings. One mistake that most investors have made is passing on a company at seed and then missing the big opportunity to invest at Series A. But with help from an algorithm, investors can keep an eye on how a company is developing. Things that businesses do every day - make a senior hire or redesign their website - can flag a shift in growth or potential that is worth looking into. These always-on alerts provide capacity and powers that humans alone cant.

Like most knowledge industries, data can only take you so far. Relationship building will remain fundamental to venture and there will always be a role for it. Networking is its own form of intelligence, uncovering things you will never find in the data.

Some will argue that using AI in venture will only lead to more cookie-cutter companies being created. But intentional and careful use of AI can have the opposite effect, creating the potential for a system where untypical and unusual founders can be uncovered.

In this way, we can use technology and software to build a more equal tech ecosystem, with all the benefits to economies and even overlooked communities that will bring. Uncovering a different sort of founder, with a different mission, is vital.

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The real power of applying Artificial Intelligence to VC? Uncovering ... - Tech.eu

Artificial intelligence (AI) has been making notable strides in various fields, but its struggles with accuracy are well-documented. The technology has produced falsehoods and fabrications, ranging from fake legal decisions to pseudoscientific papers and even sham historical images. While these inaccuracies are often minimal and easily disproven, there are instances where AI creates and spreads fiction about specific individuals, threatening their reputations with limited options for protection or recourse.

One example is Marietje Schaake, a Dutch politician and international policy director at Stanford University. When a colleague used BlenderBot 3, a conversational AI developed by Meta, to ask who a terrorist is, the AI incorrectly responded by identifying Schaake as a terrorist. Schaake, who has never engaged in any illegal or violent activities, expressed concerns about how others with less agency to prove their identities could be negatively affected by such false information.

Similarly, OpenAIs ChatGPT chatbot linked a legal scholar to a non-existent sexual harassment claim, leading to reputational damage. High school students in New York created a deepfake video of a local principal, raising concerns about AIs potential to spread false information about individuals sexual orientation or job candidacy.

While some adjustments have been made to improve AI accuracy, the problems persist. Meta, for instance, later acknowledged that BlenderBot had combined unrelated information to incorrectly classify Schaake as a terrorist and closed the project in June.

Legal precedent surrounding AI is limited, but individuals are starting to take legal action against AI companies. In one case, an aerospace professor filed a defamation lawsuit against Microsoft, as the companys Bing chatbot wrongly conflated his biography with that of a convicted terrorist. OpenAI also faced a libel lawsuit from a radio host in Georgia due to false accusations made by ChatGPT.

The inaccuracies in AI arise partly due to a lack of information available online and the technologys reliance on statistical pattern prediction. Consequently, chatbots may generate false biographical details or mash up identities, a phenomenon referred to as Frankenpeople by some researchers.

To mitigate accidental inaccuracies, Microsoft and OpenAI employ content filtering, abuse detection, and other tools. These companies also encourage users to provide feedback and not rely solely on AI-generated content. They aim to enhance AIs fact-checking capabilities and develop mechanisms for recognizing and correcting inaccurate responses.

Furthermore, Meta has released its LLaMA 2 AI technology for community feedback and vulnerability identification, emphasizing ongoing efforts to enhance safety and accuracy.

However, AI also has the potential for intentional abuse. Cloned audio, for example, has become a prevalent issue, prompting government warnings against AI-generated voice scams.

As AI continues to evolve, it is crucial to address its limitations and potential harm. Stricter regulations and safeguards are necessary to prevent the spread of false information and protect individuals from reputational damage.

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Artificial Intelligence's Struggles with Accuracy and the Potential ... - Fagen wasanni

In todays fast-paced world, the ability to create effective presentations is an invaluable skill that often requires extensive time and effort. However, Gamma AI, a free AI-driven presentation tool, has emerged to drastically cut down the time spent on presentation design without compromising quality.

Gamma AI offers a free platform that harnesses the power of artificial intelligence to design presentations. Users can input text, ideas, or general topics, and Gamma takes care of the rest, transforming those inputs into engaging slides complete with text, images, and graphics.

The key feature that sets Gamma apart from conventional software is its design flexibility. With Gammas collapsible and interactive cards, traditional slide limitations are a thing of the past. These cards allow presenters to avoid cramming vast amounts of data onto a single slide, providing a more organized and visually appealing presentation.

One of the standout features of Gamma AI is its support for a wide range of media elements. Presenters can incorporate GIFs, YouTube clips, and even TikTok videos into their slides, creating multi-faceted and dynamic presentations that captivate their audience.

Gamma also recognizes the importance of structure and organization in impactful presentations. The tool provides toggles and footnotes, enabling logical flow of information and simplifying navigation for viewers.

Collaboration is another advantage of Gamma AI. The real-time collaborative feature allows multiple users to comment and edit presentations, fostering teamwork and enhancing the content creation process. Additionally, Gamma provides insights into viewer engagement, helping presenters understand which slides resonate most with their audience.

While there are some limitations to consider, such as potential formatting discrepancies when exporting Gamma presentations to platforms like PowerPoint or Google Slides due to Gammas unique card system, the benefits of the tool are evident.

Crafting a presentation with Gamma AI is a straightforward process. Users begin by inputting prompts, which can be tailored for more accurate results. After establishing an outline, users can select a theme from a variety of options or have Gamma randomly choose one based on the content. Detailed editing allows users to refine their presentation further by changing card templates or adding new layouts.

What sets Gamma apart is its enhanced AI editing capabilities. Users can request AI assistance in refining content, such as rephrasing a card or adding specific images.

Once the slides are ready, presenting is seamless. Gamma presentations can be displayed full screen or within a tab and easily shared via a link, ensuring accessibility for audiences anywhere.

Gamma AI exemplifies the transformative power of technology in the realm of presentations. By streamlining the creation process and offering features that cater to the needs of presenters, Gamma paves the way for more innovative solutions in content creation. As AI continues to evolve, tools like Gamma will play a crucial role in effectively conveying information and engaging audiences.

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Gamma AI: Revolutionizing Presentation Creation with Artificial ... - Fagen wasanni