Maga Techs

Category: AI

  • What is AI investment trust? Explaining the benefits, how to choose, and future trends!

    What is AI investment trust? Explaining the benefits, how to choose, and future trends!

    Did you know that AI, which has been applied in various fields in recent years, is also being used in investment trusts?

     

     

    With investment trusts, investment targets are determined by experts, so you may be concerned about costs and reliability.

    Therefore, by using AI, it analyzes all kinds of information and provides asset management advice, making it possible to use it with confidence.

     

     

    What is AI investment trust?

    An investment trust is a product that combines money collected from investors into one large fund and is invested and managed in stocks, bonds, etc. by asset management experts.

    With investment trusts, experts decide where to invest based on the investor’s investment amount, and the investment results are distributed.

    Therefore, there is no need for investors to decide where to invest.

    When AI is utilized in the aforementioned investment trusts, AI will take the place of investment experts.

    By using AI-powered investment trusts, you can manage your assets while referring to advice from AI.

    Advantages of using AI investment trust

    There are two advantages of AI investment trusts:

    1. Able to make calm decisions
    2. no cost

    I will explain each point.

    Able to make calm decisions

    The first advantage of AI investment trusts is that they allow you to make calm decisions.

    Emotional trading can be avoided as AI provides operational advice.

    Using machine learning and deep learning , you can quickly analyze huge amounts of data and make predictions.

    AI advice reflects large-scale and detailed analysis results that were impossible with traditional human analysis.

    Therefore, you can notice things that humans might miss, so you can invest in investment trusts with confidence.

    no cost

    The second advantage of AI investment trusts is that there are no costs.

    Generally, investment trusts incur fees such as purchase fees and audit fees.

    However, some AI investment trust services charge only management fees.

    Depending on the product and investment method you choose, there are no labor costs, and costs can be kept lower than investment trusts.

    How to choose an investment destination

    Now let’s talk about general investment.

    There are two ways to choose a recommended investment destination:

    1. Focus on familiar companies
    2. Focus on the company’s track record and business content

    Focus on familiar companies

    First, let’s take a look at the companies whose products and services we often use.

    Then, think about what you like about the company and what it will look like in the future, and decide whether to invest in it.

    Focus on the company’s track record and business content

    Next, let’s examine the company’s underlying business content and performance.

    After researching them, it is important to know what their strengths are and what their future business plans and direction will be.

    In the process of researching a company’s performance, it’s a good idea to check items such as trends in sales and profits, and the company’s position in the industry.

    Examples of investment trusts using AI

    As an example of an investment trust that utilizes AI, we would like to introduce “Deep AI”, which is set up and operated by Asset Management One.

    In addition, based on the model analysis results, a portfolio is constructed by combining text analysis such as news flow and fundamental analysis of individual companies at the discretion of the fund manager.

    The future of AI investment trusts

    Currently, many AI investment trusts and related products are on sale in United State.

    However, it is said that the penetration rate of AI investment trusts is low in United State compared to other countries.

    This is related to the doubts about reliability due to the fact that AI is not perfect, which was mentioned in the section on the disadvantages of AI investment trusts.

    From now on, AI will be used in a variety of fields, and it is expected that trust in AI will increase.

    Investment trusts that collect AI companies

    Apart from AI investment trusts, there are other general investment trusts that only deal with AI companies.

    Why not consider an investment trust that specializes in AI companies that have gained momentum in recent years?

    The following are two investment trusts that collect recommended AI companies.

    1. Global AI Fund
    2. Nomura Global AI Related Stock Fund A Course

    Global AI Fund

    First, we will introduce the “Global AI Fund” set up and managed by Sumitomo Mitsui DS Asset Management.

    As of November 2021, it is an AI investment trust that has grown four times in five years and has high expectations.

    A distinctive feature is that the group of companies related to AI ( artificial intelligence ) is not limited to the technology sector (information technology and communication services).

    Therefore, as AI permeates industries, we are expanding our investment scope.

    We are also taking flexible measures as industries such as travel, dining out, and entertainment, which have been suspended due to the coronavirus pandemic, are moving toward resumption.

    Nomura Global AI Related Stock Fund A Course

    Next, we will introduce the “Nomura Global AI-related Stock Fund A Course” set up and managed by Nomura Asset Management.

    The main investment target here is AI ( artificial intelligence ) technology-related stocks from around the world, including emerging countries.

    When selecting stocks, we focus on research results in advanced AI technology from a global perspective.

    Stocks are selected with a focus on profit growth, with a focus on stocks in AI-related fields that are expected to become more attractive as investments as AI technology becomes more practical.

    In addition, as a general rule, we aim to reduce exchange rate fluctuation risk through currency hedging (including alternative hedges using currencies of developed countries, etc.).

    summary

    In this article, we introduced investment trusts that utilize AI and general investment trusts that collect only AI companies.

    Similarly, AI continues to gain momentum in other industries as well.

    It is predicted that AI will be used for even more things in the future.

     

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  • 4 Ways your startup can take advantage of AI today !

    4 Ways your startup can take advantage of AI today !

    To find out if your startup needs AI today, start by prioritizing your business problem. Let’s frame the best approach to solving these challenges and evaluate how technology can help. In most cases, you will be able to work effectively with basic analytics, statistics, or simple machine learning.

    In some situations AI horsepower is needed. In such scenarios, additional intelligence and automation will transform your startup. This article is for such cases.\

    When people feel the need for AI, the next question they often ask is, “Do we really need a big budget to use AI?” The answer to this question is no. It doesn’t take months of hard work, elite data scientists, or a big budget to make your business AI-driven.

    Here are four ways your startup or small business can start using AI today. These suggestions are laid out in order from easiest to hardest, so start at the top and see which option best fits your needs.

    (*Translation Note 1) Mr. Kesari, the author of this article , published an article titled ” When should we not invest in AI? ” on January 9, 2021 in the business media “Entrepreneur” Asia Pacific Edition .
    Here are the five situations in which AI should not be invested, according to Mr.5 Situations Where You Shouldn’t Invest in AI

    1. When a problem can be solved in a simpler way: If a problem can be solved without AI, then the cheaper and simpler solution should be implemented.
    2. When you don’t have enough training data: Introducing AI is useless if you don’t have enough data to train it.
    3. When the effect of introducing AI has not been proven: It is dangerous to introduce AI to problems in domains where the effect of introducing AI has not been sufficiently proven.
    4. When the cost outweighs the benefits: Even if AI is introduced, maintenance costs are required. If this cost outweighs the benefits, AI should not be introduced.
    5. When Humans Should Get Involved: Even if AI’s ability to solve problems surpasses humans’, there are situations where humans should get involved. For example, even if AI outperforms human doctors in diagnosing cancer, it should be a human being who announces cancer.

    1. Enabling AI features for tools you already use

    AI is all around us. Your smartphone probably has at least a dozen AI-powered apps. This technology is empowering us to take better photos with our cameras, organize our photos, and curate our social feeds.

    Most enterprise tools are adding AI-powered features to their products. Microsoft has built some AI features into Excel. If you insert data from screenshots or take advantage of the insights suggested by Excel’s Ideas panel, you’re using AI. 

    Salesforce has integrated Einstein , the company’s AI engine, as an intelligent assistant across its popular CRM (customer relationship management) platforms. Some companies will bundle AI capabilities into their core products, while others will need to upgrade.

    Ask your vendor if the software you buy has AI capabilities. Existing toolsets may already be AI-driven, or could be adapted with a simple upgrade.

    Five popular tools in this option: MS Office , Google for Business , Dropbox , Github , Mixmax

    How to insert data in Excel is explained in this help page.

    2. Buy off-the-shelf AI-powered SaaS tools

    Today, SaaS (Software as a Service) tools are plentiful and available for a reasonable monthly fee. Want to polish your marketing copy? Grammarly ‘s handy copy-editing feature covers the good stuff. Want to transcribe testimonial videos or do professional-grade media editing? Descript ‘s AI features make it easy.

    If you have an unmet business need, look for functional SaaS tools with intelligent features. Most SaaS tools come with built-in integrations that make it easy to integrate into your existing IT ecosystem. Even if it doesn’t fit perfectly, what matters is whether it solves most of the problems. If so, you can avoid investing in expensive enterprise licenses for similar AI capabilities.

    Evaluate the available tools against your key requirements. Check for matching coverage and ease of integration. If the results of your investigation exceed the acceptable range, let’s hurry to postpone the hiring.

    Five popular tools in this option: Zoho Zia , Trello , Grammarly , Descript , WaveApps

    3. Incorporation of ready-made AI models into tools

    If you can’t find a tool with built-in intelligence, the next best thing is to search the cloud for AI models that you can connect to the tool. For example, if you’re trying to find manufacturing defects in a product, AI can be used to automate visual inspection. Amazon Lookout for Vision is a cloud-based machine learning (ML) service that plugs directly into your workflow.

    Unlike the previous step, this step requires DevOps capabilities (including software development and IT operations). It also doesn’t require data scientists, but the team will need programming expertise to link software applications to online AI models. Also pay attention to the subscription cost, which is determined by usage.

    To consider this option, identify an online ML platform that has pre-built AI models to solve your domain problem. The space has seen promising startups such as Clarifai, Dialogflow, and SightHound, as well as big players such as Microsoft, Google, and Amazon.

    Five popular tools in this option: ML on AWS , Azure ML , Google Cloud ML , Clarifai , Sighthound

    4. Retraining publicly available AI models

    Once you’ve exhausted the options above, hire a data scientist to train an AI model in-house. Save effort by reusing publicly available AI algorithms and easily curated datasets instead of starting from scratch. These resources can be applied to solve your problem.

    For example, let’s say your startup needs to understand customer satisfaction by analyzing text feedback from customer surveys. For that, we need algorithms with natural language processing (NLP) capabilities. Instead of painstakingly training new AI models , teams can build AI models based on models that have won public competitions such as Kaggle , DrivenData and AICrowd .

    The best things on the Internet are often free, but it takes time to find them. Look for open repositories like HuggingFace that publish models with pre-trained weights, or communities that publish ML models like PapersWithCode . Many of these sites share rich and curated data that can accelerate the process of model building. As a team, evaluate the effort required to adapt the published models (to the problem you want to solve) and determine the cost of maintaining them as a product.

     

    Being AI-driven is a journey, not a destination

    In this article, we’ve seen four ways to get started with AI and get the most out of your resources. While starting an AI journey is often straightforward, it requires ongoing attention and investment to deliver consistent business value.

    Enterprises will need to train users, restructure organizational workflows, and manage the cultural shifts associated with AI adoption. It’s also important to regularly review the total cost of ownership (TCO) of your AI investment. A valid option today may be expensive a year from now.

    For example, a subscription to an AI-powered SaaS tool (shown as the second option) might be a good fit for a small team serving an early customer base. As teams grow and usage increases, subscription costs can become prohibitive. At such a stage, you may find it more economical to hire a small team of data scientists and retrain publicly available AI models (option 4).

    We have summarized what options are available to streamline decision-making regarding AI introduction.

     

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  • 4 Ways your startup can take advantage of AI today !

    4 Ways your startup can take advantage of AI today !

    To find out if your startup needs AI today, start by prioritizing your business problem. Let’s frame the best approach to solving these challenges and evaluate how technology can help. In most cases, you will be able to work effectively with basic analytics, statistics, or simple machine learning.

    In some situations AI horsepower is needed. In such scenarios, additional intelligence and automation will transform your startup. This article is for such cases.\

    When people feel the need for AI, the next question they often ask is, “Do we really need a big budget to use AI?” The answer to this question is no. It doesn’t take months of hard work, elite data scientists, or a big budget to make your business AI-driven.

    Here are four ways your startup or small business can start using AI today. These suggestions are laid out in order from easiest to hardest, so start at the top and see which option best fits your needs.

    (*Translation Note 1) Mr. Kesari, the author of this article , published an article titled ” When should we not invest in AI? ” on January 9, 2021 in the business media “Entrepreneur” Asia Pacific Edition .
    Here are the five situations in which AI should not be invested, according to Mr.5 Situations Where You Shouldn’t Invest in AI

    1. When a problem can be solved in a simpler way: If a problem can be solved without AI, then the cheaper and simpler solution should be implemented.
    2. When you don’t have enough training data: Introducing AI is useless if you don’t have enough data to train it.
    3. When the effect of introducing AI has not been proven: It is dangerous to introduce AI to problems in domains where the effect of introducing AI has not been sufficiently proven.
    4. When the cost outweighs the benefits: Even if AI is introduced, maintenance costs are required. If this cost outweighs the benefits, AI should not be introduced.
    5. When Humans Should Get Involved: Even if AI’s ability to solve problems surpasses humans’, there are situations where humans should get involved. For example, even if AI outperforms human doctors in diagnosing cancer, it should be a human being who announces cancer.

    1. Enabling AI features for tools you already use

    AI is all around us. Your smartphone probably has at least a dozen AI-powered apps. This technology is empowering us to take better photos with our cameras, organize our photos, and curate our social feeds.

    Most enterprise tools are adding AI-powered features to their products. Microsoft has built some AI features into Excel. If you insert data from screenshots or take advantage of the insights suggested by Excel’s Ideas panel, you’re using AI. 

    Salesforce has integrated Einstein , the company’s AI engine, as an intelligent assistant across its popular CRM (customer relationship management) platforms. Some companies will bundle AI capabilities into their core products, while others will need to upgrade.

    Ask your vendor if the software you buy has AI capabilities. Existing toolsets may already be AI-driven, or could be adapted with a simple upgrade.

    Five popular tools in this option: MS Office , Google for Business , Dropbox , Github , Mixmax

    How to insert data in Excel is explained in this help page.

    2. Buy off-the-shelf AI-powered SaaS tools

    Today, SaaS (Software as a Service) tools are plentiful and available for a reasonable monthly fee. Want to polish your marketing copy? Grammarly ‘s handy copy-editing feature covers the good stuff. Want to transcribe testimonial videos or do professional-grade media editing? Descript ‘s AI features make it easy.

    If you have an unmet business need, look for functional SaaS tools with intelligent features. Most SaaS tools come with built-in integrations that make it easy to integrate into your existing IT ecosystem. Even if it doesn’t fit perfectly, what matters is whether it solves most of the problems. If so, you can avoid investing in expensive enterprise licenses for similar AI capabilities.

    Evaluate the available tools against your key requirements. Check for matching coverage and ease of integration. If the results of your investigation exceed the acceptable range, let’s hurry to postpone the hiring.

    Five popular tools in this option: Zoho Zia , Trello , Grammarly , Descript , WaveApps

    3. Incorporation of ready-made AI models into tools

    If you can’t find a tool with built-in intelligence, the next best thing is to search the cloud for AI models that you can connect to the tool. For example, if you’re trying to find manufacturing defects in a product, AI can be used to automate visual inspection. Amazon Lookout for Vision is a cloud-based machine learning (ML) service that plugs directly into your workflow.

    Unlike the previous step, this step requires DevOps capabilities (including software development and IT operations). It also doesn’t require data scientists, but the team will need programming expertise to link software applications to online AI models. Also pay attention to the subscription cost, which is determined by usage.

    To consider this option, identify an online ML platform that has pre-built AI models to solve your domain problem. The space has seen promising startups such as Clarifai, Dialogflow, and SightHound, as well as big players such as Microsoft, Google, and Amazon.

    Five popular tools in this option: ML on AWS , Azure ML , Google Cloud ML , Clarifai , Sighthound

    4. Retraining publicly available AI models

    Once you’ve exhausted the options above, hire a data scientist to train an AI model in-house. Save effort by reusing publicly available AI algorithms and easily curated datasets instead of starting from scratch. These resources can be applied to solve your problem.

    For example, let’s say your startup needs to understand customer satisfaction by analyzing text feedback from customer surveys. For that, we need algorithms with natural language processing (NLP) capabilities. Instead of painstakingly training new AI models , teams can build AI models based on models that have won public competitions such as Kaggle , DrivenData and AICrowd .

    The best things on the Internet are often free, but it takes time to find them. Look for open repositories like HuggingFace that publish models with pre-trained weights, or communities that publish ML models like PapersWithCode . Many of these sites share rich and curated data that can accelerate the process of model building. As a team, evaluate the effort required to adapt the published models (to the problem you want to solve) and determine the cost of maintaining them as a product.

     

    Being AI-driven is a journey, not a destination

    In this article, we’ve seen four ways to get started with AI and get the most out of your resources. While starting an AI journey is often straightforward, it requires ongoing attention and investment to deliver consistent business value.

    Enterprises will need to train users, restructure organizational workflows, and manage the cultural shifts associated with AI adoption. It’s also important to regularly review the total cost of ownership (TCO) of your AI investment. A valid option today may be expensive a year from now.

    For example, a subscription to an AI-powered SaaS tool (shown as the second option) might be a good fit for a small team serving an early customer base. As teams grow and usage increases, subscription costs can become prohibitive. At such a stage, you may find it more economical to hire a small team of data scientists and retrain publicly available AI models (option 4).

    We have summarized what options are available to streamline decision-making regarding AI introduction.

     

    Follow us on Facebook for updates and exclusive content! Click here: Each Techy