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As Co-Founder and CEO of Google Deepmind, Demis Hassabis is an AI visionary who has dedicated his professional life to the field. A chess prodigy as a child, he holds a PhD in cognitive neuroscience and worked as an AI video games programmer early in his career. Hassabis co-founded the AI research laboratory DeepMind Technologies in 2010. Google acquired it in 2014, with Hassabis continuing as CEO. The acclaimed 2017 AI documentary film AlphaGo centres on a computer program developed by DeepMind and Hassabis that plays the abstract strategy board game "Go". Given his pivotal role in spearheading Google's AI efforts , Demis Hassabis's quotes on AI are a valuable insight into today's AI landscape. 1 - On his reasons for being involved in AI "I want to understand the big questions, the really big ones that you normally go into philosophy or physics if you’re interested in. I thought building AI would be the fastest route to answer some of those questions." In a 2023 interview with Time , Hassabis articulated how attempting to answer age-old questions about human existence fuels his passion for AI. 2 - On using AI to solve some of humanity's biggest problems "I would actually be very pessimistic about the world if something like AI wasn’t coming down the road." At the 2018 Economist Innovation Summit, Hassabis spoke about how AI could be the quantum technological leap needed to help address pressing global issues like inequality and climate change. 3 - On AI's role in supporting human experts "It is in this collaboration between people and algorithms that incredible scientific progress lies over the next few decades." Writing in the Financial Times in 2017, Hassabis stated his belief that combining the abilities of AI to identify patterns and insights with the expertise of scientists will drive incredible progress over the next few decades. 4 - On the timeline to achieve artificial general intelligence (AGI) "Right now, I would not be surprised if we approached something like AGI or AGI-like in the next decade." Speaking with the Verge in 2023, Hassabis gave his thoughts on the timeline for achieving artificial general intelligence (AGI). 5 - On concerns about AI development "I think there are valid concerns and they should be discussed and debated now, decades before there's anything that's actually of any potential consequence or power that we need to worry about, so we have the answers in place well ahead of time." Speaking in 2015, Hassabis acknowledged legitimate concerns raised around the use of AI by, among others, Professor Stephen Hawking and Elon Musk, an early DeepMind investor. 6 - On the risks of AI being used by bad actors "This technology has such potential for enormous, enormous good, but it’s a dual-use technology. So if bad actors get hold of it, it could be used for bad things." Speaking with the New York Times in 2023, Hassabis discussed signing an open letter on making the risk of extinction from AI a global priority like other risks such as pandemics and nuclear war. 7 - On adapting to the rapid pace of AI change "You look at today, us using all of our smartphones and other devices, and we effortlessly adapt to these new technologies. And this is gonna be another one of those changes like that." In a conversation with CBS in 2023, Hassabis stated his belief that humans are an infinitely adaptable species and that we will adapt to AI as we did to mobile phones and social media. Aiifi's Thoughts on Demis Hassabis's Quotes Demis Hassabis's quotes on the future of AI provide us with a balanced perspective on AI's potential and challenges. Drawing on his lifetime of real-world experience, his thoughts highlight the transformative potential of AI, along with the need for caution in how we apply it. With his ongoing involvement at the forefront of AI development, he will continue to be a source of wisdom over the next few years and decades.

People are wondering about job losses due to AI and it is easy to understand why. There has been a deluge of AI content so far in 2023 - both written by AI and about AI - and a lot of it has focused on job losses. From mass layoffs to doomsday reporting it would seem that we are on the verge of an AI jobs armageddon. But is that really the case? Let's take a look at some AI job loss statistics so far in 2023. AI Job Loss Statistic #1: 800 Million Jobs Will Be Lost To AI by 2030 That is according to one small business research firm and I personally think this is totally incorrect. AI will utterly change how we work and interact with technology. However, artificial intelligence is only the latest in a long line of technologies that were slated as job-destroying, like the tractor, the sewing machine, and the computer, that in fact increased human production. This is most certainly the Worst Case Scenario and I deem it very unlikely. AI Job Loss Statistic #2: 300 Million Jobs Will Be Lost To AI by 2030 This is a similarly pessimistic report from Goldman Sachs . It is a lower number than the 800 million mentioned above but aside from that it is equally unlikely. There is no doubt about the ability of generative AI but some recent high-profile mistakes show we are a long way off AI taking over the workplace. AI Job Loss Statistic #3: 56% of Companies Have Adopted AI Already Over half of firms are using AI already according to Forbes . This one I certainly can believe. In fact, I see this as a positive step. We all have tasks we hate and letting advanced artificial intelligence software proofread long documents or speed up presentation making makes our lives easier. I do not see many farmers yearning for the days of manual plowing or accountants rushing back to paper and ink ledgers. AI Job Loss Statistic #4: 80% of Workers Will Be Impacted by AI This one is from the ever-reliable OpenAI and I tend to agree. They measure this as workers having 10% of their day impacted by artificial intelligence. In all my years of working, I have not come across an employee who would not jump at the chance of trimming their hectic workday by 45 minutes. AI Job Loss Statistic #5: Women Will Be Impacted More Than Men This is a tricky one but I think is going to be the case. Men tend more towards physical jobs than women and this current AI revolution is very much focused on white-collar and office-based work. Throughout history women have shown themselves to be resilient to the changing job market so this is not a reason to despair - but it is certainly something to keep in mind. AI Job Loss Statistic #6: 31% of Workers Are Worried About AI Taking Their Job This one comes from PwC and there is no reason to doubt it. In my own personal experience, at least one in three workers is concerned about AI. Here at Aiifi we are firm believers that learning about AI and adapting to it is the best approach to insulate yourself from any job losses due to AI. AI Job Loss Statistic #7: AI Could Create 97 Million New Jobs by 2025 Now that's more like it. The usually gloomy World Economic Forum is optimistic about the impact of artificial intelligence on the jobs market. Their very detailed report looks at this topic in great detail and one part stands out. They highlight the key skills needed to thrive in this new AI world as analytical thinking, creativity and flexibility. AI Job Loss Statistic #8: Only 122,900 AI Job Losses in 2023 Only 122,900 job losses in 2023 were attributed to artificial intelligence. This is only a small fraction of the widespread layoffs announced globally and shows that things are not as bad as the attention-grabbing headlines suggest. The US and Europe are both experiencing unemployment rates well below recent norms Conclusion From AI Job Loss Statistics It is perfectly understandable that workers are worried about the impact of artificial intelligence on their jobs and livelihoods. Technology has always forced us to change how we work and AI will be no different. However, the reports proclaiming hundreds of millions of job losses are based on a whole lot of assumptions that are not borne out by the reality of 2023 employment. That being said, there is absolutely no doubt that highly repetitive white-collar tasks will disappear, and this will impact different sectors of society unequally. The invention of the tractor impacted mostly rural men and the automated sowing machine impacted mostly urban women. AI will impact office workers, work-from-home freelancers, and anyone whose job is computer-based. F inancial analysts and web designers are among the most exposed to AI job losses according to Euronews for instance. So what can you do next? The obvious answer is to follow us here at Aiifi to learn about AI. Meanwhile, you can read about how to leverage AI (here) or about setting up your own AI automation agency (here) .

This is a question a lot of workers are asking themselves in 2023. From ChatGPT to killer robots the news coverage of AI tends towards the pessimistic and it is understandable that workers are concerned. But which jobs will be impacted by AI first? Will AI Take My Job in 2023? For an unlucky few the answer is yes. In fact, 122,900 workers have already lost their job to artificial intelligence this year. Almost one in three workers are afraid that AI will take their job and it is easy to see why. The news coverage is predominantly negative and some reports are quoting job loss numbers into the hundreds of millions. However, if you have survived this far then you will probably survive the rest of the year. Will AI Take My Job Soon? That really depends on your industry and the type of work you do . If your role is in any way physical then you are safe. Robotics is a fascinating area but the advancements are slow (relatively speaking) and the technology is expensive. Plumbers, electricians, farmers, nurses, bricklayers and anyone else who is in need of a good sit down at the end of their work day is safe. The main exception here is warehouse workers as this is one area where robotics already exist. However, if your job requires nothing physical beyond the occasional trip to the printer then you are potentially at risk. The more repetitive and monotonous the job, the more likely it is AI will replace it. Jobs that require creativity, decision-making, or responsibility are much safer. A recent report in Euronews highlighted mathematicians, accountants and legal secretaries as jobs that were 100% exposed to AI. These are by no means low-skill jobs, quite the contrary in fact. However, they are repetitive, fact-based jobs that often rely on rules rather than creativity or judgment. Remember, the more your job can be automated, the bigger the risk of it being taken over by AI. The reality is many companies will be targeting an aggressive reduction in headcount in these roles by 2030. That does not mean they will achieve it, but in this high-cost, high-inflation environment companies focus on cutbacks. The first wave of these job losses will come via automation. AI Job Automation As we discussed in our AI Job Loss Statistics post, AI and automation will impact all of us. Some jobs and some demographics may bit hit sooner but the AI automation drive is well and truly underway. While some jobs will be "automated away", for example, legal secretaries according to Euronews, others will remain but change massively. Sales jobs are one area that illustrates this AI impact from extreme automation. Sales jobs will never go away but through the use of generative AI, the industry could look very different in the near future. Where Will AI Take Jobs There is a general consensus that AI will impact the world's wealthier countries the most . This makes sense. The economies of wealthier nations are far more services based and already rely on technology more and that makes them far more susceptible to AI automation. There is also an economic factor. Companies are generally motivated by profits and reducing headcount in expensive regions generates more financial save than reducing headcount in low-cost regions. This might sound blunt but it is true, and the recent wave of job losses illustrates it. Will AI Take My Software Engineer Job For the most part, unfortunately, the answer looks like yes. OpenAI reckons software engineering jobs might vanish entirely. Personally, I am not so pessimistic but there is absolutely no doubt that AI will drastically change software engineering as a career and industry. This example from ChatGPT shows the ease at which generative AI can produce useful C++ code. Will AI Take My Graphic Designer Job Graphic design is another industry expected to suffer at the hands of artificial intelligence. Apps like Leonardo and Midjourney have gone viral in 2023 and it is easy to see why. Visual content is naturally engaging and text-to-image AI generators have caught the public's attention. The question is what will this do for job prospects for graphic designers? It is probably too soon to say, but my expectation is there will be far fewer people employed in this area by 2030 than there are today, and freelancers will be the worst hit. How To Prevent AI from Taking Your Job There are a few ways to prepare yourself against AI automation and AI job losses. The first thing to do is understand how vulnerable your particular job is to automation. Is your job white-collar or more physical in nature? Is it repetitive or creative? The other key thing to focus on is your own skill set. If, like many officer-based workers, your skillset has developed into the smooth execution of repetitive tasks it might be time to up-skill . A more drastic approach might be to consider a new line of work entirely. Of course, you can always follow Aiifi to stay up to date with all the latest AI news.

Of all the industries Artificial Intelligence (AI) has been transforming recently, podcasting stands out. While AI tools like ChatGPT and Synthesia have garnered much press attention, several AI tools have been making waves in the world of podcasting. These tools use AI to solve common podcast production pain points and problems. They have transformed podcast editing and production for established podcasters and have made podcasting far more accessible to newcomers. So how exactly is AI solving podcast production ? Rather than doubling as a sound engineer or video editor, AI lets you focus on the most vital part of podcasting - storytelling. Everyone has their own unique story to tell. Read on to see seven ways AI will help you to tell yours. 1. AI Speech-to-Text: Automatic Podcast Transcription No need to manually transcribe your audio or video files anymore. AI can automatically transcribe your recordings in seconds using advanced speech-to-text technology. Riverside, a leading AI podcasting tool , advertises " AI transcriptions with 99% accuracy " as part of its offering. A quick review of your AI-generated transcript to catch the 1% that was missed, and you are ready to go with a fully accurate transcript. 2. One-Click Removal of Filler Words like "Ums" and "Ahs" Research proves that people who speak without using filler words sound more persuasive and better educated. Thanks to AI, you can automatically remove filler words from your recordings. You and your guests sound more polished, while listeners have a better listening experience with filler words cut out. AI podcasting tool Descript lets you " purge your recordings of "ums," "ahs", "you knows", and a dozen other filler words with a click ". 3. Eliminate Audio Echo, Background Noise, and Auto-Level Sound Good quality audio is critical to a successful podcast. Listeners will switch off if your audio has background noise, an echo, or static. AI acts like your personal sound engineer, isolating speaker voices and regenerating and enhancing their audio quality while eliminating echo and background noise. Podcastle, an AI podcast editing tool , states its users have " no need to worry about recording in a noisy environment or using fancy noise cancelling equipment. AI-powered noise cancellation delivers clean, crisp audio to help you sound like you're in a professional studio. " 4 . Edit Audio and Video Using Text, Bypassing Complex Editors Audio and video editors can be horribly complex and intimidating, especially for beginners. AI has turned post-production podcast editing on its head. Now, you can edit your audio and video by editing your AI-generated text transcript, document style. Adobe is leading the way here with their AI-Powered Adobe Podcast tool - " Adobe Podcast Studio transcribes every word using the same industry-leading transcription as Adobe Premiere Pro. Simply cut, copy, and paste your audio just like a text document. Editing audio has never been easier. " 5. AI Voice Cloning: Easily Edit Words or Phrases in Podcasts AI text-to-speech tools use generative AI to clone your voice. Instead of manually re-recording individual words or sentences, you type what you want to be said. The AI model of your voice will say the words or sentences and match the tone of the surrounding content. Listeners will have no idea the new words were not part of the original recording. Descript's Overdub " makes correcting your recordings as simple as typing. Type any words that your audio or video tracks are missing. Make mid-sentence changes to real recordings – Overdub will match the tonal characteristics on both sides ". 6. AI-Powered Selection of Key Moments for Short-Form Clips Engaging, short-form clips for TikTok, Instagram Reels or YouTube Shorts are essential to hook people into watching entire episodes of your podcast. Creating them used to involve carefully watching your finalized recording to find key moments and then editing them into short clips. AI tools like Opus Clip automate this process " Our AI analyzes your video to identify the most compelling hooks, extracts relevant juicy highlights from different parts of your video, and seamlessly rearranges them into cohesive viral short videos. " These tools can automatically add customizable subtitles, emojis and sounds to increase engagement and build excitement for your podcast. 7. Leverage AI to Repurpose Your Podcast into Other Content Forms Why not take your podcast transcript and get AI to turn it into a blog post with an AI writing tool like WriteSonic ? Or use an AI marketing tool like Jasper to turn your transcript into captivating social media posts. Or use Opus Clip, mentioned above, to turn your long-form video into viral short-form clips. With AI, your podcast is no longer a single piece of long-form audio or video. It can be repurposed into other content types automatically, enabling you to extract total value for yourself and your brand from each and every episode. In recent years, rapid advancements in artificial intelligence have reshaped numerous sectors. Podcasters have been massive beneficiaries of this transformation. And the good news is the momentum shows no signs of stopping. AI podcasting tools continue to add new features, further alleviating the pain of podcast production. If you have a story to tell, there is no better time to launch your podcast.

There are a lot of questions surrounding AI Automation Agencies (AAA) and whether they are a legitimate business model. Several YouTubers began posting videos on "starting your own AI Automation Agency" over the summer. The volume of videos on the topic has grown exponentially, and many of these videos are of questionable quality. Certain YouTubers are merely posting videos about AAAs to get views rather than provide valuable insight. Despite the low quality of content, I have written previously about the overall idea behind AI Automation Agencies being sound. If you ignore the noise and the dubious online claims, there is a legitimate business model there if you target the correct niche . In this post, I will fact-check four claims made about AI Automation Agencies to see if they are legit. By clearing these points up, we can make an informed answer to the question, is AI Automation Agency Legit? Claim 1 - You can start an AI Automation Agency with $0 While technically accurate, the reality in the real business world is you need to spend money to make money with an AAA. The AI tools you will use in your agency all cost money. Charges for using AI tools are monthly, annual or per-usage basis. Before approaching potential clients, you must test and get familiar with these AI tools. Therefore, you must pay to get started and use these tools. Some tools do offer free trials or freemium versions. However, if you are serious about starting a business, you must create samples/prototypes to show potential clients what you can do for them. Solely depending on free trials or freemium versions is not a sustainable business strategy. You must also pay for basic business necessities, like a website and branded email address. You could use a free website builder and a Gmail or Outlook mail address. But these look cheap and unprofessional. Businesses will trust agencies that invest in a professional online presence. In business, as in life, first impressions matter. If you want to be taken seriously, you need to look serious. So the claim you can start an AAA with $0 is not legit in the real world. Claim 2 - AI Automation Agency is a brand-new business model YouTubers and content creators are using creative licence here. The term "AI Automation Agency" is new. It first started appearing online over the summer of 2023. The idea of companies/agencies automating tasks and processes for other companies using AI is not new. Large consulting firms like McKinsey , Deloitte and PWC have offered AI automation services for years. AI Automation Agencies are different because they are not targeting the large firms that McKinsey, Deloitte and PWC are. AAAs will target small and medium-sized businesses instead. These businesses are too small to be attractive to the large consulting firms. So an AAA can slide in and offer AI automation services to these companies instead. So while the AAA terminology and online buzz suggest a novel concept, in reality, it's a repackaging of an existing idea. They are scaled-down, focused versions of businesses that are already operating. This does not mean there is no opportunity to target small and medium-sized companies; I absolutely believe there is. However, it's essential to debunk the notion that an AI Automation Agency is an entirely novel concept. Claim 3: You can start an AI Automation Agency with zero experience No certification or qualification is needed to set up an AI Automation Agency. It is different from becoming a doctor or lawyer, where you need to pass exams to get credentialed. However, you need to know about AI, how AI is applied to automate business processes and how the AI tools used to automate business processes work. If you're unfamiliar with terms like machine learning , natural language processing, large language models or training data, then you don't understand the basics of what an AI Automation Agency does. Understanding the fundamentals of AI is a prerequisite to running a business that sells AI services. Before you go and pitch a basic service like an AI-powered customer service chatbot to a potential client, you need to understand how these chatbots work. Because you can be guaranteed, the client will ask you how they work. No one will use an AI Automation Agency if the owner does not understand the basics of the service he claims to offer. Clients want expertise, and they will only hire an agency whose owner is well-versed in the fundamentals of their services. So the claim you can start with zero experience is not legit. You need to understand AI, AI tools and what specific problems and pain points AI tools can solve for a business. If you already have these, then great, you have experience. If you do not have this experience, you need to learn about AI, start using the tools, and automate some of your own tasks to test the business model and see if this is really for you. Claim 4: You can hire a person or team to do the technical part If you have the thousands or tens of thousands of dollars, it would cost to pay a person or team to do the technical part; this claim may be valid. For 99% of people, this claim is not financially feasible. Most people will not have the cash to invest in hiring people from day one. The only realistic scenario for this to happen is to team up with technically-minded people to co-found your agency. For instance, combining forces—a marketer and a software developer—offers an agency a balanced foundation. In that case, it makes sense that the marketer leaves the technical work to the developer. Relying on freelancers from platforms like Fiverr or Upwork without a clear understanding of AI is also a poor approach. You will be talking with potential clients, looking at their processes and determining what you can automate. You need to understand how AI automation tools work to determine what you can do for clients. You only have a business if you can figure this out. I hope that by addressing these four claims, I have helped clarify whether AI Automation Agencies are legit. For the right people, there is a great opportunity out there. However, the idea that someone can jump in with $0, zero experience and no intention of getting involved on the technical side is erroneous. AI Automation Agencies are legit and represent a valid and promising business model. It is essential to differentiate between the accurate information written about them and misleading content.

In previous articles, we looked at what an AI Automation Agency (AAA) is, if AI Automation Agencies are a scam and the types of niches AI Automation Agencies target . However, we have yet to delve into the particulars of the AI Automation Agency business model. How exactly does an AAA make money? What does it sell, and how does it sell it? This article will be handy for budding AAA entrepreneurs and those curious about the new world of AI agencies. A business model is a company's plan for generating revenues and profit in a specific market. This article will examine how AI Automation Agencies are structured, their target market, their services, and how they price their services. We will also look at the expenses an AI Automation Agency will face and how they look to turn a profit on the revenue they earn. It is important to note that no one-size-fits-all business model exists. AI Automation Agencies are a new concept in online business. What we outline here is the general approach owners are currently taking. Some may do things differently, and that is ok. No AI Automation Agency rulebook exists that must be adhered to. A smart company will regularly review and update its business model to anticipate trends and challenges ahead. In a space as fast evolving as AI, AI Automation Agencies would be wise to do likewise. Structure of an AI Automation Agency The first piece in the AI Automation Agency business model is the setup and structure of an AAA. Most AAAs adopt one of three structures: 1. Sole trader A single person sets up an AAA. They do all the AAA's work, including product development, marketing, client onboarding, and the technical part of AI automation delivery. 2. Partnership Two or more founders join together to create an AAA. Often the founders will have complementary skills so that each can focus on a specific aspect of the AAA's business. For example, a marketer and software developer may join forces to create an AAA. The marketer is in charge of identifying potential clients and selling the AAA to the potential clients. The developer oversees service delivery and the technical side of using AI tools to automate tasks and processes. 3. Agency An owner, or owners, founds an agency and hires employees or freelancers to work in the agency. Different teams or departments in the agency will have different roles and responsibilities. An AAA might start as a solo venture or partnership before evolving into a fully-fledged agency as demand for its services grows. AI Automation Agency Value Proposition The "value proposition" is a core component of any business model. A value proposition describes the products/services an AI Automaton Agency offers and why they are valuable to potential clients. An AAA must also explain why its product or service is different and better than its competitors. There are two items an AAA must define for its value proposition: 1. Define a specific problem the AAA will solve There have to be problems or pain points companies have that the AAA can identify and define so that they can use AI to solve these problems. An AAA could specialise in a single issue or solve multiple problems. The key is that these problems must exist and be painful enough that a business owner is willing to pay to solve them. 2. Define the Target Market Agency owners must define a specific target market . The agency's AI product/service must solve the target customers' problems. Many AAAs target businesses with the following characteristics: Small and medium-sized (potentially sole traders too) Have issues/pain points the AAA can identify, define and solve with AI Do not have AI expertise in-house to solve the problems/pain points Are open to hiring and paying outside agencies for their expertise Products and Services an AAA Sells Once an AAA has a target market and identifies a problem/pain point to solve, they can finalise the products and services they will offer. The product portfolio of an AAA largely depends on market needs. Below are four examples of services AAAs are currently offering. Note that this list is merely indicative of services AAAs can offer. The range of services an AAA can offer is vast. Over 200 AI tools in 30 different categories are currently listed on Aiifi. An AI Automation Agency's services will only be limited by the imagination and business nous of its owner: 1. AI Content Generation Models Content in this context means marketing material, social media posts, product descriptions etc. With AI, you can train a content generation model on a company's existing content. The new content the model generates will then match the company's style and tone. Jasper is an example of an off-the-shelf AI marketing tool companies use to create new content. AAAs can use off-the-shelf tools or build more complex proprietary models for clients using other available AI tools. 2. AI Business/Data Analysis AAAs can offer AI-powered tools that provide new insights to help businesses in their decision-making processes. Many companies generate vast amounts of data every day. Before AI, they could not utilise or analyse this data meaningfully. An AAA can create a model to ingest and analyse company data automatically. This analysis can identify trends, patterns and anomalies to help businesses make informed decisions. 3. Internal & External AI Chatbots AI Chatbots can be integrated with a client's own data that is stored in their existing data/knowledge bases. Employees/clients can then query the chatbot to get answers to questions where the stored data/knowledge contains the answer to that question. Chatbots are used for many reasons, such as customer service, internal training and HR queries. 4. AI Integrations and API Flows AI automation tools like Zapier and Make, for instance, help different software applications communicate with each other. AAAs can automate workflows between various applications so that humans do not need to transfer data between the systems manually. Doing this saves time and increases accuracy as it minimises the chances of human error during the data transfer. Pricing AAA Products and Services Fee-for-service model An AAA will charge a set fee for an agreed service. The price can be a one-off, hourly/weekly monthly rate, or a client can engage the AAA on a retainer basis. Consider an example where an AAA is building an internal chatbot for a 15-person legal firm: One-off fee The AAA and the legal firm agree on an amount for the AAA to deliver the completed chatbot. This amount is agreed upon before the AAA starts to build the chatbot. Whether it takes the AAA 10 or 100 hours to build the chatbot, the amount they receive will remain the same. Hourly/weekly/monthly rate The AAA and the legal firm agree on an amount the AAA will be paid for a period of time worked. Again, the amount is agreed between both parties upfront. In this case, the AAA will get ten times the revenue if the chatbot takes them 100 hours instead of 10 hours. However, it would be difficult for the legal firm to measure how productive the AAA is during the hours they work. This method is more suited to short projects or AI automation consultancy work you may do for a client. Retainer basis Regarding an AAA, working on retainer means you agree to a scope of work or deliverables with a client in advance, and they pay you a certain amount each week or month. In our law firm chatbot example, the AAA agrees to work on retainer and is paid a monthly fee by the law firm. The AAA agrees to monitor the chatbot to ensure it is operational, ensures new data is feeding into it, and fixes any problems that arise with the chatbot directly with the chatbot service providers. AI Automation Agency Expenses One benefit of the AI Automation Agency business model is that starting with very little upfront investment is possible. Start-up costs are minimal compared to starting a franchise or brick-and-mortar retailer. You can also manage and minimise ongoing costs. Such a model enables entrepreneurs to launch an AAA with minimal initial investment. It is important to note that you will require some cash to start, and you need to identify where this cash will come from - personal savings, a loan, an investment etc. You will also need to budget how long the money will allow you to fund your AI Automation Agency, as you may not have revenue coming in the door for some time at the start. As AAAs grow, other expenses inevitably emerge. The below costs are vital for an AAA owner to consider from the very start: Building and hosting a website for your AAA A professional-looking website is essential for an AAA. You can build your website with a service like WordPress or Wix to keep costs low. The website can be as simple as a homepage, an about us page, a services page and a contact us page. Ideally, you will have articles/videos showing some of the work your agency has done for marketing purposes. Business email address with your AAA website domain Whatever website domain you use, you need a business email with that domain. If your website is: www.aiifi.ai, your email needs to be: sales @ aiifi.ai, support @ aiifi.ai or similar. A @gmail or @outlook address will do, but any serious business will have a business email address. Cost of using AI tools to train and learn how they work You must familiarise yourself with the AI tools you expect your AAA to use before you start soliciting clients. Ideally, you will use the tools for your own business/side project or friends and family first. You learn how the tools work and can now create articles/videos/prototypes to show potential clients. If you have a prospect interested in a chatbot and you can show them a working example of a chatbot you built that they can test, it is an invaluable marketing tool. Cost of using AI tools to deliver your AAA services Your most significant cost will be the cost of the AI tools you use to deliver your services. For some tools, you need to decide who pays for the tools and whose name the tool accounts are in. Either the AAA sets up the account in their name and pays for the tools, or the account is set up in the client's name, and the client is charged directly for the tool usage. Remember that some tools charge on a per-usage basis, so it is vital to monitor usage levels to ensure an unexpected bill does not suddenly arrive. Concluding AI Automation Agency Business Models The AI Automation Agency (AAA) landscape is rapidly evolving. For those interested in AAAs, a clear grasp of the AI Automation Agency business model is essential. Understanding their structure, value proposition, service offering, pricing models, and expenses provides an excellent foundation for anyone considering starting or hiring an AAA. As highlighted, smart businesses will review and update their business model periodically. I expect AAAs to do the same and look forward to the evolution of this new type of agency.

AI is revolutionizing every stage of the agricultural process from planning and planting to crop estimation and harvesting. It might not be a glamorous industry but it has long been at the forefront of technological advancement and the advancements of AI in farming are truly impressive. Artificial intelligence in agriculture is a far cry from generative AI like ChatGPT but it incorporates all sorts of technology from computer vision and machine learning to lasers and robotics. For many, agriculture is an abstract concept. With more and more people living in cities, farming and the production of our food has become quite removed from our daily lives. But agriculture underpins almost everything we do, from the food we eat to the water we drink and it is intimately linked to current affairs like climate change, deforestation and wildfires. Great work is underway to utilize AI across a wide spectrum of agricultural activities. Let’s take a look at some recent developments in the key stages of the farming process. Using AI To Plant Crops and Trees Problem: the Earth loses 26 million hectares of trees each year, with over 30% attributed to wildfires. Solution: AI-powered seed planting drones can help undo this damage by planting trees in previously inaccesible lands. Getting seeds in the ground is where it all begins. Each year farmers and other professional growers have to plant their new seeds. Although there is a science as to where and when you plant, it remains a hugely manual and time-consuming process. Farming and reforestation are undergoing wholesale improvement due to artificial intelligence. Pioneering firms like Flash Forrest and AirSeed have developed incredible futuristic autonomous flying machines to speed up this process. They use drones, computer vision, GIS and mapping technology to fly over inaccessible and hazardous lands and identify appropriate planting locations. Then, using robotics, they drop seed pods to the earth. These drones can plant seeds 25 times faster than manual human planting and are revolutionizing how we approach the planting process. The drones have a camera attached to their base. The machine then flies over the land and scans the ground for appropriate planting locations using computer vision (the same software behind facial recognition). Advanced machine learning algorithms are used to make these determinations. GIS and mapping software then record the desired planting locations, and either the same drone or a partner machine will drop or some cases fire the seed to the ground using advanced, highly accurate robotics. This explainer video from The World Wildlife Federation explains the process. The company AirSeed has a lofty goal of planting 100 million seeds next year. Their focus is on trees rather than crops but the technology is the same. Through the use of computer vision-enabled drones, they can plant as many as 20,000 seed pods every day , to help replenish forests that were destroyed by fires . Crop Fertilization Using AI Problem: waterways in many parts of the world have become contaminated by excessive nitrogen levels and 12% of the world's arrible land is no longer usable. Solution: Artificial Intelligence powered soil sensors allow farmers fertilize far more accurately, boosting yields, reducing pollution and increasing profits. Next up in the growing process is the act of fertilizing, essentially feeding the young plants. Every plant requires the right mix of nutrients and minerals to grow, and some are more sensitive than others. One of the key nutrients is nitrogen, which is produced from ammonia by naturally occurring biomes in the soil. If the nitrogen levels are too low crop yields can be harmed. If nitrogen levels are too high then it can become a pollutant , especially when it runs off into water supplies. Through the use of advanced sensors, known as chemPEGS , or “chemically functionalized paper-based electrical gas sensor,” machine learning algorithms can accurately predict soil nitrogen levels over the next week or so (generally up to 12 days). This allows farmers to fertilize more accurately and sparingly, reducing their costs and minimizing the risk of pollution and run-off. Weeding and Pest Reduction Using AI Problem: the earth loses 40% of annual agricultural crops to pests. Solution: AI-powered technology can remove 200,000 weeds per machine per hour and reduce pesticide useage by 90% . Weeding is the bane of any farmer’s existence and one of the primary applications of artificial intelligence in agriculture. The suite of technology here is quite similar to what we saw during the planting process, with computer vision and machine learning underpinning the automation. However, instead of connecting this equipment to futuristic drones, these AI-powered weeding rigs are connected to normal-looking farm equipment and tugged along behind a tractor. Of course, beneath the surface, this technology is still cutting-edge. One of the pioneers in this space is Blue River Technologies (read our in-depth case study here) with the groundbreaking Sea & Spray product. In simplistic terms, this technology uses facial recognition software to identify weeds and spray them with pesticides. This selective application of chemicals allows for a 90% reduction in pesticide use compared to a more traditional blanket spray approach, saving farmers money and reducing the amount of toxic pesticides being used. Another approach is the laser-zapping technology of Carbon Robotics . The setup is very similar to See & Spray, but instead, the weeds are zapped out of existence by laser. This technology can remove up to 200,000 weeds per hour , something that would take 70 people to achieve using non-AI methods. Harvesting Crops Using AI The final part of the farming process is typically the harvesting of the crop. Some types of produce are easily harvested by machine, and generally the more delicate the product the harder it is to automate its harvesting. Combine harvesters have existed for decades, automating the relatively straightforward harvesting of cereals. Strawberries, kiwis and other such fruits were long considered beyond our technological capabilities as the fruits are so delicate. These are typically still picked by hand. AI is changing all that. Once again computer vision is a key component of this process. A camera is used to scan the plants and identify pickable fruit. GIS is used to map out where this is. Robotics is used to gently grab or hoover up the fruit. This is hard to imagine but the below video from AbundantRobots illustrates the process. Crop Yield Estimation With AI Apart from automating the mechanical aspects of farming, AI is being used to improve the science of agriculture too. Understanding crop yields is a vital component of farming. It is also part of governmental and economic forecasting. It impacts food prices, can help predict and prevent famines and plays a big role in global financial markets. Companies like Gro Intelligence use artificial intelligence to make highly accurate predictions about agricultural output. They utilize over 170,000 data sets in their models, everything from satellite imagery and soil samples to price data and the weather. Their proprietary models are then used to predict yield estimates from crops like Brazilian soy and US corn, measure the amount of arable land in West Africa, or predict if unseasonably high rains will damage European crops or cause urban flooding. This does not change the processes of how we farm, but it does allow farmers, corporations and governments to make better decisions in terms of agricultural planning. This can help tackle some of humanities greatest problems from hunger and famine to flooding and wildfires. AI Advancements In Farming Summary Artificial intelligence is driving some real change in farming, and farming is helping advance AI. It is helping us make better decisions, plant more seeds, access more land, roll back deforestation, use less chemicals and make farming more profitable. Farming has long been at the forefront of technological advancements and in this era of artificial intelligence, agriculture is once again driving human development.

Known as "The Godfather of AI", Geoffrey Hinton's pioneering work laid the foundations for many of the artificial intelligence (AI) applications we use today. A computer scientist and cognitive psychologist, he holds a PhD in computer science from the University of Edinburgh. With Yoshua Bengio and Yann LeCun, he won the Turing Award, referred to as the Nobel Prize for Computing, in 2018. In the early 1990s, Hinton began working on deep learning, a type of machine learning that uses artificial neural networks to learn from data. His work was initially met with scepticism, but his refusal to alter course proved correct and eventually led to a revolution in AI. Today, deep learning is used in various applications and AI tools , including driverless cars, natural language processing, and facial recognition systems. Hinton worked for Google from 2013 to 2023. He helped create Google Brain, a research team that is dedicated to advancing the state of deep learning. Hinton left Google in 2023 so that he could speak freely about the dangers of AI. He warned not enough guardrails were in place to control the technology and that he has slight regrets about some of his contributions to the field. In a world increasingly reliant on AI, Geoffrey Hinton's quotes, insights, and warnings are more critical than ever. As the debate on the future direction of AI continues, his perspective, grounded in decades of experience and research, serves as a crucial guide as we explore this new frontier. 1. The Dichotomy of Intelligence: Biology vs. Logic "Early AI was mainly based on logic. You're trying to make computers that reason like people. The second route is from biology: You're trying to make computers that can perceive and act and adapt like animals." Back in 2011, Hinton highlighted to the Globe and Mail the two main approaches to artificial intelligence : one based on human logic and the other on biological adaptation. He believes that learning and adaptation, which form the cornerstone of deep learning, will be critical to creating a complex form of artificial intelligence. This is a paradigm shift from traditional hand-programmed AI. 2. A Rocky Road: Hinton's Early Belief in Neural Networks "I had a stormy graduate career, where every week we would have a shouting match. I kept doing deals where I would say, 'Okay, let me do neural nets for another six months, and I will prove to you they work.' At the end of the six months, I would say, 'Yeah, but I am almost there, give me another six months." Looking back on his time in academia with the Globe and Mail in 2017, Hinton mentioned that despite facing scepticism and resistance in the early days of his career, he remained steadfast in his belief that neural networks would eventually outperform logic-based approaches. They had been discredited at that time, but Hinton never doubted that they would one day prove superior to the logic-based approach. This conviction laid the groundwork for the resurgence and widespread adoption of neural networks in modern AI. 3. The Morality Spectrum: The Influence of Human Bias on AI "AI trained by good people will have a bias towards good; AI trained by bad people such as Putin or somebody like that will have a bias towards bad. We know they're going to make battle robots. They're not going to necessarily be good since their primary purpose is going to be to kill people." At the Collision conference in 2023, Hinton underscores the dual nature of AI , highlighting that human decisions ultimately shape its impact on society. He emphasizes the critical need for proactive measures to mitigate the negative consequences of AI. His concerns resonate deeply in a world grappling with the ethical implications of rapidly evolving AI technologies. 4. A Double-Edged Sword: The Unseen Dangers of AI Enhancement "I am scared that if you make the technology work better, you help the NSA misuse it more. I'd be more worried about that than about autonomous killer robots." Speaking with the Guardian in 2015, Hinton played down concerns about the dangers of autonomous AI, directing attention instead to a more immediate problem: the misuse of AI by influential organizations for surveillance and other malicious purposes. His perspective highlights the importance of addressing not only the long-term risks of AI but also the immediate threats posed by its integration into existing power structures. 5. The Promise of Progress: Sharing the Benefits of AI "In a sensibly organized society, if you improve productivity, there is room for everybody to benefit. The problem isn't the technology, but the way the benefits are shared out." In a Daily Telegraph interview in 2017, Hinton expressed a measured optimism about the potential of AI to revolutionize fields like medicine and contribute to economic progress. However, he noted that the key challenge lies in ensuring the benefits of these advancements are equitably distributed across society. 6. The Inevitability of Progress: A Global Race for AI Advancement "The research will happen in China if it doesn't happen here because there's so many benefits of these things, such huge increases in productivity." In an interview with National Public Radio (NPR) in 2023, Hinton commented on why he did not sign a letter signed by 30,000 AI researchers and academics calling for a pause in AI research. He acknowledged the concerns of the broader AI community but argued that halting research is not a viable solution. His stance highlights the complexities and challenges of regulating AI while development proceeds at breakneck speed. 7. A New Chapter: Hinton's Commitment to Responsible AI "I want to talk about AI safety issues without having to worry about how it interacts with Google's business. As long as I'm paid by Google, I can't do that." On leaving Google in 2023, Hinton commented to the MIT Technology Review that he left so that he could openly express his concerns without the constraints of corporate interests. He intends to contribute to the discussion about responsible AI development and deployment. Hinton's 2023 departure from Google marked a pivotal moment in his career, as he chose to prioritize ethical considerations over corporate allegiance. His decision underscores the importance of open and candid discussions about the responsible development and deployment of AI, free from commercial pressures. Conclusion to Geoffrey Hinton's Quotes Having left Google, Hinton continues to talk about AI publicly. While he believes progress in the field of artificial intelligence is inevitable and probably a good thing, he qualifies this with the warning that we need to ensure AI is used for good and that no existential threat is conceived. As AI continues to evolve and permeate every aspect of our lives, Geoffrey Hinton's quotes, insights and warnings become increasingly important. The safe and ethical development and deployment of AI should be a priority for governments, companies and citizens across the globe.

As the world of agriculture and farming undergoes an AI revolution we look at some key statistics. The Need For Artificial Intelligence Solutions in Agriculture 12% of Arable Land that was once viable farmland is no longer usable due to excessive nitrogen levels. The chemical is a necessary fertilizer but through incomplete information and overuse, it has led to water pollution. 30% of deforestation each year comes from wildfires and the need to replant is growing rapidly. Deforestation is a concern in its own right but it can impact farming directly through land destruction and indirectly by releasing gigatons of carbon . 40% of crops are lost to pests each and every year. This staggering number is due in part to farm labor shortages , climate change and pesticide resistance . With an ever-growing requirement for calories to satisfy global populations this problem cannot be underestimated. Food production needs to increase by 70% over the next 50 years to match population growth around the world. Otherwise, we will not produce enough food to satisfy basic human caloric requirements. Current AI Capabilities in Agriculture AI-powered planting drones can plant 40,000 at a rate 25 times faster than humans or non-AI machinery. These airborne machines can reach difficult-to-access lands and make previously unusable terrain arable again. AI-powered technology can remove 200,000 weeds per hour per machine, with one machine replacing the work of 70 people. This is based on laser-zapping technology and computer vision (facial recognition) software. AI-powered technology can reduce pesticide usage by 90% by using computer vision, robotics and machine learning. Rather than blanket spraying entire fields of crops AI-powered machines can spray the right herbicide or pesticide exactly where it is needed. Fruit harvesting machines have a harvesting success rate of 51% for highly difficult and delicate produce such as kiwis. There is obviously plenty of room to improve but it is already a significant improvement in a difficult. Agrobot has developed an AI-powered strawberry picker that can harvest close to 20 acres in three days . This 24-armed robot is vastly more productive than human picking and helps alleviate labor shortages. The Big Numbers With AI expected to contribute to planting a billion of seeds over the next five years it is no surprise the industry is expected to grow. By one estimate the industry will be worth almost $5 billion by next year and could grow exponentially after that. Artificial intelligence is already making a massive impact on agriculture and that is good news for farmers, consumers and the planet. For more information, read our in-depth analysis of the impact of AI on agriculture here .

Artificial Intelligence is changing all sorts of industries from farming to finance, and sport is no different. The AI impact on sports will be profound, particularly when it comes to sports data science analytics. Artificial Intelligence is helping teams in every aspect of their management, from recruiting talent and keeping players fit to improved training and in-game performance. For many people, AI means little more than ChatGPT and funny images but the truth is this is such a thin sliver of AI that it overlooks so many groundbreaking new technologies. Let's take a look at how some of these AI technologies are being applied to sports. AI Performance Analytics in Sports Sports analytics was originally very rudimentary and really only in the last 20 years has the analysis become formalized. Sports analytics was popularized by the movie Moneyball , based on the book of the same name, which detailed a groundbreaking new approach to baseball analytics. The gist of the approach was that The Oakland A’s should not try to recruit the best individual players, but rather recruit players who generated the most runs (even in indirect ways) and therefore wins. The term is now used across sports for coaches and organizations using analytics to underpin new approaches to tactics and recruitment. Rather than old-fashioned “eye tests” or the use of rudimentary statistics like home runs, sports data science is incredibly complex and takes all sorts of metrics into account. A very simple example is how you might measure the performance of a quarterback (QB) in American football. Basing your QB analysis solely on the number of team wins is clearly not insightful. It might capture some aspects of the player's contribution but at a minimum, the analysis will be clouded by the team’s defensive performance. The analysis could be refined by just looking at how many points the team scored and disregarding how many they conceded or allowed. However, that still does not paint an accurate picture of the QB performance and you will see many in sports media discuss a QB based on the number of touchdowns and interceptions thrown (i.e. how many of his passes lead to a score and how many turned over possession to the opposition). This is clearly somewhat accurate. However, more advanced analysis of QB performance will look at metrics like catchable passes (passes that should have been caught, rather than ones that were actually caught) or EPA (Expected Points Added) and CPOE (Completion Percentage Over Expectation). Advancing Sports Analytics With AI As the analysis becomes more and more advanced, the scope for technology to play a role increases in parallel. The primary type of artificial intelligence used in sports analytics is machine learning and the analysis of “big data”. The ability of machines to find patterns is far beyond that of humans, particularly when it comes to analyzing vast amounts of empirical data. Similar to our stylized QB example above, AI-powered machine learning algorithms can help identify if a player scores a lot because he himself is exceptional or if he is reaping the reward of an exceptional player elsewhere on the team or the contribution of others. Learn how computer vision and machine learning can be appied to pickleball AI performance analysis. A good example in Association Football, or soccer, is the evolution of measuring a player’s attacking output . Originally a player would be judged on goals scored. Then the concept of assists was added, to measure not only scoring but facilitating other teammates scoring. However, this did not take into account poor finishing by other players so now teams will measure Expected Assists (xA). This is similar to the catchable pass in American football. If you pass the ball to a teammate and that pass should be scored 100% of the time (which, admittedly is unlikely) you would then receive an xA score of 1.0 - However if that pass should lead to a goal threequarters of the time it would give you an xA score of 0.75. Types of Data in AI Sports Analytics This type of analysis is often referred to as Event Data. You track the key events in a game, such as goals, passes and fouls in soccer, and then give the information to powerful machine learning software and it will identify patterns. Essentially this measures what happens in a game, when it happens, and what the patterns are. However, we can take the analysis even further by adding another AI-powered technology, Computer Vision. This is the same software that underpins facial recognition technology. Essentially this technology converts camera images into analysable data. The application of computer vision in sport science is known as Tracking Data. Sticking with our soccer analogy, let's consider what happens in the build-up to a goal. One player scores, one player gets an assist, and both of these will be measured by metrics such as xG (Excepted Goals) and xA (Expected Assists). However, what about players who contributed without touching the ball or generating an event (i.e. something that would be recorded as part of Event Data)? That is where Tracking Data comes in. Soccer coaches are always training their players to make off-the-ball runs. This creates space for your teammates and can cause confusion among the opposition players. Through the use of cameras and computer vision AI can now track these runs and analyze their contribution to the team’s performance. While one player is scoring all the goals, others may be contributing significantly to this through selfless running or passing earlier in the build-up or pre-scoring phases. This would be practically impossible without artificial intelligence. Even if we could see all 22 players on the field it is far beyond the capacity of a human brain to track and record so much simultaneous movement. With AI it is essentially automatic. Most professional sports games have many cameras and these are now using computer vision to convert the position and movement into Tracking Data and recording it for machine learning analysis. The application is similar to the AI-powered Second Spectrum software used in the NBA. This artificial intelligence uses the same logic as discussed above, with computer vision software tracking the position of players and their actions and recording it to a huge database of games. Machine learning algorithms then analyze the data, giving coaches insights into how plays develop, how to defend better and so much more. The use of AI in sports analytics is still in its infancy but it is already taking the in-game and performance analysis to the next level through computer vision and machine learning. What about other areas of sports science? Lets take a look at some other ways AI is being used to modernize sports analytics. Artificial Intelligence and Injury Prevention in Sports Injury prevention is a key component of sports science and as the value of players continues to increase teams are becoming more and more concerned about keeping them healthy. Using the same suite of technology as in performance analysis teams can predict injuries and make changes before a player is hurt. Obviously, not all injury is predictable but in some cases, there are telltale signs and AI can identify when a player is running an increased risk of injury. One cutting-edge example is using computer vision to see when a player might begin to favor one side of his body over the other. For example, consider a player whose left ankle is weaker than his right. Computer vision will recognize when he starts to favor his right side before the human eye would, and that would be a warning sign that the risk of injury is too high. This is considered to be a leading indicator of an injury and as any sports fan, athlete or coach can tell you, prevention is better than cure. Artificial Intelligence and Player Recruitment Another area of sport being revolutionized by AI is player recruitment. Artificial intelligence helps coaches make better in-game decisions , better injury prevention decisions, and now we can see how it helps make better recruitment decisions. Using similar data and technology, artificial intelligence can help teams in two ways. Firstly, artificial intelligence can show teams what their true need is, and secondly, AI can reveal the true qualities a player has. The Use of AI in The NFL Draft Player recruitment is difficult in any sport but there is nothing quite as intense as the NFL draft . Pro Football in the US enjoys one of the most level playing fields in all of sport, thanks in no small part to the draft system. This sees the best 250 or so university players each year move to the NFL with the weaker teams getting the first choice of picks. Teams need to understand what their weaknesses are and have a plan of how to address those shortcomings by bringing in new talent. Artificial intelligence lets coaches see patterns in performance data, both of their own team and the players in the draft, and make better decisions. Does a college quarterback throw the ball exceptionally well , or does he have receivers who are so good they make his mediocre passes look more impressive than they really are? That is a straightforward question and one that most American football coaches could probably answer themselves without the help of any technology. There are, however, many other similar questions where AI is far more advanced. The performance of fullbacks or running backs could require tracking the position and performance of many other players at the same time and is therefore more suited to computer vision. Off-Field Decisions and On-Field Performance Recruitment technology is now taking into account additional data beyond just game day performance. Just because a player is good it does not necessarily mean he will be good for you. Sport is littered with flops and stars who didn't cut it and that is often down to off-field factors as opposed to any physical or technical attributes. NFL teams are now incorporating player social media into their AI-powered recruitment algorithms to help better understand the players. AI Recruitment in Soccer Soccer teams spend billions of dollars every year buying players and many clubs seem to struggle to find the right fit. The Cristiano Ronaldo-inspired Saudi League is offering clubs a legitimate exit strategy for marquee signings that did not fit their system, but historically, overspending on a big-name player was a disaster for European football clubs. Unlike in American sports, in Europe players are paid whether they play or not, and it can be impossible to get an expensive player off your books. English heavyweights Manchester United are currently suffering in this way having overpaid for the defender Harry Maguire. Maguire led Leicester to a history-making Premier League win in 2016 and he was considered one of the best English centre-backs. Then he made the move to England’s most successful team for a record £80 million pound fee and it looked a perfect move. Except it wasn’t. Almost nothing has gone right for Maguire at United and the club has tried to offload him multiple times but his wages are too high and he is almost unsellable. Could AI have prevented this? Well, the truth is very probably, yes. Just because Maguire was a good defender in one system a Leicester it should not be assumed he would be as effective in Manchester, surrounded by different players, playing a different style, for a different coach. This stands in stark contrast to the approach taken by far smaller clubs like Brentford. Inspired by their gambling and stats-obsessed owner Matthew Benham the London club have used Moneyball techniques to buy diamonds in the rough over and over again. This has allowed them to reach and stay in the English Premier League on a far smaller budget than their rivals. Their AI approach to recruitment would never allow them to waste £80 million on a big-name player who did not fit the system. Artificial Intelligence in Sports Analytics AI is changing every single industry and sport is no different. Computer vision allows us to record far more than the human eye can see, and machine learning identifies patterns hidden in the data. This is helping coaches and teams make better in-game decisions, prevent injuries, and recruit better. Some teams have leaned into this new frontier while others are slowed to adapt and the Rob Thomas quote comes to mind. The IBM Senior Vice President said, "AI is not going to replace managers, but managers who use AI will replace the managers who do not." Artificial Intelligence is not going to win football games, but football teams who use AI will win over teams who do not. Want to learn more? Read how football teams use AI in areas such as recruitment and injury prevention .