question:You are an expert tech writer for Forbes Council. Rewrite with clear flow of ideas from one paragraph to another. Include all the important points of every paragraph, don't miss out on any paragraph . Ensure that the article is only within 600 words, "Advancements in artificial intelligence and automation are transforming the way businesses operate, providing opportunities for small businesses to significantly improve their operations. AI and automation enable small businesses to streamline processes, reduce costs and improve customer experiences, making it easier to stay competitive in an increasingly crowded marketplace. Despite concerns about cost, complexity and disruption, small businesses can successfully embrace these technologies with the right strategy and resources. Common areas where AI and automation can be applied in small businesses include customer service, sales, marketing, inventory management, accounting, and human resource management. However, there are also challenges to overcome, including cost and complexity, resistance to change by employees, data quality and security issues. To successfully leverage AI and automation, small businesses should focus on the customer, monitor and measure impact, invest in employee training and education, collaborate with others and start small. There are many case studies showcasing the possibilities and benefits of AI and automation for small businesses. Akaike Technologies is an AI-native company that provides sustainable, multimodal AI solutions to improve efficiency and increase ROI for customers. They offer a comprehensive range of Vision AI, Speech AI, and Text AI services that cater to the unique needs of each business. During the early 20th century, science fiction introduced the world to the concept of AI robots. By the 1950s, young British polymath Alan Turing explored the mathematical possibility of AI and how to build and test intelligent machines. However, computers needed fundamental changes and hefty expenses deterred investment in AI research. The proof of concept for AI was initialized through Allen Newell, Cliff Shaw, and Herbert Simon’s Logic Theorist program in 1956, which catalyzed 20 years of AI research. AI flourished from 1957 to 1974, but several obstacles emerged mainly due to lack of computational power. In the 1980s, AI was reignited by new techniques such as deep learning and expert systems. Although ambitious goals of the Japanese government’s Fifth Generation Computer Project were not met, AI thrived during the 1990s and 2000s, achieving milestones like IBM’s Deep Blue defeating chess champion Gary Kasparov in 1997. Improvements in computer storage enabled AI to make significant progress, and AI is now widely applied in fields such as technology, banking, marketing, and entertainment. The future of AI could involve AI language and driverless cars, while general intelligence and sentient robots remain long-term goals. As AI continues to advance, discussions about machine policy and ethics will be crucial. Before you know the story of Akaike Technologies, it’s important to know the story of our Founder Rahul Thota and his tryst with data science and artificial intelligence in career spanning 20 years. It all began during his second year at college when he was trying to improve my grades with extra projects. Rahul decided to speak with his hostel warden, Professor Jalihal, and asked him if there was any work he could do. Professor Jalihal came up with an interesting project for Rahul – converting a color image to black and white and then the other way around. Back then, we used MATLAB to handle this task. Using MATLAB, Rahul managed to convert the color image to black and white easily, although converting black and white to color wasn’t mathematically possible. That project really got him into image processing, and Rahul went on to work on more projects and internships in the field. At the time, he was using tools like MATLAB and, eventually, Python became a popular alternative. Over time, Rahul gained more experience by working with different companies and eventually started his own company, Akaike Technologies. Looking back, it’s fascinating to see how the tools and methods of learning have evolved throughout the years. Today, there are degree programs in AI/ML and a stronger emphasis on self-learning and problem-solving. Overall, it’s been an incredible journey, and Rahul is grateful for the opportunities and advancements in the fields of image processing, computer vision, and AI that have made it possible to develop effective solutions for complex challenges we once considered impossible to solve. You know what we realized? Python is like a cheat code for non-CS folks to do really cool things. Even though my job doesn’t involve coding, I still use it all the time. It’s such an approachable language that you can start seeing results quickly and that motivates you to keep going. Plus, you can do almost anything with it once you get the hang of it. Speaking of programming languages, let’s talk about Matlab. Remember that image conversion project? It was easy to convert a color image to black and white in Matlab - just a few lines of code. But going from black and white to color is a totally different ball game. It’s mathematically impossible to go from one number (grayscale) to three (RGB) Nowadays, you can train an AI to do that kind of thing, but back then it was unheard of. Anyway, Rahul always loved working with images and graphics, which is why he did a couple of internships at a government research institute. He got to play around with all sorts of satellite and remote sensing data, like capturing images of different types of vegetation. It was fascinating to see how different spectrums could indicate different ways that plants grew. Even water bodies had different characteristics! From there, Rahul moved on to work with medical imaging and surveillance at Siemens and Microsoft, where he did a ton of research on background subtraction and registering CT scans and MRIs. It was so cool to see how combining functional and structural information could give deeper insights. But back in those early days, Rahul had to deal with very specific problems, like tracking customers walking into a showroom. There was no one “right” way for them to enter, which made it a tricky (and expensive) problem to solve. Neural networks were just a concept, and cloud adoption was just starting to become a thing. It’s amazing to see how far image processing and computer vision have come since then where you can even find solutions for unconstrained problems. So, there are two different scenarios we can talk about. The first one is when we were working on replicating some facial detection work. We were able to come up with a new scheme where we could extract a million features from a small image. It was pretty cool because we could identify vertical and horizontal patterns at different scales, which helped us figure out the facial pattern. We did it all from scratch, which was a bit challenging! But nowadays, things are a bit easier because you can find open source code for almost anything. This means that you can clone a project on GITHUB and replicate it. Before, a lot of the research time went into just trying to replicate past results, but now you have access to all the code. Reputed researchers even put out their own code. In the AI space, the availability of models and new ideas with different architectures is amazing. You can search and get new pre-trained models online. But the problem isn’t finding a good face detection model or people detection model. It’s really about understanding the constraints of the client’s ecosystem and then making it work in the production setup. Once you have a solution, distributing it to the right people is what really matters. That’s the bottom line. Previously, a small dataset of only 5000 models was available and it required a lot of time to create a large repository of data. However, now there are thousands of datasets available for different varieties. Back then, training a model would take a month due to the lack of high-level tools and the limited computing ability. Today, the same computation can be done in just a day. The availability of datasets and advances in computing have made a huge difference. In comparison to the past, there is now a much higher level of interest in AI across departments, with groups from different areas coming together to build AI solutions. While in the past, coding was done mainly in C++, to build production models, it is no longer necessary to use low-level languages like C. The focus has shifted towards higher levels of abstraction, and there are now four-year degrees in AI from reputable institutes. While guided learning may be required when starting off, self-learning soon replaces it, and individuals become their own guide with maturity and confidence. While there is no clear answer as to whether CS or AI/ML degrees are better, the number of people graduating with guided data science degrees is increasing significantly. There has been a lot of change in terms of the companies and sectors that were previously involved in AI research. Many companies have now spearheaded AI research, and the most promising latest research is coming from them. Even within companies, there has been a diversification of work, with a lot more focus on computer vision and advertising data. One significant development in computer vision is the Voyalla and Johns paper, which has increased the detection accuracy of faces from 60% to 99.99%. There has been a flurry of papers published on people detection since then. Overall, advances in technology have made building companies around AI justified, and there is a lot of excitement about the possibilities. AI is delivering real value to businesses, with insurers automating manual underwriting processes, retailers personalizing shopping experiences, and healthcare systems predicting patient outcomes. The growth of AI can be attributed to increasingly powerful graphics processing units, data scientists, and sophisticated algorithms. However, AI adoption for many enterprises remains at an experimental stage due to uncertainty regarding business prospects and confusion over which problems AI can solve. There is also fear over the unknown and the potential costs of AI deployment. Nevertheless, early AI adopters can greatly benefit from redefining their industries, gaining recognition as technology trailblazers, and shaping the transformation of their respective sectors. AI is becoming a game-changer, and although deployment costs are expected to decline, businesses that delay AI implementation may find it difficult to recover or catch up in the fast-paced market. AI algorithms can significantly benefit businesses in various industries, such as advertising, agritech, aviation, BFSI, education, entertainment, healthcare, hospitality, human resources, manufacturing, retail and e-commerce, and logistics. It can help with audience targeting, ad placement, ad creative, bid management, and predictive analytics. In agritech, AI applications include precision agriculture, crop management, predictive analytics, livestock management, and supply chain optimization. The aviation industry can use AI for flight operations, maintenance, passenger experience, safety and security, and air traffic management. In BFSI, AI can help with fraud detection, risk management, customer service, process automation, and personalized marketing. Education can benefit from personalized learning, adaptive assessments, virtual tutors, automated grading, and predictive analytics using AI. The entertainment industry can utilize AI for personalized recommendations, content creation, virtual assistants, and gaming. Healthcare can use AI for medical imaging, personalized treatment, drug discovery, virtual nursing assistants, and administrative tasks. The hospitality industry can use AI for personalization, chatbots, revenue management, operational efficiency, security, and marketing and advertising. Human resources can use AI in recruitment, candidate matching, employee engagement, performance management, diversity and inclusion, and training and development. Manufacturing can use AI for predictive maintenance, quality control, supply chain management, autonomous vehicles, process optimization, and product design. Retail and e-commerce businesses can use AI for personalization, demand forecasting, pricing optimization, chatbots, visual search, and fraud prevention. Vertical industries, such as logistics, can use AI for predictive analytics, route optimization, inventory management, warehouse automation, and last-mile delivery. Inspection and defect detection can benefit from AI-powered image recognition, sensor analytics, predictive maintenance, and quality control. Customer success teams can use AI for customer data analysis, chatbots, predictive analytics, automated workflows, and knowledge management. AI can also be utilized for image and video analytics, sales and marketing, and learning and development. Their modular and sustainable approach to AI enables the development and deployment of more flexible, cost-effective systems. They also provide cutting-edge expertise in generating synthetic data, optimizing business processes, and delivering personalized, targeted experiences. All these services are facilitated through their AI Value Creation Lab, which allows businesses to stay competitive in the market. For small businesses looking to reap the benefits of AI implementation, Akaike’s services can provide valuable analytical insights, enhance interactions with visual data, interpret natural language, and transcribe text in multiple languages. Additionally, their low-code AI platform allows for quick construction, testing, and deployment of business process management applications."

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