We are currently in the middle of an AI boom. Experts in Machine Learning earn astronomical pay, investors are delighted to share their hearts and wallets when they meet AI startups. It is a good thing: this could be one of the transformative technologies that happens only once in a generation. Technology will not go away, and it will not stop transforming our lives.
This doesn't mean that creating your AI startup is simple. There are crucial pitfalls to avoid for anyone who wants to build a company around AI.
In the field of economics, two items are complementary if you prefer to purchase them together. Examples include automobiles and gasoline or cereal and milk, bacon and eggs, etc. If the cost of one complement decreases, the need for another complement will increase. The pair of the cloud is the program that is built over it, and AI products also have the property of needing a large amount of computational power. Therefore, it makes sense to ensure that its development is as cost-effective as is possible.
As thrilling as the development is, it's not good news for individuals and companies who have invested a lot of money in AI capabilities. These days, they provide an advantage in competition because acquiring the skill of a Machine Learning engineer will require a lot of time reading through papers and having an understanding of math at the very beginning. However, as accessibility to technology improves, this will not be the case anymore. It will be more about reading tutorials than research studies. If you don't grasp your advantage and you don't, an intern group with libraries might take your lunch. Particularly, if they have more information, which leads us to my next topic - the data is more crucial than costly AI architectures.
Data vs. AI Architectures
Let's say there are two AI startup founders, Alice and Bob. Their businesses have raised the same amount of funds and are fiercely competing in the identical market. Alice invests in the top engineers, PhDs, and PhDs who have solid track records for AI research. Bob employs average but skilled engineers as well as invests his ("Bob" is abbreviated as Roberta!) money into acquiring better information. Which firm would you place your money in?
My money is on Bob. Why? The essence of machine learning operates by removing information from a data set and then transferring it to the weights of the model. A more efficient model is more effective in this procedure (in the sense of speed or overall quality); however, assuming a level of adequateness (that is that the model is learning something), more accurate data will prevail over the more efficient design.
Furthermore, Alice's engineers aren't only competing with Bob's colleagues. Because of the open nature that is the hallmark of AIice’s AI group and the focus on sharing knowledge, they're in competition with researchers at Google, Facebook, Microsoft, as well as a multitude of universities all over the world.
The best-performing technology currently described in the literature and training it on your personal data is a tried and true strategy if your objective is to resolve a problem (instead of making an innovative research contribution). If there's no solution that's truly good to be found, the most likely scenario is of waiting for a quarter or two before someone comes up with an answer. In particular, you could organize things like a Kaggle contest to motivate researchers to study your specific issue.
The importance of good engineering is never diminished; however, if you're conducting AI, then the data is what gives you an advantage in the competition. The question of the billion dollars is, however, whether you will keep your edge.
What’s the Best Take on AI?
The way to categorize the value of something within a company is to determine if it creates value on its own or gives leverage to a different value-added source. Let's consider an online retailer as an instance. If you develop a new product line, you have created value immediately. It was not there before; today, there are widgets, and customers are able to purchase these. The creation of an alternative distribution channel, in contrast, can be an option.
When you start selling the products you make on Amazon, you can increase your sales. The reduction in costs can also be leveraged. If you can negotiate an improved deal in conjunction with the Chinese widget manufacturer, You can increase profits.
Levers are able to increase the speed of the needle than direct force applications. However, a lever operates when it is coupled with an immediate value source. The number isn't going to be small when you multiply it or even triple it. If you don't have any widgets to sell, acquiring the distribution channel you want isn't worth your time.
What is the best way to look at AI in this regard? There are a lot of companies who try to create AI as its own item (APIs to recognize images, other similar applications). This is a tempting idea If you're an AI specialist. But, it is not a good idea in the long run. In the first place, you're in competition with giants like Google as well as Amazon.
Additionally, creating an AI product that is truly useful is a nightmare. For instance, I've always wanted to make use of the Google Vision API. However, we have never run into a customer whose needs could be met in a satisfactory way by the service. It was never enough or too little for the customer, and custom-designed development was more preferable to the process of inserting a square peg into the round hole.
Possible Alternatives
An alternative is to think of AI as a tool. It is possible to use an existing business model and boost it by using AI. For instance, if you have a procedure that is dependent on human cognitive labor and automation can be a boon to your margins. The most obvious examples include ECG analysis or industrial inspection, the analysis of satellite images. What's exciting is that because AI is a back-end process, it gives you non-AI alternatives to maintain and build your competitive edge.
AI is a revolutionary technology that is truly transformative. But, putting your startup on it can be a challenging business. It is not advisable to solely rely on your AI abilities, since they're depreciating because of more general market changes. The creation of AI models could be fascinating, but what is important is having more information than your competition.
Being competitive is difficult, particularly when you meet a competitor that has more money than you. This is extremely likely when your AI idea is successful. It is important to develop an efficient data collection system that is impossible to duplicate by your competitors. AI is well-suited to change industries that depend on the brains of humans with low qualifications because it can automate the task.