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5 Artificial Intelligence trends shaping business in 2017
Artificial Intelligence as an industry matures. Many of the problems that impeded adoption of this technology in the past are disappearing and AI reveals its potential to change how we live, move, and work. So let’s have a look at the key AI trends that will shape our future.
Artificial Intelligence Trends 2017:
1. Democratization of AI tools
Tech industry leaders, Google, Facebook, Microsoft, Apple, now compete in the field of AI platforms, announcing new tools and APIs that will help coders build the next generation of AI-powered applications. The Machine Learning frameworks offer pre-trained APIs, algorithms, development and training toolkit, as well as computing power to train and deploy models into your applications.
Democratization of AI tools will enable more companies to try these new technologies. Powerful machine learning will become easier for you to incorporate into your apps, whether you have a machine learning team of your own or you just want to use machine learning as a service.
2. CNNs are changing our relationship with technology
Convolutional neural networks (CNNs) has been an incredible breakthrough in the field of computer vision and natural language processing. There is an exponential growth in the number of solutions based on CNNs and this trend has been stable for the last couple of years.
Advances in machine learning, natural language processing, and computer vision make machines more human-like and change the way people interact with technology. Now we can talk to our gadgets, asking AI for advice, and getting what we need through it.
AI already plays a variety of roles within technology interfaces:
- Curator – curates content for people, for example, Netflix recommender system knows your movie preferences better than your best friend.
- Advisor – guides users towards optimal outcomes, for example, Google Maps now can show when there’s likely to be traffic to a specific destination, which makes planning a trip a bit easier.
- Orchestrator – collaborates with multiple channels on your behalf to achieve desired outcomes. For example, Alexa can control your smart home, provide you with news, use Spotify to play music for you, and much more.
3. Reasoning problem remains strong
We are increasingly entrusting our security, health, and safety to intelligent machines. But AI is still a black box, we get an intelligent answer from a computer, but we cannot explain why the computer made the decision.
In the enterprise, the ‘Why’ question is very important, and our inability to explain the reason behind a decision will keep enterprises from using complex machine-learning approaches like deep learning.
There is always a human responsible for every decision, not a machine, and whether it’s an investment decision or a medical decision, we don’t want to rely on a ‘black box’ method. Being able to explain things gives us the power to learn and improve over time.
As artificial intelligence is becoming people’s “go-to” technology, companies will be obliged to create a responsible AI, more understandable to their creators and accountable to their users. From the other side, humans must learn to intuitively trust the machines, despite their inscrutable decision-making. At the moment such leap of faith in people’s minds seems as far-fetched as explaining the decisions made inside the ‘black box’.
4. Narrow AI is where the action is today
Current AI applications can only do what they are trained to do. That doesn’t mean AI isn’t useful. Quite the opposite. AI is an indisputable winner when it is focused on solving a specific problem like ‘which road should I take home’ or ‘what is the right price for my new product’’.
The branch of AI focused on solving specific, well-defined problems is referred to as narrow AI. Speech recognition and natural language understanding we know from personal assistants like Siri and Cortana are examples of this technology subset.
Narrow AI is where the action is today, the technology is used to automate time-consuming tasks, make accurate predictions, perform complex scoring tasks, improve customer experience, etc. It does not attempt to perform the full range of human cognitive abilities but helps create real, measurable benefits for enterprises.
5. AI is outgrowing the phase of hype
2017 has been the year of AI. According to Gartner, Machine Learning and Deep Learning technologies have reached the ‘peak of inflated expectations’ this year. VC and corporate investment in AI-powered startups and projects is reaching a fever pitch as well. Most of the investment consists of internal R&D spendings by large tech companies like Amazon, Baidu, and Google.
As any other hot technology AI will pass the phase of experimentation, and the economic impact of AI applications will become the subject of discussion. The deals with AI startups won’t be just about technology and talent, VCs will focus on startup’s capability to deliver real-life benefits.
As highly targeted AI systems demonstrate maximum productivity, more industry-specific applications will appear. Team’s domain expertise will become essential to make AI products relevant and practical to their target markets. The experts in specific domains who understand what to look for in the data and what solution will fit within a regular workflow of a target customer will be most valued.
The data itself can be the true value. By purposefully collecting proprietary data that can not be easily replicated or acquired by a competitor, AI companies can create an unfair advantage. An elaborate data strategy is what distinguishes a winning AI-driven business from the competition. So if you still don’t understand the long-term value of the data that you are collecting it’s time to answer the question.
Although the future where AI will completely replace human workers seems scary, the AI solutions that offer a complete automation are the most attractive for investors at the moment. The companies that strategically aim their efforts at reaching the level of near total automation are doomed to succeed.
Food for thought
AI will continue to expand as a layer across every business process and will affect almost every industry. But to the moment AI adoption outside of the tech sector is still at an early stage. According to McKinsey survey of 3,000 AI-aware C-level executives, across 10 countries and 14 sectors, ‘only 20 percent said they currently use any AI-related technology at scale or in a core part of their businesses.’
High tech, telecom, and financial services are the leading early adopters of machine learning and AI. These industries are usually the first to invest in new technologies to gain competitive advantage and AI is not an exception. There are clear benefits from improved accuracy and speed in AI-powered fraud-detection systems in financial services and churn prediction solutions in telecom.
Companies with no plans of investing in AI either can’t find a business case for these technologies or don’t have required skills, processes, and infrastructure. There are still many obstacles to AI adoption, but the actions of so many players at the market should alert businesspeople to the power and importance of this new technology.
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