Recently, Medium.com released eight trends forecast for the 2018 AI world. Big data analysis is not a trend that has already ebb. As the amount of data continues to grow, big data analytics are constantly improving. When it comes to the application of predictive analytics, we only see the tip of the iceberg. By using current data mining, machine learning, and artificial intelligence techniques to analyze current data, it has successfully helped event organizations (ie, forecast sales, optimize marketing, etc.). All these different types of artificial intelligence are linked together, profoundly changing the way we work everyday, and more changes have yet to come.
Here are some key data from the AI ​​world about big data, predictive analytics, and machine learning:
By 2018, 75% of developers will introduce at least one AI function (from IDC) in business applications and services.
By 2019, 100% of IoT activities will be supported by AI functions (from IDC)
By 2020, 30% of companies will use AI to improve at least one key program (from Gartner).
By 2020, the algorithm will actively affect hundreds of millions of behaviors globally (from Gartner)
By 2020, the artificial intelligence market will exceed $40 billion (from ConstellaTIon Research)
By 2025, AI will drive 95% of user interaction. (from Servion)
Trend 1 - Big company's first advantage, it is bound to winAmazon, Google, Facebook and IBM will lead the development of artificial intelligence. As big companies, they have the right resources to collect data, so more data is available.
Here are the top players' developments in artificial intelligence:
Amazon:
Investing in artificial intelligence for more than 20 years
Web crawls data from over 5 billion web pages
The logistics center has more than 500000 JPEG images and corresponding JSON metadata files
Daily monitoring of global broadcasts, prints, and online news records exceeds 250 million
Nearly 100 million images and videos with video, audio and annotations
Amazon's Echo leads the voice-activated assistant market by more than 70%
Google:
One of the largest dataset libraries with 10-15 Exabytes of data - Cirrus Insight
Focus on application and product development, not long-term artificial intelligence research
A team of 1,300 researchers - Google Brain
23.8% of the voice assistant market - Voicebot user share
Using the open source platform for machine learning, TensorFlow, gives everyone access to the machine learning platform
The size of the Google Earth database is estimated to be 3,017 TB or approximately 3 pb - Google Earth Blog
Google Street View has about 20 pb of street photos - Peta Pixels
Process 2.5 billion content and more than 500 terabytes of data per day - Tech Crunch
The Facebook Artificial Intelligence Research Center has about 80 researchers and engineers - FAIR
Daily average of 2 billion "likes" and 300 million photos - Tech Crunch
Scanning 105TB of data every 30 minutes - Tech Crunch
Built a 62,000 square foot data center that can hold up to 500 racks
Translate 2 billion user posts in over 40 languages ​​every day, and 800 million users can see translations - Fortune
IBM:
Plan to spend 10 years, investing $240 million to invest in the creation of the Massachusetts Institute of Technology - IBM "Watson" artificial intelligence laboratory - IBM
Watson's customer business spans 6 continents and exceeds 25 countries - IBM
IBM invested $1 billion in the Watson Group, including $100 million in venture capital to support IBM startups and companies that build Watson-IBM cognitive applications - IBM
More than 7,000 applications have been built through the Watson ecosystem - Fortune
Google is most likely to be at the forefront of deploying machine learning to products and services. They are not only the first company to start artificial intelligence research, but Google is a fairly large company with more than 70,000 employees. In addition, Google Brain is a deep learning artificial intelligence research project, Google has a complete team, its research agenda includes machine learning, natural language understanding, machine learning algorithms and techniques, and robotics.
All of the second echelon companies investing in artificial intelligence, such as Intel, Salesforce, and Twitter, will follow big companies that already have data and start using their data, algorithms, and artificial intelligence. Data transactions will occur between industry participants, and algorithms and technologies will be consolidated. The trading of data and the integration of algorithms and techniques will make artificial intelligence more efficient.
As giants such as Google and Facebook acquire smaller players, the algorithms will be integrated into their core platforms/solutions. DeepMind, a London-based artificial intelligence company, built a universal learning algorithm, and Google acquired the company to gain a competitive edge with other technology companies. On the other hand, Facebook acquired Wit.AI to help with speech recognition and voice interfaces. It also acquired artificial intelligence startup Ozlo to upgrade its virtual assistants.
Trend 3 - crowdsourcing data will be hugeAll artificial intelligence companies will pursue huge data sets to find ways and means to achieve their ambitions for artificial intelligence. These companies will begin crowdsourcing large amounts of data. They have found different ways to assess the quality and authenticity of crowdsourced data, which not only gives companies the ability to benefit from this data, but also gives consumers a chance to speak.
Joel Gurin, founder and editor of OpenDataNow.com, said: "We live in a crowdsourcing culture, and more and more people are willing and interested in sharing what they know through social media."
Google uses crowdsourcing to get a lot of images to build their imaging algorithms. In addition, the company uses crowdsourcing technology to help improve translation, transcription, handwriting recognition and map services through its crowdsourced applications. Amazon also uses crowdsourcing artificial intelligence to improve Alexa's current 15,000 skills.
Trend 4 - M&A will be more and moreAccording to CBInsights, the acquisition of artificial intelligence companies will begin in 2018, when we will see more and more mergers and acquisitions as companies compete for intellectual capital and talent. All small companies in the field of machine learning/artificial intelligence will be acquired by large companies. There are two main reasons:
Without a data set, artificial intelligence cannot work independently. Because large companies have large data sets, they have a huge advantage for small companies.
Algorithms without data are useless. vice versa. Data is at the heart of the algorithm, and getting a lot of data is critical.
As a robotic engineer and director of the Creative Machines Lab at Columbia University, Hod Lipson said: "Data is fuel and algorithms are engines."
Trend 5-AI tools move toward democratization to increase market shareLarge companies will begin to open up their algorithms and other toolsets to gain market share. The barriers to market access to data and algorithms will decrease, and new applications for artificial intelligence will increase. Through democratization, small companies that do not have access to artificial intelligence tools will have a large amount of data to train and launch complex artificial intelligence algorithms.
As Sundar Pichai, CEO of Google, said, "The most exciting thing we can do is to learn about machine learning and artificial intelligence." The disenchantment." This is very important for everyone.
In addition, frameworks, SDKs, and apis will become the standard for all major players to open up. The underlying model of SaaS and PaaS will be the business model pursued by all of these companies.
Trend 6 - Human-computer interaction will improveSiri and Alexa are the two most popular human-computer interaction tools. More robot-based solutions like this will be the first entry for AI. For example, although the machine has been programmed for speech analysis and facial recognition, it will also be able to recognize your emotions based on your voice pitch, which is emotional analysis.
Manufacturing automation and non-consumer solutions will be improved first. Manufacturing automation will primarily use advanced technology, including automation, robotics and advanced manufacturing techniques to save labor costs. In the agricultural and pharmaceutical sectors, improvements in non-consumer solutions such as human-computer interaction will also be popularized in 2018.
Trend 7 - Artificial Intelligence will affect more vertical areasManufacturing, customer service, finance, healthcare, and transportation have been affected by artificial intelligence. Autopilot cars are expected to be available in 2018. Next year, artificial intelligence will affect more vertical areas. Here's a brief example of industry and how artificial intelligence will affect them:
Insurance - Artificial Intelligence will improve the claims process through automation
L Legal - NLP can sum up thousands of pages of legal documents in minutes, reducing time and increasing efficiency
PR & Media - AI will help process data quickly
Education - the development of virtual tutors artificial intelligence-assisted paper scoring; adaptive learning programs, games and software; and personalized education programs provided by artificial intelligence will change the way students and teachers interact
Health - Machine learning can be used to make more complex and accurate methods to predict a patient's pre-symptomatic condition
Just as the industrial revolution of 100 years ago changed almost everything, artificial intelligence will change the world in the next few years.
Everything under the artificial intelligence umbrella, such as machine learning and big data, is vulnerable to emerging security and privacy issues. Sometimes critical infrastructure plays an important role in it. Security requirements related to privacy issues, such as keeping bank accounts and health information confidential, add to the need for security research. 2018 will be a year of attention to security and privacy issues, and there may be new developments.
The ethics of artificial intelligence will also be a major issue in 2018. Ethical and ethical issues need to be addressed, including how artificial intelligence can harm or benefit humanity. There are also concerns about the possibility of robots replacing humans, especially if artificial intelligence is to be used in areas where human empathy works, such as nurses, therapists, or police. Another problem that will be dealt with is the autonomous weapon. Given the level of autonomic function, artificial intelligence will need to cover certain functions because he is not a weapon in the hands of humans.
Conclusion: Although artificial intelligence has been around for many years, the artificial intelligence we know today is still in its infancy. There is a lot of hype around artificial intelligence and its various applications, from autonomous vehicles to virtual personal assistants, and many other tasks that require artificial intelligence. Although there are a long list of artificial intelligence use cases, most of them are designed to improve specific processes, and it takes time to successfully deploy them. Artificial intelligence still has a long way to go. 2018 will obviously be a crucial year for the development of artificial intelligence, let us wait and see.
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