Artificial Intelligence (AI) and associated technologies will be present across many industries, within a considerable number of software packages, and part of our daily lives by 2020. Gartner has also predicted that by 2020, AI will become one of the top five investment priorities for at least 30 percent of Chief Information Officers. Global software vendors are after this new gold rush. Unfortunately, though the promise of new revenue has pushed software business owners to invest in AI technologies, the truth is that most organizations do not have skilled staff to embrace AI.
An implicit note of warning in many industry surveys on AI and its impact on industries is that software vendors should first focus on understanding the business-customer needs and potential business benefits from AI, before chasing the gold rush, which has been termed as “AI Washing,” as suggested in How Enterprise Software Providers Should (and Should Not) Exploit the AI Disruption.
The trust deficit in the “capabilities of tech-enabled solutions” that exists today will vanish in the next 10 years, states In Ten Years: The Future of AI and ML. Over the next decade, we will witness a radical shift from partial mistrust and skepticism to complete dependence on AI and other advanced technologies. Most AI-powered applications are consumer facing, which is another solid reason for mainstream users to overcome the trust barrier over time. With more exposure and more access to technological solutions for their daily business, the Citizen Data Science community will pave the way for a new-technology-order world.
Leveraging AI and Machine Learning as Competitive Business Drivers claims that while technologies like the Cloud brings agility to business processes, AI and Machine Learning have the power to influence business outcomes.
According to Gartner:
“Artificial Intelligence and Machine Learning have reached a critical tipping point and will increasingly augment and extend virtually every technology enabled service, thing, or application.”
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The Future of AI
In the post-industrialization era, people have worked to create a machine that behaves like a human. The thinking machine is AI’s biggest gift to humankind; the grand entry of this self-propelled machine has suddenly changed the operative rules of business. In the recent years, self-driving vehicles, digital assistants, robotic factory staff, and smart cities have proven that intelligent machines are possible. AI has transformed most industry sectors like retail, manufacturing, finance, healthcare, and media and continues to invade new territories.
The Future of Machine Learning
Here are some predictions about Machine Learning, based on current technology trends and ML’s systematic progression toward maturity:
- ML will be an integral part of all AI systems, large or small.
- As ML assumes increased importance in business applications, there is a strong possibility of this technology being offered as a Cloud-based service known as Machine Learning-as-a-Service (MLaaS).
- Connected AI systems will enable ML algorithms to “continuously learn,” based on newly emerging information on the internet.
- There will be a big rush among hardware vendors to enhance CPU power to accommodate ML data processing. More accurately, hardware vendors will be pushed to redesign their machines to do justice to the powers of ML.
- Machine Learning will help machines to make better sense of context and meaning of data.
The blog post, 5 Predictions for the Future of Machine Learning from IBM Big Data Hub, offers descriptions of the above trends.
Some Predictions about Machine Learning
A seasoned user of ML techniques shares his insights into the world of ML, suggesting these trends are imminent in the field of ML:
- Use of Multiple Technologies in ML: The emergence of IoT has benefitted Machine Learning in many ways. The use of multiple technological strategies to achieve better learning is currently is practice in ML; in the future more “collaborative learning” by utilizing multiple technologies is probable.
- Personalized Computing Environment: Developers will have access to API kits to design and deliver “more intelligent application.” In a way, this effort is akin to “assisted programming.” Through these API kits, developers will easily embed facial, speech, or vision-recognition features into their systems.
- Quantum Computing will greatly enhance the speed of execution of ML algorithms in high-dimensional vector processing. This will be the next conquest in the field of ML research.
- Future advancement in “unsupervised ML algorithms” will lead to higher business outcomes.
- Tuned Recommendation Engines: ML-enabled services of the future will become more accurate and relevant. For example, the Recommendation Engines of the future will be far more relevant and closer to an individual user’s personal preferences and tastes.
Machine Learning and Artificial Intelligence Trends in 2018 provides a quick roundup of the most salient technology trends for 2018. Gartner’s Top 10 Technology Trends of 2017 sums up the all-pervading digital fever as the existence of people, machines, and business processes in a unified system.
Will Advanced AI and ML Affect Cyber security?
Going by the current research trends in AI and ML, the advancements in cyber-security has taken ML algorithms to the next level of learning, which suggests the security-centric AI and ML applications of the future will be marked for their speed and accuracy. The full story is available in Machine Learning, Artificial Intelligence & the Future of Cyber Security. This growing trend may bring Data Scientists and cyber security experts closer to achieving common software-development goals.
Benefiting Humanity: AI and ML in Core Industry Sectors
It is hard to ignore the global impact of “AI Washing” in the current business market, and how AI and ML may change the application-development markets of tomorrow.
AI and ML have jointly been given the same importance as the discovery of electricity at the beginning of Industrial Revolution. These frontier technologies, just like electricity, have ushered in a new era in the history of Information Technology.
Today, AI- and ML-powered systems are drastically changing the way business is done across all industry sectors. These frontier technologies are gradually bringing about transformative changes across industry sectors, a few of which are listed here:
Gradually, human practitioners and machines will work in tandem to deliver improved outcomes. Advanced machines will be expected to deliver accurate and timely diagnosis of patient conditions, while the practitioners can focus more on patients.
AI And Machine Learning are the New Future Technology Trends discusses how the latest technologies like blockchain are impacting India’s capital markets. For instance, capital-market operators can use blockchain to predict movements in the market and to detect fraud. AI technologies not only provide opportunities for newer business models in the financial market, but also solidify the AI technologist’s position in the business-investment ecosystem.
In Real Estate
Contactually.com, an advanced CRM system for the real estate business, has been specifically designed to connect Washington DC-based investors and startups. The additional power of Machine Learning algorithms transforms the static system into a live, interactive machine, which responds, approves, and recommends.
In Database Administration
The repetitive tasks in an average DBA system provide opportunity for AI technologies to automate processes and tasks. Today’s DBA is empowered with advanced tools, so that they can make value-added contributions to their organizations rather than just performing rote functions, as explored in What Do AI and Machine Learning Mean for DBAs.
In the Personal Device Market
Some business analysts at claim that AI is a game changer for the personal device market. By 2020, about 60 percent of personal-device technology vendors will depend on AI-enabled Cloud platforms to deliver enhanced functionality and personalized services. AI technology will deliver an “emotional user experience.”
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