In this article, we discuss the future of artificial intelligence, including 10 predictions about 10 companies. The future of artificial intelligence (AI) is a topic of much speculation and debate. Some experts believe that AI has the potential to revolutionize many aspects of society and industry, while others are more cautious about its potential impact. The artificial intelligence sector is thriving across the globe. AI-enabled tools, like smarter chat-bots for customer service and robots for self-service at banks, are gaining traction in the mainstream.

TIFIN, the wealth AI platform known for its engaging experiences to enable better outcomes, has launched a new platform, TIFIN AMP, that addresses distribution frictions in the asset management industry. This new platform combines marketing, data science and sales enablement capabilities to drive more intelligent and modern distribution. The asset management industry is facing multiple challenges with the rapid proliferation of investment products, fee pressures and increasingly fragmented sales channels. As firms grapple with these challenges compounded with constrained sales capacity and limited integration between marketing, data, and sales efforts, TIFIN is introducing an interconnected AI-powered SaaS platform that integrates data from marketing and awareness activities, CRM information, and other data feeds, to generate actionable signals to fuel lead generation, qualification, nurturing and optimization in a firm's sales efforts. "Historically, distribution intelligence has been largely limited to data analytics and summary of purchased data feeds." said Dr. Vinay Nair, Founder & CEO of TIFIN.

Artificial intelligence is transforming the asset management industry by enabling fundamental analysts to research and extract more information faster so they can uncover accurate investment insights. Analysts spend hours and sometimes even days manually researching hundreds of sources. This process is extremely labor-intensive, and it's easy for analysts to miss critical pieces of information. Analysts can use AI and natural language processing (NLP) to detect and extract the most relevant facts from unstructured datasets. One of the ways AI has evolved is with its accessibility.

On Thursday, Udacity announced a new AI-based Nanodegree. Developed in partnership with WorldQuant, an international asset management firm, "Artificial Intelligence for Trading" will help learners bring machine learning to financial trading. Until recently, most banks have relied on historical data to map out future market trends. Computer modeling and machine learning algorithms, however, allow analysts to test millions of different scenarios to determine which will lead to the best outcomes. The course comprises of two three-month terms.

When you hear those terms, what comes to mind is a science fiction movie where machines achieve consciousness--and trouble ensues. At best, you might dismiss the concepts as hype or the buzz word of the day. It's true that the AI/machine learning landscape feels a bit like the Wild West right now, with opportunists promoting solutions when, in fact, they don't actually have a product--perhaps just some roughly defined services--but no technology to back it up. Regardless, AI and machine learning are very real--and very relevant to asset management firms. Unfortunately, traditional asset management and CRM (customer relationship management) systems like Satuit, Protrack, or Salesforce don't have the ability to deliver the benefits of AI and machine learning.

I Know First was selected from over 200 companies to be part of the fintech showroom in Tokyo, Japan on May 23rd, 2018. The showroom event, as well as a housewarming party, were hosted by JIAM (Japan International Asset Management Consortium, where I Know First presented their Algorithmic AI solutions using machine-learning algorithms. As part of the showroom event, I Know First gave a short pitch under 5 minutes followed by a deeper dive and demo if visiting client were interested. JIAM are a not-for-profit organization, which assists international asset management firms, asset management technology firms and talented financial professionals in tapping into the Japanese market. Through collaboration with Japanese government agencies (primarily the Tokyo Metropolitan Government) and industry associations, JIAM's goal is to bolster the Japanese asset management industry and support firms and individuals associated with it.

We are in the midst of a technological revolution. The increased use of artificial intelligence (AI), machine learning, blockchain, and big data technologies is rapidly changing the asset management industry. A recent survey on the future of the hedge fund industry by the Alternative Investment Management Association (AIMA) found that new "statistical and computational tools, including advanced quantitative techniques and artificial intelligence is forcing hedge fund firms to re-evaluate how they operate and invest." So how are manager selection and due diligence changing as a result of these new technologies? For insight on this question, we met with Michael Oliver Weinberg, CFA, chief investment officer of MOV37 and Protégé Partners, to discuss the evolution of the manager selection and due diligence processes. Protégé and MOV37 are specialized asset management firms that invest in smaller hedge funds and selected emerging managers. Founded in 2002, Protégé has been a key market player in supporting the growth of the hedge fund industry through seed deals and capital allocations to discretionary managers.

This article originally appeared in eFinancialCareers. While Silicon Valley has been investing in artificial intelligence over the last decade-plus, AI-driven asset management is just starting to get some love from investors. Over the next ten years, we will see this change as asset management turns increasingly to AI and technology – and the careers of buy-side professionals are going to change dramatically. Every year, the asset management industry takes in about $1 trillion in revenue. More than half is invested in human capital.

Everyone today talks about AI, big data and machine learning, yet most do not delve into the fundamental properties of how they will operate and how they might be an actual threat to asset managers. Some view technological methods as tools to assist them instead of being such a threat, and it would help provide both perspectives of the argument. Deep learning is a branch of machine learning that uses particular architectures of neural networks. These are artificial networks that attempt to actually replicate how the neural structures in human brains operate. Such methods have successfully been applied to areas such as computer vision – i.e. image processing and classification – as well as speech recognition. The techniques are readily available to any undergraduate student willing to learn the process.

Everyone today talks about AI, big data and machine learning, yet most do not delve into the fundamental properties of how they will operate and how they might be an actual threat to asset managers. Some view technological methods as tools to assist them instead of being such a threat, and it would help provide both perspectives of the argument. Deep learning is a branch of machine learning that uses particular architectures of neural networks. These are artificial networks that attempt to actually replicate how the neural structures in human brains operate. Such methods have successfully been applied to areas such as computer vision – i.e. image processing and classification – as well as speech recognition. The techniques are readily available to any undergraduate student willing to learn the process.