recentpopularlog in

jerryking : ibm_watson   8

Data Challenges Are Halting AI Projects, IBM Executive Says
May 28, 2019 | WSJ | By Jared Council.

About 80% of the work with an AI project is collecting and preparing data. Some companies aren’t prepared for the cost and work associated with that going in,......“And so you run out of patience along the way, because you spend your first year just collecting and cleansing the data,”.....“And you say: ‘Hey, wait a moment, where’s the AI? I’m not getting the benefit.’ And you kind of bail on it.”....A report this month by Forrester Research Inc. found that data quality is among the biggest AI project challenges. Forrester analyst Michele Goetz said companies pursuing such projects generally lack an expert understanding of what data is needed for machine-learning models and struggle with preparing data in a way that’s beneficial to those systems.

She said producing high-quality data involves more than just reformatting or correcting errors: Data needs to be labeled to be able to provide an explanation when questions are raised about the decisions machines make.

While AI failures aren’t much talked about, Ms. Goetz said companies should be prepared for them and use them as teachable moments. “Rather than looking at it as a failure, be mindful about, ‘What did you learn from this?’”
artificial_intelligence  data_collection  data_quality  data_wrangling  IBM  IBM_Watson  teachable_moments 
may 2019 by jerryking
Everything still to play for with AI in its infancy
February 14, 2019 | Financial Times | by Richard Waters.

the future of AI in business up for grabs--this is a clearly a time for big bets.

Ginni Rometty,IBM CEO, describes Big Blue’s customers applications of powerful new tools, such as AI: “Random acts of digital”. They are taking a hit-and-miss approach to projects to extract business value out of their data. Customers tend to start with an isolated data set or use case — like streamlining interactions with a particular group of customers. They are not tied into a company’s deeper systems, data or workflow, limiting their impact. Andrew Moore, the new head of AI for Google’s cloud business, has a different way of describing it: “Artisanal AI”. It takes a lot of work to build AI systems that work well in particular situations. Expertise and experience to prepare a data set and “tune” the systems is vital, making the availability of specialised human brain power a key limiting factor.

The state of the art in how businesses are using artificial intelligence is just that: an art. The tools and techniques needed to build robust “production” systems for the new AI economy are still in development. To have a real effect at scale, a deeper level of standardisation and automation is needed. AI technology is at a rudimentary stage. Coming from completely different ends of the enterprise technology spectrum, the trajectories of Google and IBM highlight what is at stake — and the extent to which this field is still wide open.

Google comes from a world of “if you build it, they will come”. The rise of software as a service have brought a similar approach to business technology. However, beyond this “consumerisation” of IT, which has put easy-to-use tools into more workers’ hands, overhauling a company’s internal systems and processes takes a lot of heavy lifting. True enterprise software companies start from a different position. They try to develop a deep understanding of their customers’ problems and needs, then adapt their technology to make it useful.

IBM, by contrast, already knows a lot about its customers’ businesses, and has a huge services operation to handle complex IT implementations. It has also been working on this for a while. Its most notable attempt to push AI into the business mainstream is IBM Watson. Watson, however, turned out to be a great demonstration of a set of AI capabilities, rather than a coherent strategy for making AI usable.

IBM has been working hard recently to make up for lost time. Its latest adaptation of the technology, announced this week, is Watson Anywhere — a way to run its AI on the computing clouds of different companies such as Amazon, Microsoft and Google, meaning customers can apply it to their data wherever they are stored. 
IBM’s campaign to make itself more relevant to its customers in the cloud-first world that is emerging. Rather than compete head-on with the new super-clouds, IBM is hoping to become the digital Switzerland. 

This is a message that should resonate deeply. Big users of IT have always been wary of being locked into buying from dominant suppliers. Also, for many companies, Amazon and Google have come to look like potential competitors as they push out from the worlds of online shopping and advertising.....IBM faces searching questions about its ability to execute — as the hit-and-miss implementation of Watson demonstrates. Operating seamlessly in the new world of multi-clouds presents a deep engineering challenge.
artificial_intelligence  artisan_hobbies_&_crafts  automation  big_bets  brainpower  cloud_computing  contra-Amazon  cultural_change  data  digital_strategies  early-stage  economies_of_scale  Google  hit-and-miss  IBM  IBM_Watson  internal_systems  randomness  Richard_Waters  SaaS  standardization  value_extraction 
february 2019 by jerryking
Artificial intelligence and jobs: What’s left for humanity will require uniquely human skills - The Globe and Mail
July 27, 2018 |CONTRIBUTED TO THE GLOBE AND MAIL by STEVE WOODS.

Where should we look for this final archipelago of human employment? The best place to start is deep within ourselves. As much as we pride ourselves on advanced skills such as mathematics and chess, humans are not born innately aware of algebra or checkmate. We are, instead, a social species. We are born innately aware of others, their reactions to us and our relationships with them. Removing a person from a social environment is so harmful that it is deemed to be a form of torture and is banned by the Geneva Convention.

When we attempt to use machines to replace the role of humans in our social lives, the response is immediate and negative......we, as a society and as a species, don’t want AI to replace our social interactions and our relationships. It’s a part of what makes us human and it’s a part that we intend to keep.....areas where we don’t desire AI replacement: relationships, trust, guidance, caring, nurturing and social interaction are traits that these post-AI jobs will share.
artificial_intelligence  automation  relationships  emotions  emotional_intelligence  empathy  EQ  humanity  creative_destruction  Joseph_Schumpeter  character_traits  AlphaGo  IBM_Watson 
july 2018 by jerryking
A.I. Is Doing Legal Work. But It Won’t Replace Lawyers, Yet. - The New York Times
By STEVE LOHR MARCH 19, 2017

An artificial intelligence technique called natural language processing has proved useful in scanning and predicting what documents will be relevant to a case, for example. Yet other lawyers’ tasks, like advising clients, writing legal briefs, negotiating and appearing in court, seem beyond the reach of computerization, for a while......Highly paid lawyers will spend their time on work on the upper rungs of the legal task ladder. Other legal services will be performed by nonlawyers — the legal equivalent of nurse practitioners — or by technology.

Corporate clients often are no longer willing to pay high hourly rates to law firms for junior lawyers to do routine work. Those tasks are already being automated and outsourced, both by the firms themselves and by outside suppliers like Axiom, Thomson Reuters, Elevate and the Big Four accounting firms.....So major law firms, sensing the long-term risk, are undertaking initiatives to understand the emerging technology and adapt and exploit it.

Dentons, a global law firm with more than 7,000 lawyers, established an innovation and venture arm, Nextlaw Labs, in 2015. Besides monitoring the latest technology, the unit has invested in seven legal technology start-ups.

“Our industry is being disrupted, and we should do some of that ourselves, not just be a victim of it,” John Fernandez, chief innovation officer of Dentons, said.....Artificial intelligence has stirred great interest, but law firms today are using it mainly in “search-and-find type tasks” in electronic discovery, due diligence and contract review,
artificial_intelligence  automation  contracts  corporate_investors  Dentons  e-discovery  IBM_Watson  law  lawtech  lawyers  legal  NLP  start_ups  Steve_Lohr  technology 
march 2017 by jerryking
Your Lawyer May Soon Ask This AI-Powered App for Legal Help | WIRED
DAVEY ALBA BUSINESS DATE OF PUBLICATION: 08.07.15.
08.07.15

ROSS Intelligence is a voice recognition app powered by IBM Watson, the machine learning service based on the company’s Jeopardy-playing cognitive system, that doles out legal assistance.

The app is yet another example of the ways machine learning is infiltrating our everyday lives. These days, it’s not just AI algorithms themselves that have improved, but the ability to deliver them across the Internet that has made so many new applications possible.....Asking Natural Questions
Ross works much like Siri. Users can ask it any question the same way a client might—for instance, “If an employee has not been meeting sales targets and has not been able to complete the essentials of their employment, can they be terminated without notice?” The system sifts through its database of legal documents and spits out an answer paired with a confidence rating. Below the answer, a user can see the source documents from which Ross has pulled the information; if the response is accurate, you can hit a “thumbs up” button to save the source. Select “thumbs down” and Ross come up with another response.
technology  law  lawtech  lawyers  law_firms  machine_learning  voice_recognition  voice_interfaces  virtual_assistants  artificial_intelligence  Siri  IBM_Watson 
june 2016 by jerryking
Kensho, a startup doing Siri (or Watson) for financial markets, has raised $10M — Tech News and Analysis
By Derrick Harris
Jan. 22, 2014

It looks like a smart product from a smart team, especially if the UI and visualizations are as good as the algorithms.... Warren (as in Warren Buffett, I presume), is a natural-language search engine for data on financial markets. You (assuming you’re a banker or very sophisticated day trader) type in a question — an example from the company’s website is “Which aerospace companies rally following major breakthroughs in drone technology?” — and it returns results in the form of data.
start_ups  open_data  value_chains  fin-tech  finance  Kensho  search  search_engines  financial_services  Siri  IBM_Watson 
january 2014 by jerryking
Identify new growth niche and how you can profit
March 19, 2013 | Financial Post | By Rick Spence.

Sparks: What other companies need unlikely solutions? How could you help them with data management, management of perishables, or guaranteeing consistent quality?
Sparks: What niche information markets could you develop and own? Or, what services could you offer to celebrity startups that have everything except business experience?
Spark: Retailers are eager to lock up new brands to differentiate themselves. How can you help more marketers achieve a competitive advantage?
Spark: What other marginal products and businesses will tech giants such as Google and Facebook drop next? How can you help users adjust? Or, what under-performers should you be trimming from your own product roster?
Sparks: Designers and builders should target early adopters eager for a colour makeover.
Spark: Where else can you find a business whose margins are so huge that Buy-One, Get-Three-Free makes sense? Or, when big names are offering value propositions like this, how can you retool your promotions and sales to compete?
Spark: How could you solve major problems like these without a supercomputer?
Spark: Gadgetry is changing so fast that even markets you thought had stabilized are wide open to new ideas. How can you use hot new technology to disrupt your industry?
Rick_Spence  growth  niches  entrepreneur  kill_rates  IBM_Watson  massive_data_sets  celebrities  ideas  entrepreneurship  new_businesses  solutions  disruption  under-performing  early_adopters  competitive_advantage  perishables  information_markets  adjustments  data_management  culling  differentiation  retailers  brands 
march 2013 by jerryking

Copy this bookmark:





to read